Suyog Bidkar
Affilation:	Portfolio Manager, Infosys Limited for CVS Health Hartford, CT, United States
Email-Id:	Suyogbidkar82@gmail.com
ACADEMIC QUALIFICATION Advanced Program Management From Cornell University – USA, 2023. Post Graduate Diploma In Advanced Computing From C-DAC– Pune, India, 2006. Bachelor Of Engineering From Govt. College Of Engineering –Pune University, India, 2006.
Project: Healthcare Interoperability Using FHIR, Infosys Limited for CVS Health – Hartford, CT. Next Generation Clinical Platform, Agile Release Train Engineer (RTE) Infosys Limited for CVS Health – Hartford, CT . Multimillion programs, Program Manger Infosys Limited for multiple clients in USA, UK & INDIA

 

Published by: vikaspatanker

IoT Enabled Solutions for Women Safety and Health Monitring
Authors:-Sudeshna P, Vivekanandan K

Abstract-Women and children today deal with a number of problems, including sexual attacks. The victims’ life will undoubtedly be greatly impacted by such atrocities. It also has an impact on their psychological equilibrium and general wellbeing. The frequency of these acts of violence keeps rising daily. Even schoolchildren are victims of sexual abuse and abduction. In our society, a nine-month-old girl child is not protected; she was abducted, sexually assaulted, and ultimately killed. Seeing the abuses of women makes us want to take action to ensure the protection of women and children. Therefore, we intend to present a device in this project that will serve as a tool for security and guarantee the safety of women and children. GSM microcontroller.

DOI: 10.61137/ijsret.vol.10.issue5.224

A 19-Level Variable Frequency Switched DC-AC Converter fed Induction Motor Drive for Bench Grinding Applications
Authors:-MTech Scholar Umang Soni, Assistant Professor Shyam Kumar Barode, Assistant Professor Hari Mohan Soni, Assistant Professor Sachin Jain

Abstract-The development of inverters with more than two layers to reduce distortion from the fundamental sinusoidal waveform gave rise to the concept of a multilayer inverter. For bench grinder applications, the induction motor drive has to be powered by AC. Therefore, a multilayer inverter is used to boost the sine wave nature of the inverter output, and an asymmetrical H-bridge type inverter is used to decrease the bulkiness and cost of the system. The MATLAB platform is used to construct the concept, and analysis is then conducted to ascertain the end product’s value.

DOI: 10.61137/ijsret.vol.11.issue1.101

Review on PAPR Reduction and Improvement of OFDM System Performance Using Artificial Intelligence
Machine Learning Algorithm
Authors:-M.Tech Scholars Rahul Mishra, Assistant Professor Vijay Bisen

Abstract-The advancement of technology necessitates the development of more sophisticated modulation strategies for wideband digital communication systems. The requirements for high-speed data transmissions can be effectively met by utilizing orthogonal frequency division multiplexing, which is an effective technique. However, a high peak-to-average power ratio (PAPR) is one of the key limits that OFDM systems face, both in terms of their performance and their power efficiency. The evaluation of the PAPR reduction has become a topic of widespread interest in this present decade due to the relevance it holds in the industrial and scientific communities. The purpose of this study is to Review show the combination of the bat algorithm with the partial transmit sequence scheme as an effective way for reducing PAPR that also eases the burden of computing work. For the purpose of providing a comparative evaluation of the PAPR reduction performance, a number of simulations using various partial transmit sequence schemes have been carried out.

DOI: 10.61137/ijsret.vol.11.issue1.102

A Review on Nano Fluid Particles through a Rectangular Corrugated Channel
Authors:-Mayank Dwivedi, Dr. Sanjay Kumar Singh

Abstract-This review examines the thermal and hydraulic performance of nanofluids flowing through rectangular corrugated channels, focusing on their potential for enhancing heat transfer efficiency. Various nanofluids, including ZnO, CuO, Fe₂O₃, Al₂O₃, SiO₂, and TiO₂, are evaluated based on parameters such as heat transfer coefficient, pressure drop, and Nusselt number. The unique properties of nanofluids, coupled with the enhanced turbulence induced by corrugated geometries, result in significant improvements in thermal performance compared to conventional fluids. However, factors like pressure drop and flow resistance also vary widely depending on the type of nanoparticles used. This review highlights the critical role of nanoparticle selection and channel design in optimizing heat transfer while minimizing pressure losses, providing valuable insights for advanced thermal management systems.

An Algorithmic Implemetation on Big Data Approach Using Mapping Techniques
Authors:-Research Scholar Ms. Shilpa Sharma, Professor R. K. Bathla

Abstract-Research is an art of scientific examination. The advance learner’s vocabulary of current English lays down the meaning of research as “A careful exploration and enquiry especially through search for new facts in any branch of knowledge. Bradman and Morry define research as “A standardize efforts to increase new knowledge”. Research is, thus an original contribution to existing stock of knowledge making for its advancement. It is detection of truth with the help of study, observation, comparison, and experiments. The technologies that give support to the entire process of cost-effectively storing and processing data, and utilize internet technologies in a scattered way have arisen in the past few years. NoSQL and Cloud computing are the renowned ones that improve the potential offered by Big Data Technologies. Map Reduce is a software manufacture introduced by Google to act upon parallel processing on large datasets supercilious that large dataset storage is distributed over a large number of machines. Each machine computes data stored locally, which in turn contributes to distribute and parallel processing. This paper focuses on the Big data and Cloud services using impact of Map Reduce Algorithm and very advantageous for the researchers and corporate sectors who are using Map Reducing System technology.

DOI: 10.61137/ijsret.vol.11.issue1.103

The Impact of Digital Transformation on Warehouse Efficiency
Authors:-Tariq Ibrahim Al Barwani, Dr.Masengu Reason

Abstract-Digital transformation has emerged as a pivotal force reshaping the logistics and supply chain sectors, particularly in warehouse operations. This study explores the multifaceted impact of digital technologies on warehouse efficiency, highlighting key innovations such as automation, data analytics, and the Internet of Things (IoT). By integrating these technologies, warehouses can enhance operational performance, reduce costs, and improve inventory management. The research identifies how automation tools, such as robotics and automated guided vehicles (AGVs), streamline processes, reduce labour costs, and minimize human error. Furthermore, advanced data analytics enable real-time decision-making and predictive analytics, allowing for optimized inventory levels and enhanced demand forecasting. The IoT facilitates seamless communication between devices, improving visibility and traceability throughout the supply chain. Through case studies and empirical data, this paper demonstrates that warehouses adopting digital transformation strategies experience significant improvements in productivity, accuracy, and customer satisfaction. However, it also addresses the challenges faced during implementation, including workforce adaptation and cybersecurity concerns. Ultimately, this study emphasizes that embracing digital transformation is not merely a trend but necessary for warehouses aiming to thrive in an increasingly competitive market. The findings underscore the importance of strategic planning and investment in technology to achieve sustainable efficiency gains.

The Impact of Digital Transformation on Warehouse Efficiency
Authors:-Tariq Ibrahim Al Barwani, Dr.Masengu Reason

Abstract-Digital transformation has emerged as a pivotal force reshaping the logistics and supply chain sectors, particularly in warehouse operations. This study explores the multifaceted impact of digital technologies on warehouse efficiency, highlighting key innovations such as automation, data analytics, and the Internet of Things (IoT). By integrating these technologies, warehouses can enhance operational performance, reduce costs, and improve inventory management. The research identifies how automation tools, such as robotics and automated guided vehicles (AGVs), streamline processes, reduce labour costs, and minimize human error. Furthermore, advanced data analytics enable real-time decision-making and predictive analytics, allowing for optimized inventory levels and enhanced demand forecasting. The IoT facilitates seamless communication between devices, improving visibility and traceability throughout the supply chain. Through case studies and empirical data, this paper demonstrates that warehouses adopting digital transformation strategies experience significant improvements in productivity, accuracy, and customer satisfaction. However, it also addresses the challenges faced during implementation, including workforce adaptation and cybersecurity concerns. Ultimately, this study emphasizes that embracing digital transformation is not merely a trend but necessary for warehouses aiming to thrive in an increasingly competitive market. The findings underscore the importance of strategic planning and investment in technology to achieve sustainable efficiency gains.

Optimizing Deep Learning Models For Edge Devices: A Framework for Efficient Ai Deployment
Authors:-Preethi V, Associate Professor Dr S R Raja

Abstract-The proliferation of edge devices such as smartphones, IoT sensors, and embedded systems has driven the demand for deploying artificial intelligence (AI) models directly on these devices. However, the limited computational and energy resources of edge devices present significant challenges for deep learning (DL) models, which are typically resource-intensive. This paper proposes a novel framework for optimizing deep learning models for edge devices, focusing on techniques such as model compression, quantization, and knowledge distillation. By applying these techniques, the proposed framework ensures minimal loss of accuracy while significantly reducing model size and inference time. The effectiveness of the framework is demonstrated through experiments on image recognition and natural language processing tasks. The results highlight the potential for scalable AI solutions on edge devices without compromising user experience.

Automatic Pathole Detection System
Authors:-K .Kirti, W.Yash, C.Nihal, W. Nikhil, Professor Vairalkar, Professor T.Vivekanand

Abstract-Automatic Pothole Detection While Driving the main theme of the design is Smart Vehicles Electric vehicle/ Electric vehicle motor and battery technology. The arising need to help road accidents has ultimately came important aspect of moment’s developing world, the graph has taken a high rise in once 5 times. And numerous families being victims of this situation have suffered a lot. 60 of road accidents are passed due to uneven roads and interferers in the line. we came up with a an idea to descry potholes and humps in an automatic manner. and the person driving will be conceded about the pothole. An automatic pothole sensor using ultrasonic detector, which detects the potholes with the help of ultrasonic detector including longitude, latitude and depth of pothole and road humps. After seeing it sends the signal to GPS receiver through Arduino WiFi module which also displays the details in the TV and a android operation. This design can be used in the transport department as the importing and exporting is substantially done in night times and this sensor helps to warn the motorists. As this product has low manufacturing bring the price might not differ important and is surely affordable for a common man, due to its range of price the deals will rise to peaks and the manufacturer also has reasonable profit.

DOI: 10.61137/ijsret.vol.11.issue1.104

Review on Improvement of Shunt Active Filter Performance Using Artificial Intelligence Methods
Authors:-Manish Tomar, Raghunandan Singh Baghel

Abstract-In this review study, we looked at a variety of power filter approaches for high-power applications that frequently involve complicated digital control circuits and expensive batteries. An analog-based hysteresis current controller and capacitive energy storage are used to create a simple and low-cost active power filter circuit in this study. The filter is designed to be a low-power add-on item that reduces AC harmonic currents generated by existing electronic equipment (such as personal computers), which cause nonlinear loads on the AC mains. The suggested filter is addressed in terms of its operating concept, design requirements, and control method.

Kissan Buddy-An Android Application for Estimating The Nearest Mandi and Transaction Costs for Farmers
Authors:-KV Achyuth Reddy, Lochan S, Associate Professor Dr. M Swapna, Shrusthi, Ediga Purushotham Goud

Abstract-Kissan Buddy is an Android application developed to assist farmers in accessing real-time information about nearby mandis (agricultural markets) where they can sell their produce at optimal prices. The app utilizes Google Maps for accurate location services and Firebase for backend support, enabling farmers to input essential details such as their location, types of crops, and expected production costs. Based on this input, the application identifies the nearest mandis and estimates the transaction costs involved in selling the produce. This empowers The application uses advanced technologies such as Firebase, Google Maps, and cloud computing to offer farmers an intuitive platform where they can track their location, manage crops, and estimate nearby mandis (markets) where they can sell their produce, ensuring better price transparency and reducing reliance on middlemen. Key features of the app include location-based mandi search, real-time price estimation, and detailed transaction cost analysis. By improving market access, optimizing pricing transparency, and minimizing costs, Kissan Buddy aims to enhance profit margins for farmers and contribute to a more efficient and sustainable agricultural economy.

DOI: 10.61137/ijsret.vol.11.issue1.105

Teaching Identification of Fractions in Context Using the Three-tier Teaching Model’s Pedagogy
Authors:-Daniel Gbormittah, Christopher Yarkwah

Abstract-This paper aims to expand our understanding of culturally relevant pedagogy by utilizing the three-tier model for teaching mathematics in a context. The three-tier model is culturally relevant pedagogy (CRP). It is an innovative teaching approach that draws on learners’ sociocultural contexts to scaffold mathematics learning. The study investigated the effects of a culturally relevant pedagogy through the use of the three-tier model on pupils’ performance in fractions in Mfantsiman Municipality (MM). The study drew on ethnomathematics and the three-tier model as its main conceptual perspective. We administered a performance test to 426 participants in 12 primary schools in the MM. We analysed the quantitative data using frequency counts, mean, standard deviation, independent samples t-test, and paired samples t-test. We employed content analysis and narrative discussion to scrutinize the qualitative data. The results demonstrated that the culturally relevant pedagogy, specifically the three-tier model for teaching mathematics in a context approach, outperformed the conventional approach, reflecting the regular practices of primary school teachers in MM. The findings have implications for policy and the ongoing professional development of mathematics teachers.

DOI: 10.61137/ijsret.vol.11.issue1.106

Art without Borders: Exploring Transcultural Adaptations in Visual Creativity
Authors:-Sarika Tyagi

Abstract-This article delves into the concept of transcultural adaptations in visual arts, a practice that involves blending diverse cultural traditions, aesthetics, and ideas to create innovative and boundary-defying works. From historical exchanges along trade routes to the digital innovations of the contemporary era, transcultural art reflects the dynamic interplay of cultures across time. While celebrating creativity and hybridity, it also navigates critical ethical questions surrounding cultural sensitivity, power dynamics, and authenticity. By examining its historical roots, modern practices, and societal impact, this piece highlights the role of transcultural art in fostering dialogue, empathy, and inclusivity, ultimately enriching the global artistic landscape.

DOI: 10.61137/ijsret.vol.11.issue1.107

The Role of TiO2 Nanoparticles in Enhancing the Structural Properties and Thermal Stability of PVA Nanocomposites
Authors:-Assistant Professor R.Venugopal, Associate Professor Chandana.N, Assistant Professor S.Kiran, Assistant Professor B.Srinivas

Abstract-Polyvinyl alcohol (PVA) nanocomposites reinforced with titanium dioxide (TiO2) nanoparticles have garnered significant attention due to their unique properties and potential applications. In this study, we investigated the impact of TiO2 incorporation on the structural characteristics and thermal stability of PVA-matrix-based nanocomposites. The PVA polymer nanocomposite films were prepared using a solution casting method. The structural studies of the prepared films were characterized via X-ray diffraction (XRD), transmission electron microscopy (TEM). Moreover, the thermal properties of the prepared films were characterized by DSC, TGA and DTA. The addition of TiO2 nanoparticles induces structural changes in the PVA matrix. TEM studies showed that a PVA polymer surrounds TiO2 in its entirety. The PVA-TiO2 nanostructure is the same as the structure of a core-shell nanostructure. TiO2-doped PVA nanocomposites exhibited improved thermal stability. Thermogravimetric analysis of the nanocomposite films demonstrated enhanced resistance to thermal degradation. DSC analysis of the PVA-TiO2 nanocomposite films revealed that the glass transition temperature (Tg) and melting temperature (Tm) were 141°C and 265°C, respectively, for the 8 wt.% TiO2-incorporated PVA-TiO2 nanocomposites. The TGA and DTA studies of these nanocomposites revealed that their degradation behavior follows a four-step process. In comparison to those of pure PVA, these composites exhibit a sluggish decomposition rate, suggesting that the better thermal stability of these composites can be attributed to the better interaction among the -OH functional groups of PVA and TiO2 nanoparticles. These nanocomposites hold promise for various applications, including coatings, sensors, and optoelectronic devices. The combined effects of structural reinforcement and thermal stability make these materials attractive for engineering applications.

DOI: 10.61137/ijsret.vol.11.issue1.108

The Potential of Durian Husk, Durian Leaf-Litter and Banana Pseudo Stem as Bio-Leather
Authors:-Erika Grace Y. Sartagoda, Claire Joy Alicarte, Cyra Fathmah Cotin, Ruben Jr. Loren, Shecainah Lagaran, Cheerwina D. Puyales

Abstract-This study aimed to investigate the potential of durian husk, durian leaf litter, and banana pseudo stem as bio-leather. The bio leather was made from durian husk, durian leaf litter and banana pseudo stem. The bio leather made from these materials were tested in terms of its thickness, elongation and tensile strength. Also, as comparison the synthetic leather was tested according to its thickness, elongation and compression strength. The tests were performed at TERMS Concrete and Materials Testing Laboratory, Inc. Data were analyzed using mean and Mann Whitney U test. Results showed that, bio leather can be used to make light weight wallets since it only requires less thickness, low percentage of elongation and low tensile strength. For synthetic leather, can be used to make bags since the values of the indicators are high. The bio leather and synthetic leather do not significantly differ in terms of thickness, elongation and tensile strength; therefore, bio leather can be a good substitute for synthetic leather in making valuable items with high economic value.

DOI: 10.61137/ijsret.vol.11.issue1.109

The Reception Theory and the Value of Adaptation in Literature and Visual Arts
Authors:-Sarika Tyagi

Abstract-This article explores the intersection of reception theory and the value of adaptation in literature, visual arts, music, and theater. Reception theory, pioneered by Hans Robert Jauss, shifts the focus from the creator to the audience, emphasizing the evolving cultural and personal contexts that shape how stories are interpreted. Adaptations serve as transformative dialogues between the original work, its reimagining, and contemporary audiences, ensuring stories remain relevant across time and space. Examples such as Jean Rhys’s Wide Sargasso Sea, Alfred Hitchcock’s Rebecca, and Lin-Manuel Miranda’s Hamilton illustrate how adaptations reframe narratives to address new perspectives, cultural dynamics, and societal values. This article highlights how the reinterpretation of familiar tales enriches their meaning, engages diverse audiences, and underscores the timeless power of storytelling. By applying reception theory, the article demonstrates that the true value of adaptations lies in their ability to connect, challenge, and inspire audiences across generations.

DOI: 10.61137/ijsret.vol.11.issue1.110

Project Naiad: An Automated Smart Irrigation Revolution for Urban Home Gardens Using Arduino UNO R4 Wi-Fi
Authors:-Bernard Felipe B. Capalit, Client Teejay N. Jimenez, Anna Fatima P. Padao, Cairoden L. Usman Jr.

Abstract-This study aimed to develop a prototype of an automated smart plant watering system for urban home gardening, focusing on the reliability and functionality of its monitoring, notification, and water dispensing features. The system incorporated components such as the Arduino Uno R4 WiFi, DHT22 sensor, soil moisture and water level sensors, a raindrop sensor, and a submersible pump to address urban gardening challenges. The study evaluated the accuracy of the system’s sensors, the real- time data display, and SMS notifications, as well as the precision of water dispensing based on soil moisture levels. Results indicated high reliability, with most sensors achieving accuracy rates between 90% and 100%. The soil moisture sensor provided consistent readings, while the raindrop and water level sensors performed with near- perfect accuracy, enabling precise environmental monitoring. Notification features, including the LCD display and SMS alerts, were effective, with minimal delays in SMS reception. The water dispensing system demonstrated precision, adjusting water volume according to soil moisture levels, achieving an average water conservation effectiveness of 85% or higher. Additionally, a weak negative correlation between soil moisture and water dispensed highlighted the system’s responsiveness to environmental conditions. In conclusion, the prototype proved effective in monitoring and responding to soil conditions while minimizing water usage, making it a viable solution for urban home gardening. Future work could explore IoT-based enhancements for improved real-time monitoring, remote control, and data logging to further optimize system functionality.

DOI: 10.61137/ijsret.vol.11.issue1.111

Spammer Detection and Fake User Identification
Authors:-Assistant Professor Devi .S, Nived P J, Abhijith M, Boya Pavan Kumar, Spandau Gowda B C

Abstract-Social networking platforms attract millions of users globally. The interactions of these users with sites like Twitter and Facebook have a significant effect, often bringing about negative consequences in everyday life. Major social networking sites have become prime targets for spammers who disseminate vast amounts of irrelevant and harmful information. For instance, Twitter has emerged as one of the most extensively used platforms, leading to an overwhelming influx of spam. Fake accounts distribute unwanted tweets to promote services or websites, impacting genuine users and causing disruption in resource utilization. Additionally, the likelihood of spreading misinformation through counterfeit identities has grown, resulting in the circulation of harmful content. Lately, research has increasingly focused on detecting spammers and identifying fake accounts on Twitter within the realm of modern online social networks (OSNs). This paper examines various methods employed to identify spammers on Twitter.

Integrated Approach to Emotion Recognition Across Multiple Modalities
Authors:-Dr. Kavitha C, Jananisri K, Monisha B T, Prathibha G, Shanmitha P, Niranjani T

Abstract-Multimodal emotion recognition is essential for advancing human-computer interactions and enabling applications like mental health monitoring and social robotics. This study focuses on utilizing text, audio, and motion data from the IEMOCAP dataset to develop independent models that capture unique emotional cues from each modality. The audio model employs a hybrid architecture combining Convolutional Neural Networks (CNN), Multi-Head Attention, and Gated Recurrent Units (GRU), achieving an accuracy of 81%. The text model leverages a CNN-based approach inspired by Temporal Convolutional Networks (TCN), achieving 94% accuracy. For motion data, a Spatio-Temporal Graph Convolutional Network (ST-GCN) was implemented, achieving 63% accuracy. A score-level fusion strategy integrates these models, improving the overall recognition performance. Evaluations using metrics like accuracy, precision, and recall demonstrate how multimodal approaches can provide a more accurate and reliable emotion recognition system by combining complementary information from diverse data types.

DOI: 10.61137/ijsret.vol.11.issue1.112

Exploring How Globalization and Migration Have Impacted the Transformation of Religious Practices among the Youth in Singapore
Authors:-Margaret Pereira, Dr. Md Rosli Bin Ismail

Abstract-Youth expect religion to create meaning in life. These expectations play a significant role in practised religion and significant changes in the religious landscape. Participation in places of worship continues to decline. Organised religion might be facing a shifting landscape but this does not mean people are shunning religion. The interactions between religious institutions and an individuals’ perspectives of religion are investigated to reveal the transformation of religious practices in Singapore, from the lens of globalization and migration. Kierkegaard’s Theory of Existentialism is used along with non-probability purposive sampling with an objective to explore how globalisation has affected religious practices of Christian youth in Singapore and to investigate how migration has affected religious practices among Christian youth in Singapore. The key informants are six young Singaporean Christian adults between 25 and 30. Qualitative approach, semi-structured interviews, open-ended questions, in-depth interviews and thematic analysis is used.

DOI: 10.61137/ijsret.vol.11.issue1.113

Adaptive Reuse and Customization
Authors:-Harishanthana US

Abstract-Adaptive reuse is the best eco-friendly design strategy to repurpose existing building forms, stepping towards sustainability and a better environment. This type of revitalization is not restricted to buildings of historic significance but is also a smart strategy adopted in the case of archaic buildings. Customizing and reusing the existing built form not only saves money and profit but also a large amount of reduction in energy consumption and environmental impacts. Preservation, Rehabilitation, Restoration, and Reconstruction are major methods in bringing Adaptive reuse and Customization efficiently. Reusing the older vacant buildings for other purposes forms a very important outlook of any urban regeneration scheme and the adaptation process suggests opting for new technologies and design concepts that will support the older built to acclimate successfully to contemporary requirements without destroying the existing urban form. Adopting the adaptive reuse approach for the redevelopment of older vacant buildings provides added benefits to the regeneration of an urban area in a sustainable way, by transforming these buildings into usable and accessible units and providing a new sense of access to the public. While a large amount of historically built structures are being demolished and reconstructed. Adaptive reuse and customization could retain the built environment to the functions and needs and also maintain the historical facts and cultural factors.”

DOI: 10.61137/ijsret.vol.11.issue1.114

Fault Identification of Vibration-Based Condition Monitoring of Motor Using Minitab and Matlab: A Case Study on Francis Turbine
Authors:-Vishwas I, Bhagyaraj KS, Samiullah R, Ashok C, Professor Dr. Yadavalli Basavaraj, Professor Dr. V. Venkata Ramana, Assistant Professor Dr. Pavan Kumar.B. K

Abstract-This paper discusses the identification of faults in a Francis turbine using vibration-based condition monitoring with Minitab and MATLAB. The vibration signals are analyzed to detect faults in the motor components of the turbine. Statistical analysis uses Minitab to identify trends, while MATLAB carries out advanced signal processing, including Fast Fourier Transform (FFT) and wavelet analysis, to extract fault features. The combined approach effectively diagnoses issues like misalignment and bearing defects, showing its value in predictive maintenance for improved turbine performance.

DOI: 10.61137/ijsret.vol.11.issue1.115

Innovative Seed Sowing Machine for Improved Agricultural Productivity and Efficiency
Authors:-Mudgal Dipak Dinesh, V.D Dhanke

Abstract-This research focuses on the design and development of an innovative seed sowing machine aimed at improving agricultural productivity through precision and efficiency. Traditional sowing methods, which are either manual or use basic machinery, face challenges like inconsistent seed spacing, high labor requirements, and frequent blockages in seed dispensing tubes. These issues lead to reduced crop yields, increased operational costs, and significant seed wastage.The proposed seed sowing machine addresses these limitations by integrating automated seed dispensing, consistent depth control, and a blockage detection system using sensors. This machine is designed to place seeds uniformly at a specific depth and spacing, enhancing germination rates and ensuring even crop growth. Testing results demonstrate improved accuracy in seed placement and reduced downtime, showing a potential to save up to 40% of labor compared to traditional methods.Overall, this seed sowing machine offers a cost-effective and efficient solution for small to medium-scale farmers, enabling more sustainable and productive farming. This research lays the groundwork for future advancements in automated agricultural machinery, contributing to the broader goal of technological innovation in agriculture.

DOI: 10.61137/ijsret.vol.11.issue1.116

Flow Investigation over Oblique Wing Configuration
Authors:-Professor Dr. Prasanta Kumar Mohanta, Mysa Koushik, Borlakunta Praneeth, Y. Shanmukha Shambhavi

Abstract-This paper discusses the aerodynamic performance of oblique wing configuration in transonic and supersonic flight regimes. By using CFD tools within the ANSYS, the research has explored the function of oblique wing towards wave drag reduction and efficiency enhancement. Pivot angle variations of 0°, 30°, 45°, and 60° were used in asymmetrically designed wing and analysed at Mach 0.9 and 1.2. Some critical parameters include CL, CD, and pressure distribution, through which the design attains optimum operating characteristics. Some results reveal wave drag at specific pivot angles with the oblique wing. As the wave drags show least values for specific pivot angles (30°: Mach 0.9; 45°: Mach 1.2), these result in great applicability in improved aerodynamic efficiency and adaptability in varied conditions of flight towards the further developments of high-speed aircraft technology.

DOI: 10.61137/ijsret.vol.11.issue1.118

Performance and features of Amazon S3
Authors:-Fawaz Ali Syed, Mugdha Dharmadhikari

Abstract-This research paper investigates the multifaceted landscape of Amazon Simple Storage Service (Amazon S3), a pivotal component of cloud infrastructure provided by Amazon Web Services (AWS). By synthesizing findings from academic papers, industry reports, and case studies, it explores the fundamental features, security considerations, best practices, and real-world applications of Amazon S3. The analysis underscores the imperative of configuring S3 buckets meticulously to mitigate security risks, citing numerous instances of misconfigurations leading to data breaches. Through an in-depth examination of security best practices advocated by experts, including access control policies (ACPs), encryption mechanisms, and monitoring protocols. Additionally, it evaluates the scalability, reliability, and versatility of Amazon S3, positioning it as an indispensable asset for enterprises across various sectors. By leveraging insights from diverse sources, this research paper offers a comprehensive understanding of Amazon S3’s capabilities and advantages, providing actionable recommendations for optimizing its usage while safeguarding data integrity and confidentiality.

DOI: 10.61137/ijsret.vol.11.issue1.119

A Promising Breakthrough for Prostate Cancer Screening
Authors:-Jalene Jacob

Abstract-Prostate cancer is a major cause of morbidity and mortality among men worldwide. While traditional screening methods, such as Prostate Specific Antigen (PSA) testing and Digital Rectal Examiniations (DRE), have facilitated early detection, they face limitations, including false results and difficulty distinguishing aggressive from non-aggressive cancers. Recent advancements in urine-based testing offer a non-invasive, accurate alternative that improves diagnostic precision and reduces unnecessary biopsies. These tests analyze genetic and RNA biomarkers, providing personalized risk scores to guide biopsy decisions without requiring a DRE. They also address cultural barriers to screening and promote higher participation rates, particularly in underserved populations. Urine-based tests have the potential to optimize healthcare resources, reduce costs, and improve public health outcomes through early detection and intervention. However, equitable access, patient education, and data privacy protections remain critical considerations. As these tests become more widely available, they may transform prostate cancer screening and care.

From Irrelevant Utilisation to Excessive Dependence
Authors:-U. Sandhya Rani, S. Hemalatha, S. Mercy, T. Sushma Raj, Paila Bhanujirao

Abstract-The information relates to the use of substances that are harmful to one’s social, physical, mental, and emotional well-being, such as alcohol, opioids, tobacco, and some addictive medications like baclofen. This will have a complete impact on health. If used recreationally and developed into a habit of reliance. Substance misuse should be managed in its early stages since it cannot be stopped once it has developed into a habit. It can be challenging to stop using drugs if one has become habituated to doing so, and occasionally it can result in potentially fatal situations. When prescribed, some medications, such as opioids and non-opioid medications should be taken. However, longer periods of time should not be spent consuming them. And shouldn’t be stopped abruptly. Tapering the doses will help to progressively discontinue the consumption. In the event that consumption is abruptly stopped, coma or death may result. Substance abuse may influence vital organs over time, changing typical vital values over time. Because of reliance, each organ in the body will sustain harm through a variety of means.

DOI: 10.61137/ijsret.vol.11.issue1.120

Why a Flexible Workplace is Essential in a Modern Organization
Authors:-Anushka Gaikwad, Vaani Sharma, Anmol Rai

Abstract-The evolving dynamics of modern workplaces underscore the growing importance of flexible work arrangements in addressing the diverse needs of today’s workforce. This research delves into the necessity and impact of flexible workplaces, aiming to understand their prevalence, motivational drivers, and implications across various demographics, including students, professionals, and part-time employees. The study adopts a multi-faceted approach to examine patterns of flexibility, encompassing remote work, hybrid models, and flexible working hours, while evaluating their role in enhancing productivity and promoting a better work-life balance. A key focus is placed on identifying the motivational factors that lead individuals to prefer flexible arrangements, such as improved productivity, reduced commuting time, educational commitments, and family responsibilities. The study also assesses the challenges encountered, including time management difficulties, communication barriers, technical issues, and social isolation. By exploring these dimensions, it seeks to illuminate how flexible work environments can both empower individuals and pose unique obstacles that require organizational attention. In addition to individual experiences, the research evaluates organizational support systems and infrastructure, such as the provision of digital tools, internet allowances, structured guidelines, mental health initiatives, and workspace accommodations. These mechanisms are analyzed to understand their effectiveness in creating a conducive environment for flexible working. The study further examines how flexible work arrangements influence productivity across different contexts and demographic groups, offering valuable insights into their broader organizational and societal implications. The findings provide actionable recommendations for organizations aiming to implement or improve flexible work policies. These include fostering a culture of inclusivity, investing in digital infrastructure, offering targeted training programs, and creating clear guidelines to support employees effectively. Ultimately, the research highlights the transformative potential of flexible work arrangements in building resilient, adaptive, and employee-centric organizations capable of thriving in a rapidly changing work environment.

DOI: 10.61137/ijsret.vol.11.issue1.121

Telugu Voice Based Farmer Friendly Equipment Booking System
Authors:-Assistant Professor Durgunala Ranjith, Muskaan Thabassum, Kanraj Dhanush, B Bharath Kumar

Abstract-Agricultural equipment booking can be a challenging task for rural farmers due to language barriers and the complexity of existing digital platforms. This study is based on the concept of equipment rental. The E-commerce website has been improved as part of this project to bridge the gap between the farmer and the vendor on a lease basis. Only the user has access to the main programme after going through the login procedure; only the user may pick and book resources. This paper is jam-packed with information about the products. Farmers will benefit from this paper. The main goal of this website is to manage a variety of agricultural machinery, including Harvester, JCB, Tractor, Pickup, Rotor, and other agricultural machinery. End users will find the proposed system simple to use. As a result, we created a single website. We are attempting to provide the farmer or user with a solution that allows them to rent the goods by the hour.

DOI: 10.61137/ijsret.vol.11.issue1.122

Quantum Computing and its Effect on Sustainability
Authors:-Ravi Teja G, Associate Professor Dr. S. R. Raja

Abstract-Quantum computing is a new technology capable of solving problems that traditional/normal computers cannot handle. It is based on principles like superposition, entanglement, and interference to process information in ways that are not possible in classical computing. Unlike traditional computers that rely on bits as units of information, quantum computers use qubits, which can exist in multiple states simultaneously. This unique ability of qubits enables the quantum machines to perform computations at speeds that cannot be attainable by classical/normal systems. This new technology has the potential to transform all industries by addressing challenges in optimization, simulation, and data processing. For instance, quantum algorithms can simulate complex molecular interactions, leading to faster drug discovery in the pharmaceutical industry. Similarly, in logistics, quantum computers can optimize supply chains and reduce energy consumption, supporting more sustainable practices. Despite its promise, quantum computing also faces hurdles such as high costs, limited accessibility, and the need for stable operating environments.

The Role of Trolling in Mental Health and Creativity of Online Content Creators
Authors:-Tanisha Das, Assistant Professor Ms. Megha D. Prasad

Abstract-Trolling, defined as repeated and intentional online harassment, has become a significant issue for online content creators, affecting their mental health and creativity. This study aims to explore the role of trolling on psychological well-being and creative processes of content creators, addressing the gap in existing literature. Utilizing a qualitative exploratory design, semi-structured interviews were conducted with online content creators aged 18-45 who have experienced trolling. Participants were recruited through social media platforms using purposive and snowball sampling techniques. Data were analyzed thematically to identify patterns related to coping strategies, emotional impact, influence on content, long-term effects, and the role of platform support. The findings revealed that trolling contributes to heightened anxiety, self- censorship, and decreased motivation to produce creative content. Creators also reported dissatisfaction with the current support systems on social media platforms, highlighting a need for better moderation policies. This research underscores the importance of developing more effective support systems and mental health resources for content creators, along with stronger platform policies to combat trolling. The study contributes to a deeper understanding of the dual impact of trolling on mental health and creativity, paving the way for further research on supportive interventions in digital spaces.

Optimizing AI-Driven Decision Support Systems: Balancing Efficiency, Accuracy, and Ethical Considerations
Authors:-Yoga Srinivas B, Dr S R Raja

Abstract-Optimizing AI-driven decision support systems necessitates a careful balance between efficiency, accuracy, and ethical considerations. Efficiency involves ensuring that the system processes data swiftly and provides timely insights. Accuracy emphasizes the need for reliable and precise outputs to inform decision-making. Ethical considerations are paramount, addressing potential biases in data and algorithms to ensure fair and just outcomes. Transparency in the decision-making process fosters trust and accountability. By integrating these factors, AI-driven decision support systems can enhance decision-making processes while upholding ethical standards and maintaining user trust.

DOI: 10.61137/ijsret.vol.11.issue1.123

Green Revolution in Vector Management
Authors:-Stelson F. Quadros

Abstract-The primary vectors for the spread of diseases of concern like malaria, dengue, are mosquito species, particularly Aedes and Culex. There has been an exponential use and increased reliance by smaller groups, not specific to municipal and govt health bodies, but by housing societies and private pest control companies who rely on the acceptability of Thermal Fogging, as one of the key control or management factors of mosquito in urban setup. The professional pest control agencies are forced to adopt the use of Thermal Fogging even if there are ULV based options as there is fairly low awareness and poor visible changes at municipal level where adoption of ULV is not seen for more immediate adoption at social self help groups and with housing societies or with professional pest management companies. The urgent need to induct for a more less pollutant carrier, like BIODIESEL in thermal fogging and use of Plant Extract based Larvicides in water sources having significantly low toxicity will help build a more sustainable and low toxic mosquito management program, helping the human society at large to be truly living healthy through the means of Integrated Mosquito Management. In stark contrast where the use of polluting carriers like diesel leaves more lasting environmental damage affecting many more lives, than saving a few.

DOI: 10.61137/ijsret.vol.11.issue1.124

Implementing a Gamified Learning System for Enhancing Student Engagement and Motivation Using Reward-Based Mechanisms and Machine Learning
Authors:-Anand Sharma, Kunal Borage, Sujal Trivedi, Nikhil Neware, Professor Radhika Adki

Abstract-Student engagement would be one of the core elements that improve educational outcomes in the classroom. A gamification framework with machine learning would increase participation and personalize the experience of learning. The framework, through mechanics such as points, badges, leader boards, and challenges, encourages the participants to engage in the learning experience and enjoy it. However, machine learning facilitates adaptive learning by adapting the content based on the individual’s performance metrics. Student sentiment analysis helps to identify which students are in need of support, and predictive analytics would be used for identifying the students that may require more support. Real-time analytics allows teachers to keep track of student progression as well as classroom trends in real time. The system was designed to improve engagement and increase efficiency in the learning process. It considers the fact that competition can get unhealthy at times as well.

DOI: 10.61137/ijsret.vol.11.issue1.125

EMO Diary: Daily Diary with Sentiment Analysis
Authors:-Dr.Kavitha Soppari, Sk Hussain, Gvn Surya, M Pulla Rao

Abstract-The “Daily Diary Writer with Sentiment Analysis” project is a full-stack web application focused on enhancing personal well-being through sentiment analysis. Users can write daily diaries journals, and the system uses natural language processing to analyze the emotions expressed in their entries. This helps users reflect on their emotional patterns over time. Voice input allows for hands-free journaling, making the process more convenient and accessible. The project promotes self-reflection and emotional well-being through detailed sentiment insights.

DOI: 10.61137/ijsret.vol.11.issue1.126

Automation Bot for Data Extraction and Processing
Authors:-Assistant Professor Ms. Shristy Goswami, Aditya Singh, Anant Shukla, Anurag, Divanshu

Abstract-This paper presents the development and implementation of an automation bot designed for efficient data extraction and processing tasks. The bot automates the process of accessing a website, downloading an input Excel file, and extracting account numbers from the file. It then compares the last four digits of each account number with the digits in the names of zip files available on the website. Upon finding a match, the bot downloads the corresponding zip file, extracts its contents, and processes the required data from the unzipped text file, subsequently loading this data into the specific account number’s field. This automation bot significantly enhances data handling efficiency, reduces manual errors, and streamlines the data management process.

DOI: 10.61137/ijsret.vol.11.issue1.127

Thermal Insulating and Sound-Insulating Fiberboards Using Durian (Durio Zibethinus Murray) and Cogon Grass (Imperata Cylindrica)
Authors:-Denaga, Allona Devy P., Mamac, Leah O., Suguitan, Janine S., Sherwin S. Fortugaliza

Abstract-Global warming is impacting our communities, health, and wildlife, while noise pollution negatively affects both physical and mental well-being. This study examined durian husk and cogon grass fibers as sustainable materials for fiberboard production, focusing on their moisture resistance, thermal insulation, and soundproofing properties. These natural fibers outperformed traditional fiberboards. In sound absorption tests, durian fibers achieved 64.017 Hz, cogon fibers measured 67.600 Hz, and combined fibers recorded 62.617 Hz, compared to 83.033 Hz for the control group. Regarding thermal performance, durian fiberboards exhibited temperatures of 36.25°C and 36.95°C, while cogon fiberboards measured 37.90°C and 39.00°C. The commercial insulator consistently registered temperatures of 45.05°C and 46.05°C. Both durian and cogon fiberboards demonstrated 0% water absorption after 24 hours, in stark contrast to traditional fiberboard, which absorbed 200% more. This research underscores the potential of durian husk and cogon grass fibers as superior, eco-friendly alternatives for construction, effectively addressing noise and heat challenges in tropical regions.

Wireless Charging Platform for Drones Using WPT Technology
Authors:-Assistant Professor Ms. G. V. Swathi, S. Ronak Jain, S. Pushpa, K. Naga Sai

Abstract-Drones are becoming indispensable tools in various critical sectors of India, such as agriculture, disaster management, land surveys, mining, and infrastructure mapping. Their ability to access remote, hazardous, or hard-to-reach areas makes them invaluable for tasks, such as crop monitoring, search and rescue, and real-time data collection. However, the effectiveness of drones in these mission-critical applications is often limited by their battery life and the need for frequent recharging, particularly in environments where human access is difficult or impossible. This project addresses this challenge by developing a wireless power transfer (WPT) system for drone charging. The system converted a standard 230V supply into a low-voltage DC output, which was then wirelessly transferred via a high-frequency (100kHz) inverter and coil setup. This WPT system is particularly suited for use in remote or inaccessible locations, where minimizing downtime is critical.

DOI: 10.61137/ijsret.vol.11.issue1.128

Advancing Human-Centered Artificial Intelligence: Enhancing Explainability Real-World Applications
Authors:-Sriram R, Dr S R Raja

Abstract-Human-centered artificial intelligence (HCAI) emphasizes designing AI systems that prioritize human values, ethics, and usability, fostering trust and responsible adoption. This research explores the advancement of HCAI by addressing key challenges such as improving explainability, integrating ethical considerations, and optimizing real-world applications across diverse sectors. By investigating state-of- the-art methods for interpretable machine learning, the study aims to enhance user understanding and transparency in AI decision-making. It further examines frameworks for embedding ethical principles, including fairness, accountability, and privacy, into AI system design. Additionally, the research evaluates case studies from healthcare, education, and autonomous systems to illustrate the transformative potential of HCAI. This study underscores the need for interdisciplinary collaboration and innovation to ensure AI technologies align with human values and societal goals, paving the way for more inclusive and sustainable AI solutions.

DOI: 10.61137/ijsret.vol.11.issue1.129

Crop Disease Detection System
Authors:-Rupesh Gaikwad, Sarvesh Dharme, Vedant Zawar, Nachiket Kulkarni, Professor Prachi Tamhan

Abstract-One of the important and tedious tasks in agricultural practices is the detection of disease on crops. It requires time as well as skilled labor. This paper proposes a smart and efficient technique for the detection of crop disease which uses computer vision and machine learning techniques. Every year India loses a significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, a computer-aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, CNN was proposed to detect plant disease. It has three processing steps namely feature extraction, downsizing image, and classification. In CNN, the convolutional layer extracts the feature from the plant image. It helps to give personalized recommendations to farmers based on soil features, temperature, and humidity.

DOI: 10.61137/ijsret.vol.11.issue1.130

Design and Implementation of a Cost-Effective, Low-Latency IoT-Enabled Dental Chair: A Global Remote-Control Solution for Enhancing Clinical Efficiency and Pre-Operative Preparations
Authors:-Hiren Uthaiah M S, Khyati Priyesh, Manjunath K V, Samichi S Mathad, Siddhart Dhargi, Suhas S Rao

Abstract-This research introduces two innovative methods to convert a standard 16-control dental chair into an IoT-enabled dental chair at a minimal cost of under 2,000 INR. The first method involves directly interfacing the chair’s control wires with a 16-channel relay and an ESP32 microcontroller, enabling remote operation through the Blynk IoT platform. The second method leverages signal analysis by identifying the dental chair PCB’s communication lines, capturing control signals with a logic analyzer, and replicating them via the ESP32 for seamless functionality. Both approaches offer global control with minimal delay (<10ms) and enhance operational efficiency by enabling preemptive actions, such as heating water or cleaning the spit bowl remotely. This study provides a scalable, low-cost solution for modernizing dental chairs, ensuring ease of use and adaptability for dental clinics worldwide.

DOI: 10.61137/ijsret.vol.11.issue1.131

Facial Expression Detection Using Machine Learning Techniques
Authors:-Associate Professor Dr Sudhamani, Assistant Professor Kavya S N, Galal Ahmed Ghaleb Abdo Almaghrebi M, Mohammad Reza Sharifi, Research Scholar Jagadeesh M

Abstract-Facial expression detection has emerged as a transformative technology with applications in numerous fields such as healthcare, security, and entertainment. The proposed system aims to enhance user engagement by dynamically tailoring playlists based on the user’s emotional state. The proposed Emotion Recognition provides a foundation for further exploration and development of intelligent systems that adapt to users’ emotional states, fostering more immersive and personalized interactions in the realm of digital entertainment.

DOI: 10.61137/ijsret.vol.11.issue1.132

Understanding A.I.
Authors:-Kajal Nanda

Abstract-This paper aims to provide an in-depth understanding of A.I., its historical development, and its transformative influence on modern civilisation. We will discuss significant concepts, evolving technologies under influence, technological advancements, and ethical and unethical A.I..

DOI: 10.61137/ijsret.vol.11.issue1.133

Latest Trends and Techniques Developed in Mechanical Engineering
Authors:-Nimgaonkar S.S., Gadade R.A., Gaikwad Niti N Bhagwat, Tambe Laxman Tukaram

Abstract-Mechanical engineers dream up and design amazing machines and technologies that improve people’s lives in all kinds of ways. From airplanes and cars to robots and renewable energy systems, mechanical engineers have shaped our modern world. New technologies are opening up incredible opportunities for innovation. Read on to learn about the exciting changes & future trends in mechanical engineering and how you can prepare for it!

DOI: 10.61137/ijsret.vol.11.issue1.134

Detecting Unauthenticated Access Using Honeypot Sentinel
Authors:-Varshini J, Asvica J, Bharathi A K, Deepika P, Dharshana S, Yazhini K

Abstract-Unauthorized access remains a critical threat to network security, as attackers can exploit vulnerable systems to obtain sensitive data or disrupt services. This paper introduces Honeypot Sentinel, a proactive intrusion detection tool designed to flag unauthorized access attempts by monitoring and verifying usernames and IP addresses. Honeypot Sentinel uses a MongoDB database for logging, enabling the system to record details of unauthorized access attempts efficiently. Upon detecting any access attempts outside the predetermined criteria, Honeypot Sentinel triggers alerts, allowing system administrators to promptly address potential threats. This approach provides network security teams with real-time data, helping them respond effectively to unauthorized access incidents.

Exploring the Role of Microglia Activation in Alzheimer’s Disease and Parkinson’s Disease
Authors:-Tamaradoubrah Favour Melex, Akuroseokike G Babbo

Abstract-Microglia, the principal immune cells within the central nervous system (CNS), are essential for maintaining neuronal homeostasis. Nonetheless, the chronic activation of microglia has been associated with the development of neurodegenerative diseases, notably Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). This review investigates the mechanisms underlying microglial activation, the dual functions of microglia in neuroprotection and neurotoxicity, and the implications for therapeutic strategies. By examining contemporary research, we aim to clarify the molecular pathways that link microglial activation to the progression of these diseases and identify potential approaches for modulating microglial responses to alleviate neurodegeneration.

Medicine Remiander Device Using ESP8266
Authors:-K. Likitha, M. Usha

Abstract-This journal discuss in detail on a suggested medication reminder device that will be made for senior citizens based on their problems. This study’s background is explained in the report, and its primary goal is to guarantee that the medication reminder device will be resolving issues that older people have. The problems that have been discovered are mostly focused at the elderly and are meant to address the problems that they encounter on a daily basis, particularly with regard to medication use. In order to design a better device, the study will also examine similar implemented devices and systems to determine the advantages and disadvantages of other pertinent devices and systems. This portable and economical system would be helpful to every age group also.

DOI: 10.61137/ijsret.vol.11.issue1.135

Assessment of Sustainable Building Material and the Benefits of Green-blue- grey Infrastructure for Feasible Urban Flood Risk Management
Authors:-Hambal Ahmad Khan

Abstract-The green infrastructure has some other benefits besides flood risk reduction. Those benefits mainly covers conservation of water, energy, and improvement in air quality and much more. The materials used are the essential components of the green infrastructure. The proper design accompanied by the material properties provides the accountability of the mechanical strength of the infrastructure. Hence, the eco-friendly materials are given more priority for green infrastructure. The price is considered primarily when either selected or r4elated material is compared for the similar purpose. Except the social and environmental costs, the cost of the building element conveys only the cost of transportation and manufacturing. Therefore, the sustainable development of the nation relies on the proper choice of the construction materials having least burden on the environment. Moreover, the result of mixture of blue, green and grey infrastructure is likely the best adaptation strategy as these comply with each other. The grey infrastructure reduces the flooding risk while the green infrastructure has its own multiple benefits which is not offered by the grey infrastructure. The paper focusses on the contribution of the sustainable building material in order to reduce the impact of environmental degradation which could help in identification of the strategies that are highly effective in improving the urban flood risk management.

Heart Attack Risk Assessment Using Deep Learning with Feature Optimization
Authors:-Ch. Rishitha, G. Poojitha, B. Sahith, Profeesor Shashank Tiwari

Abstract-Heart attacks remain a critical global health issue, necessitating accurate predictive models to identify at- risk individuals and support preventive care. This project, titled “Heart Attack Risk Assessment Using Deep Learning with Feature Optimization,” applies deep learning techniques to assess the likelihood of a heart attack. The study utilizes a Fully Connected Neural Network (FCNN) model enhanced by feature optimization methods, ensuring that the most relevant predictors are prioritized. Additionally, the project incorporates risk visualization, enabling clear and actionable insights for early detection and management of heart attack risks.

DOI: 10.61137/ijsret.vol.11.issue1.136

What Role Do Artificial Intelligence and Machine Learning Play in Enhancing Human Resource Decision-Making Processes by Method from 2015 to 2025 Using Bibliometric Method
Authors:-Muhammed Bah

Abstract-This research examines the impact of artificial intelligence (AI) and machine learning (ML) on improving human resource (HR) decision-making procedures, with an emphasis on the years from 2015 to 2025. Employing a bibliometric approach, the study uncovers trends, obstacles, and prospects related to artificial intelligence and machine learning usage in human resource management. The results depicted in Figure 1 (“Document by Year”) indicate a marked rise in research activity after 2020, emphasizing an increasing interest in the incorporation of AI and ML in HR practices. Figure 2 (“Document by Area”) illustrates that computer science (45%) and business studies (30%) lead in research contributions, highlighting the technical and strategic aspects of these technologies. The geographic analysis shown in Figure 5 (“Document by Country”) reveals that 40% of the studies come from the United States, while European and Asian nations account for 30% and 20%, respectively. Institutional contributions, shown in Figure 7 (“Document by Affiliation”), indicate that 60% of research originates from academic institutions, while corporate research centers account for 25%. Figures 3 and 4 underscore the variety of sources and funding, showing a balance between academic integrity and practical uses, with government funding representing 50%. The research highlights the revolutionary impact of Artificial intelligence and Machine learning in human resource management, especially concerning talent acquisition, employee engagement, and workforce management. Nevertheless, ethical issues, biases in algorithms, and privacy threats present significant challenges. By combining technological advancements with ethical guidance, as illustrated by the trends shown in the figures, organizations can develop adaptable, inclusive, and effective HR systems that meet the changing needs of the workforce.

DOI: 10.61137/ijsret.vol.11.issue1.137

Development of Robotic Arm Using Arduino
Authors:-Lingam Lakshmi Vagdevi, Edupuganti Harshitha

Abstract-Innovation in robotic arm control has been sparked by the development of Arduino-based technology, which provides both experts and enthusiasts with an affordable and user-friendly platform. The creation of robot arm control with an Arduino controller is presented in this work. The project entails integrating sensors and Arduino microcontrollers to provide dynamic and accurate control over a robotic arm. Four servo motors—which rotate left, right, front, and back—control the suggested robot. The study lays the groundwork for future developments in this emerging topic by discussing the difficulties faced during the development process and offering solutions. The demonstrated robotic arm control system has the potential to increase access to robotics education and promote automation innovation due to Arduino’s broad availability and low cost.

DOI: 10.61137/ijsret.vol.11.issue1.138

Object Detection Using Ultra Sonic Sensor
Authors:-Assistant Professor. D .Veeraswamy, Yagnasri Madhav, Lakkakula Lohith, Balla Kanth Naga Ayyappa

Abstract-Ultrasound is simply sound whose frequencies are too high to be heard by the mortal observance, that’s to say the frequencies are above c 20 kHz. At the top end of the scale, ultrasound is used at frequentness up to several GHz. The main end of this system is to descry object that will be ahead of ultrasonic transducer. Utmost ultrasonic detectors are grounded on the principle of measuring the propagation time of sound between send and admit (propinquity switch). The hedge principle determines the distance from the detector to the glass (retro-reflective detector) or to an object (through- ray detector) in the measuring range. Ultrasonic detectors are grounded on the measured propagation time of the ultrasonic signal. They emit high- frequency sound swells which reflect on an object. The objects to be detected may be solid, liquid, grainy or in greasepaint form. It sends an ultrasonic palpitation out at 40 kHz which travels through the air and if there is a handicap or object, it will bounce back to the detector. By calculating the trip time and the speed of sound, the distance can be calculated. Ultrasonic detectors are a great result for the discovery of clear objects.

DOI: 10.61137/ijsret.vol.11.issue1.139

Strategizing Digital Transformation with LangGen Cloud Computing
Authors:-Nikhil A Rawool, Dr. Tatiana Walsh, Professor John Lewis

Abstract-Cloud Computing with field of emergence with frameworks with intelligent platform based on cloud which is designed with “reliability , availability “ with key deliverables with a specific and specialized platforms which are designed on methods for computing technology and service streamlined with pattern – self based analysis for Multimedia Management with Enterprise on Digital Platforms for reviewing Data Agents for digital Background for computing level of architecture format with a self-developing ecosystem for pattern recognition and texture delivering format.

DOI: 10.61137/ijsret.vol.11.issue1.140

Data Transmission Using Li-Fi Technique
Authors:-A.Jeevan, P.N.Koushik, K.Murali

Abstract-Light fidelity (Li-Fi) technology is a wireless communication system that utilizes visible light spectrum to transmit data with high speed and secure manner compared to the traditional Wireless Fidelity (Wi-Fi) architecture. In this paper a smartphone is used in Li-Fi communication system. The aim of this proposed approach is to maximize the bit rate with high accuracy by using the flashlight of built-in smartphone camera as a source to send data and detect the effect of using a built-in smartphone ambient light sensor and external light detector sensors that is connected to Arduino UNO circuit to receive data. Four practical experiments were conducted to discover which light sensor accomplish higher data bit rate and tested the system performance under changing the distance between transmitter and receiver. The evaluation results demonstrated that the data bit rate is better with the proposed research than the others, where it reached more than 100 bps with accuracy 100%.

DOI: 10.61137/ijsret.vol.11.issue1.141

Cotton Detector and Collector Robot
Authors:-Professor Meenakshi Annamalai, Ashwini Rode, Archana Sonawane, Parigha Patil

Abstract-Robots that harvest cotton have become a viable way to alleviate labor shortages and boost production efficiency. In order to detect, navigate, and gather cotton bolls in the field, these robots use cutting-edge technologies. Cotton boll detection relies heavily on deep learning and machine vision methods. An innovative weed identification model that distinguished weeds from cotton seedlings with a map of 98.43% was developed using the CBAM module, the BiFPN structure, and the bilinear interpolation technique (Fan et al., 2023). The chromatic aberration approach showed great sensitivity and specificity with a 91.05% identification rate for cotton boll detection in natural lighting (Singh et al., 2021). GNSS and optical detection techniques are combined in cotton harvesting robot navigation systems. While boll position estimation demonstrated great precision with an R2 value of 99% when stationary and 95% when moving, a pixel-based method for cotton row detection obtained 92.3% accuracy (Fue, Li, et al., 2020). These developments in navigation and identification aid in the creation of accurate and productive cotton harvesting robots. In conclusion, there is a lot of promise for automating cotton harvesting through the combination of sophisticated detection algorithms, navigation systems, and robotic manipulation techniques. But there are also issues with adapting these technologies to different crop kinds and field conditions, which calls for more study and advancement in this subject.

Beta Blocker Management Post MI: Navigating Continuation and Interruption Strategies
Authors:-Bhupathi Sravani, Sirasani Tapaswi

Abstract-This study examines the effects of interrupting versus continuing beta-blocker therapy in post-myocardial infarction patients. The ABYSS trial, a multicenter noninferiority study, found that interrupting beta-blocker therapy did not offer any advantages over continuation in reducing major cardiovascular events or improving quality of life. The interruption group experienced a slight increase in hospitalizations for coronary-related conditions. These findings challenge existing guidelines recommending beta-blocker discontinuation after one year for certain patients. The trial highlights the necessity for additional research to clarify the role of beta-blockers in modern post-MI care, especially for patients with preserved left ventricular function.

DOI: 10.61137/ijsret.vol.11.issue1.142

School Management Committees’ Roles and Academic Performance of Pupils in Selected Government-Aided Primary Schools In Bulambuli Town Council, Bulambuli District
Authors:-Nambuya Mary, Dr. Ssendagi Muhamad, Wolukawu Ambrose

Abstract-This study investigated School Management Committee roles and the academic performance of pupils in selected government aided primary schools in Bulambuli Town Council, Bulambuli District. The study sought to; examine the relationship between the supervisory role of School Management Committees (SMCs) and academic Performance of pupils; examine the relationship between the supervisory role of School Management Committees (SMCs) and academic Performance of pupils; and examine the effect of the consultative role of School Management Committees (SMCs) on academic Performance of pupils. The study adopted a cross-sectional research design. Both simple random sampling and purposive sampling techniques were used to select the sample of respondents. The researcher studied a sample of 82 participants who included teachers, SMC members from selected government-aided primary schools, officials from DEO’s office and CCTs of selected schools in Bulambuli TC. Questionnaires and key informant interviews were used for data collection. Quantitative data from questionnaire was analyzed for both descriptive and inferential statistics using SPSS and Excel while qualitative data was analyzed thematically. The findings of this study were that; SMCs have significant influence on academic performance of pupils. The Pearson correlation coefficient shows that there is a significant positive relationship between the administrative role of SMCs and academic performance of pupils, r = 0.729, p = 0.000; supervisory role of SMCs influences the academic performance of pupils, r = 0.689, p = 0.000; and consultative role of SMCs has a significant positive relationship with academic performance of pupils, r = 0.648, p = 0.000. From the findings of this study, the researcher recommended that the SMCs should work with school administration to provide support such as academic intervention programs to struggling pupils so as to improve the academic performance of pupils; tighten regular monitoring and assessment of pupils’ progress to identify areas of improvement; and work closely with parents and other stake holders to support pupils’ learning. By involving the broader school community in academic initiatives, school will create a network of support that might help pupils thrive academically.

DOI: 10.61137/ijsret.vol.11.issue1.143

Simulation of Harmonics in Electric Locomotive Power Supply Device
Authors:-Assistant Professor Mr.J.Munichandra Sekhar, R .Prasad, P.Ganesh, K.Vijay Kumar, A. Tharun

Abstract-An electric locomotive power supply device is responsible for providing electrical power to the traction motors that drive the locomotive. These systems often use alternating current (AC) or direct current (DC) to power the motors, and they can operate using either overhead catenary systems or third-rail power supplies. Simulation of a locomotive power supply device involves analyzing its electrical and mechanical performance, power quality and efficiency. The power electric device which works under condition of high power and heavy load, suffer from faults frequently. The main circuit of the device is a kind of single-phase full bridge half controlled rectifier circuit. Harmonics are higher-frequency components that distort the waveform of current or voltage. In locomotive power supply systems, harmonics are typically introduced by non- linear loads, such as the power electronic devices (inverters, converters) used in these systems. Harmonics can cause several issues, including increased losses, power quality .

Blockchain Based Student Council Election Portal
Authors:-Professor Kusumlata Pawar, Asmi Santosh Wayare, Pooja Krishnakant Chavan

Abstract-The focus of this project is to make a secure and transparent voting system at college level. Even though paper based voting was a traditional approach and is being used for centuries, but still as we are facing the challenges during the overall voting process which includes, security risk, lack of transparency, human errors and privacy concerns. So, to overcome this limitations and vulnerabilities we came up with the idea of a blockchain based voting system. It is in high demand due to the immutability, transparency and decentralized solutions. The objective of this paper is to incorporate blockchain technology to construct a secure, tamper-proof elections at college level. It will also help developers to build and deploy smart contracts. The use of smart contracts guarantees the accuracy and provides fast voting result and makes counting procedures protected against fraudulent actions. This technology supports peer-to-peer decentralized network in which all the transactions are stored in blocks. To sum up, the proposed system will shorter the time for voting process while offering security and authentication and it also dwindles the expenses as there is no need to print ballots.

HSS and HCS Cutting Tool Material Influencing Surface Roughness in Machining of GFRP
Authors:-Dr. K N Lingaraju, Dhanushree M R, Prashanth Kumar N, Prashanth N, Vinayak Basavaraj Ganiger

Abstract-This work is concerned with the machining of Glass Fiber Reinforced Plastics (GFRP), with a primary interest in how surface roughness is affected by cutting tool materials. GFRP has found a niche in aerospace, automotive, and marine applications, and has obtained recognition for its ratio of strength to weight, resistance to corrosion, and thermal stability. Because of the peculiar structure of E-glass fibers in epoxy resin matrix systems, specific problems concerning the use of this material arise, such as delamination, fiber pull out, and wear of tools, thus requiring tailored machining techniques. The experiment compared performance between cut rods using high-speed steel (HSS) and high-carbon steel (HCS) while machining a GFRP rod (30 mm diameter, 240 mm length) using a lathe. Surface roughness parameters are Ra, Rz, Rt, Rpk, all measured by a Talysurf device. The results showed that HSS tools led to smoother surfaces and greater accuracy but that HCS tools were more economically viable for less rigorous jobs. These results touch upon the need for tool selection based on application and give some insight into further pros in terms of coatings, monitoring systems, and further sustainable machining approaches.

DOI: 10.61137/ijsret.vol.11.issue1.144

Design and Simulation of Fuzzy Logic-Based Maximum Power Point Tracking for Solar Pv Arrays
Authors:-Mr.D.Ramesh, M. Bharath Kumar, Sunkara Gyan Harsh, Shaik Sadik

Abstract-This paper presents a Fuzzy Logic-based Maximum Power Point Tracking (MPPT) algorithm for Solar Photovoltaic (PV) systems to enhance energy efficiency. The proposed approach adapts to fluctuating solar irradiance and temperature by utilizing rule-based logic, eliminating the need for precise mathematical models. Unlike traditional methods, the fuzzy logic controller provides fast and accurate responses, minimizing power loss and improving performance. It continuously adjusts the duty cycle of a DC-DC converter to maintain operation at the peak power point. MATLAB/Simulink simulations show faster tracking, reduced oscillations, and higher energy harvest compared to Perturb and Observe (P&O) and Incremental Conductance (IncCond) methods. This robust solution maximizes PV output, advancing the feasibility of solar energy as a renewable source.

Simulation and Performance Analysis of Solar PV System Using MATLAB
Authors:-Dr. M. Prasad, Ch.Srinitha, S.Srujan, N.Deva Raj

Abstract-Photovoltaic power generation system implements an effective utilization of solar energy, but has very low conversion efficiency. The major problem in solar photovoltaic system is to maintain the DC output power from the panel as constant. Irradiation and temperature are the two factors, which will change the output power of the panel. A boost converter is utilized as a DC-DC converter.The simulation includes the modeling of a solar panel, and a power conversion unit such as a DC-AC inverter. Key parameters such as irradiance, temperature, and shading effects are considered in the analysis to assess their impact on the power output and overall system efficiency. The results highlight the dynamic behavior of the system under different operating conditions. The MATLAB/Simulink environment is utilized to evaluate the system’s performance, and a comparison is made between the theoretical and simulated values. It is obtained by using MATLAB Simulink Model.The aim is to effectively track the maximum power points considering the fluctuations in solar irradiation and temperature.

Efficacy and Safety of an Oral Nutritional Supplement in Treating Nutritional Deficiencies and Related Conditions: A Phase 3 Randomized Controlled Trial
Authors:-Reedhika Puliani, Deepika Sharma, Priyanka Shetty

Abstract-Nutritional deficiencies are common worldwide and can also lead to weak immunity, weak stamina, metabolism and decreased bone health. To address these challenges, relying solely on diet may be insufficient, as most individuals do not consume nutritionally balanced diets. Nutritional supplements can help in achieving optimal, balanced nutrition while preventing nutritional deficiencies. This multicentre, double-blind, randomized, parallel-group phase 3 clinical trial evaluated the efficacy and safety of a nutritional supplement from British Life Sciences, Pvt. Ltd, BSURE Sugar-Free (Dutch Chocolate Flavour) against a multivitamin powder (Zooversandhaus Jung, Germany) in patients with nutritional deficiencies, weak immunity, low stamina, compromised bone health, and weak metabolism. Over three months, 231 participants were recruited, with 200 completing the study. Results demonstrated that BSURE achieved 97% and 98% efficacy in improving weak immunity and stamina, respectively, and showed comparable safety and tolerability to the control product. These findings support the use of the product for nutritional support in adults.

Magma- Estate Agility
Authors:-Ritesh Kumar, Professor Bhumi Shah

Abstract-This is a presentation of the development of “Magma Estate Agility” web application. The application is designed with the use of HTML, CSS, and JavaScript for improving on estate management and aims at processes like property listing, tenant management, and sending in requests for maintenance. This paper describes the methodologies used, the technologies employed, and the results realized from the implementation of the project. From the findings, it shows that incorporating these technologies into the estate management process raises efficiency and user-friendliness in the processes.

Simulation of Harmonics in Electric Locomotive Power Supply Device
Authors:-Assistant Professor Mr.J.MunichandraSekhar, R .Prasad, P.Ganesh, K.Vijay Kumar, A. Tharun

Abstract-An electric locomotive power supply device is responsible for providing electrical power to the traction motors that drive the locomotive. These systems often use alternating current (AC) or direct current (DC) to power the motors, and they can operate using either overhead catenary systems or third-rail power supplies. Simulation of a locomotive power supply device involves analyzing its electrical and mechanical performance, power quality and efficiency. The power electric device which works under condition of high power and heavy load, suffer from faults frequently. The main circuit of the device is a kind of single-phase full bridge half controlled rectifier circuit. Harmonics are higher-frequency components that distort the waveform of current or voltage. In locomotive power supply systems, harmonics are typically introduced by non- linear loads, such as the power electronic devices (inverters, converters) used in these systems. Harmonics can cause several issues, including increased losses, power quality.

Simulation of Harmonics in Electric Locomotive Power Supply Device
Authors:-Assistant Professor Mr.J.MunichandraSekhar, R .Prasad, P.Ganesh, K.Vijay Kumar, A. Tharun

Abstract-An electric locomotive power supply device is responsible for providing electrical power to the traction motors that drive the locomotive. These systems often use alternating current (AC) or direct current (DC) to power the motors, and they can operate using either overhead catenary systems or third-rail power supplies. Simulation of a locomotive power supply device involves analyzing its electrical and mechanical performance, power quality and efficiency. The power electric device which works under condition of high power and heavy load, suffer from faults frequently. The main circuit of the device is a kind of single-phase full bridge half controlled rectifier circuit. Harmonics are higher-frequency components that distort the waveform of current or voltage. In locomotive power supply systems, harmonics are typically introduced by non- linear loads, such as the power electronic devices (inverters, converters) used in these systems. Harmonics can cause several issues, including increased losses, power quality.

MATLAB Implementation of Sine and Cosine Generator Using CORDIC Algorithm
Authors:-Assistant Professor Mr. Goutam Barma,K. Chakradhar,J. Appa Rao,S. Abhishek

Abstract-The CORDIC (Coordinate Rotation Digital Computer) algorithm is a versatile and efficient iterative method for computing a wide range of mathematical functions, including trigonometric, hyperbolic, exponential, logarithmic, and square root functions. Central to the CORDIC approach is its ability to perform vector rotations in a polar coordinate system, effectively transforming coordinates through a series of predefined angles. These methods eliminates the need for complex multiplications by utilizing simple shift and add operations, making it particularly well-suited for hardware implementations in resource-constrained environments, such as digital signal processors (DSPs) and field-programmable gate arrays (FPGAs).The algorithm operates in several modes, including rotation mode and vectoring mode, allowing it to adapt to various computational requirements. Each iteration reduces the angle by a fixed amount, using pre-computed arctangent values to guide the rotations. The convergence of the algorithm depends on the number of iterations, with higher iterations yielding greater accuracy. CORDIC’s unique architecture supports parallel processing, enabling simultaneous calculations of multiple functions, further enhancing its efficiency. Evaluation of trigonometric functions such as sine, cosine and tan has been obtained using MATLAB. This abstract encapsulates the fundamental principles, operational modes, computational advantages, and diverse applications of the CORDIC algorithm, underscoring its significance in modern digital computation and system design.

OpenCV- Based Intelligent Vehicle Surveillance and Time Stamping System
Authors:-Professor Dr.J.Preetha, Assisstant Professor Mr.R.Viswanathan, A Rasidha Begum, S Pooja, R Jona

Abstract-Automated traffic monitoring solutions have become necessary due to the difficulties of manual monitoring systems and the exponential growth in vehicular traffic. The new Advanced Vehicle Detection System described in this work uses sophisticated computer vision algorithms to identify, recognize, and log vehicle data in real time. Utilizing OpenCV, CNN (Convolutional Neural Network), YOLO (You Only Look Once), and OCR (Optical Character Recognition) technologies, the suggested system detects automobiles and records license plate information. In addition, the system gives law enforcement, traffic management, and institutional surveillance a reliable and scalable approach by automating the entry and exit timestamp logging process. Mostly we are developed for the college buses which has been include arrival and Departure time with an owner details and also the vehicle claim the insurance or not, These also updated the count of vehicle that are recognized by the entry and the exit time. Experimental results demonstrate the system’s high precision and efficiency, ensuring its practical applicability in real- world scenarios. This practical and efficient system is an excellent example of how technology can address real- world challenges in monitoring and managing vehicles.

DOI: 10.61137/ijsret.vol.11.issue1.145

PV Panel Drive 3-Phase Induction Motor Using Matlab Simulink
Authors:-Assistant Professor Mr.J.Munichandra Sekhar, K.Sudhakar, K.Pavan Kumar, K. Dheekshith

Abstract-This project presents a simulation-based study of a Photovoltaic (PV) panel driving a 3-phase induction motor using MATLAB Simulink. The model is developed to explore the feasibility of utilizing solar energy to power electric motors, which are essential in various industrial and agricultural applications. The PV panel generates DC power from sunlight, which is then converted into 3-phase AC power using a 3-phase inverter. The AC power is used to drive the induction motor, which converts electrical energy into mechanical energy to operate loads such as pumps and machinery. The MATLAB/Simulink environment is used to model and simulate the behavior of the entire system, including the PV panel, inverter, and induction motor. The simulation allows for real-time monitoring of key parameters such as power output, rotor speed, electromagnetic torque, and current in the motor’s stator and rotor. This enables performance optimization and ensures the system operates efficiently under varying irradiance and temperature conditions.

Impacts of Climate Change on the Himalayan Cryosphere: A Comprehensive Study of Snow Cover, Glacier Lakes, and Associated Geo-Hazards in Uttarakhand, India
Authors:-Samreen Azhar, Alishba, Anum Bibi, Meerab karamat, Maria, Hafiza Zoha Noor, Muhammad Arslan Aslam, Mazhar Ali, Talha, Dr Sumaira Abbas

Abstract-The Himalayas are referred to as the “Third Pole” and contain the largest concentration of glaciers outside of the Arctic and Antarctic. The glaciers are, therefore, an important source of water for these river systems including the Indus, Ganga, and Brahmaputra that support nearly 15% of India’s population. It has been observed during recent decades that the glaciers have retreated, and snow cover reduced significantly because of climate change, and this has resulted in the formation of lakes. In this study, the focus is on Uttarakhand in the Central Himalayas, assessing the relationship between climate change, snow cover, glacial lakes, and associated geo-hazards. There is a focus on key climate trends, snow cover dynamics, glacial lake expansion, and geo-hazards such as Glacier Lake Outburst Floods (GLOFs) using long-term satellite imagery, numerical models, and ground-based observations. The findings show that high-altitude areas are warming at 0.6 °C/decade, with declining rainfall trends, widespread reductions in the extent of snow cover, and deposition of potentially hazardous glacial lakes. Effective mitigation, long-term monitoring, and community-based approaches are necessary to minimize the risks to the environment and socio-economic sectors.

Unravelling the Dark Side: The Negative Impact of Social Media on Mental Health and Society
Authors:-Ishwarya B

Abstract-The Social media’s ubiquitous impact on contemporary life has unquestionably changed communication, connection, and information sharing, but underlying its glitzy exterior is a more sinister reality with significant ramifications for both societal well-being and personal mental well- being. This abstract examines how social media negatively impacts mental health, emphasizing problems like body dysmorphia, loneliness, anxiety, and depression that have become more prevalent as virtual platforms have grown in popularity. Feelings of inadequacy and loneliness are made worse by the continual push to produce idealized versions of oneself and the addictive nature of social media. Emotional well-being is further undermined by the culture of comparison, cyberbullying, and reality distortion promoted by algorithm- driven material. At the societal level, an over dependence on social media has led to a disintegration of interpersonal relationships, promoting divisiveness, echo chambers, and the dissemination of false information. Two of the main signs of this digital age are the decline in in-person interactions and people’s shortening attention spans. This abstract examines how social media is eroding the basis of genuine relationships and shared societal ideals while also enabling virtual interactions.

A Literature Survey on High Energy Physics
Authors:-Sanskriti Chanda, Dr. Subhash Chanda

Abstract-Research in modern science based on primordial particles those constitute the observable world. It is highly interesting to cover the vast field of materialistic world which lead to the scientists to investigate the building stone of the object that occurs naturally. Without proper knowledge of constituents of observable objects research on high energy physics will never yield satisfactory result. The aim of this paper is to intricate the right direction of investigation.

Implementation of NN Based MPPT Technic for Solar PV Module
Authors:-Associate Professor Mr. M. Raja Shekar, P. Narendar Reddy, K. Pramodh, Ch. Manoj Kumar

Abstract-Efficient power extraction from photovoltaic (PV) systems is critical in optimizing energy utilization for renewable applications. This project explores a Neural Network (NN)-based MPPT technique implemented in MATLAB/Simulink, designed to dynamically predict and track the maximum power point (MPP) of a solar PV system with battery storage. The NN-based MPPT is trained on a dataset encompassing various environmental conditions (irradiance, temperature, PV voltage, and current) to accurately predict the optimal duty cycle for the DC- DC converter, thereby maximizing power transfer from the PV system to the battery. A Simulink model incorporating a PV array, DC-DC converter, and the NN-based MPPT controller was developed, allowing for simulation and performance assessment under diverse scenarios. This work underscores the viability of intelligent MPPT solutions for advancing solar energy efficiency and sustainability.

Impact of Plastic on Environment
Authors:-Assistance Professor Naseem Husain, Assistance Professor Aqsa Almas Sheikh

Abstract-A serious environmental issue that has an impact on ecosystems, wildlife, and human health is plastic pollution. Since plastic production has increased to almost 368 million tons per year, plastics are found in both terrestrial and marine habitats. Although plastic materials are useful for many purposes, their durability also adds to their persistence in nature, which frequently harms the environment. Millions of marine species consume or become entangled in plastic waste, which can cause harm or even death, making marine life especially vulnerable. Microplastics, which are tiny plastic particles smaller than five millimeters, have also gotten into food chains, affected biodiversity, and endangered human health by contaminating water and shellfish. Plastic pollution has major socioeconomic repercussions since it impacts public health, tourism, and fisheries. Animal health and biodiversity are severely harmed by plastic pollution, which also poses a serious threat to ecosystems and animals. Ingestion, entanglement, or accumulation in food chains are all possible outcomes of the millions of tons of plastic waste that enter rivers, oceans, and terrestrial habitats every year. Fish, marine mammals, seabirds, and other marine species are especially at risk. Malnutrition, internal damage, and obstructions can result from consuming plastic waste, which significantly lowers survival rates. Several governmental efforts are being implemented to reduce plastic production and consumption, encourage recycling and biodegradable alternatives, and increase public knowledge of sustainable practices to reduce plastic pollution. To properly solve this complex issue, however, a comprehensive strategy including individuals, businesses, and governments is required.

DOI: 10.61137/ijsret.vol.11.issue1.146

Leveraging Data Science for Predictive Insights in Healthcare
Authors:-Maheshwar Pratap Roy, Associate Professor Dr S R Raja

Abstract-AThe rapid advancements in data science have revolutionized the healthcare industry, offering tools to enhance decision-making and optimize patient care. This paper focuses on the application of predictive analytics and machine learning models in healthcare, demonstrating how these technologies can forecast outcomes, identify patterns in patient data, and improve operational efficiency. By leveraging large-scale patient datasets, this research aligns with ethical practices and sustainability goals, ensuring equitable and impactful healthcare solutions. The results underscore the potential of data science in transforming healthcare delivery and promoting evidence-based decision-making.

Performance Analysis of Hybrid Solar Module and Wind Turbine Using Matlab
Authors:-Assistant Professor Ms. J. Malavika, Racharla Sri Harshini, Pinninti Sai Charan Reddy, Kandukuri Nithin

Abstract-The most popular renewable energy technology is Hybrid Power System consisting of wind and solar energy sources because the system is reliable and complimentary in nature. Wind / PV Hybrid system is commonly used in Distributed Generation (DG). This project proposes a new solution for improved voltage stability with quality power output. In this system voltage output from Wind Energy Conversion System(WECS) and Photo Voltaic Panels are given to separate DC-DC converters are independently controlled and connected to a common DC bus and from there it is inverted. In the proposed controller the voltage stability is obtained with a PI controller. The implementation of the proposed method is done by using MATLAB Simulink platform. The performance of the suggested coordinate control system is analyzed by comparing the computer simulation results with and with out using controllers and it shows that the proposed system is more efficient.

Simulation of Propulsion and Performance Analysis of Wap-7
Authors:-Assistant Professor Mr. D.Ramesh, D.Sai Kiran, T.S.Dinesh Karthik, E.Niharika

Abstract-This paper presents a comprehensive simulation and performance analysis of the WAP-7 electric locomotive, a cornerstone of Indian Railways’ passenger traffic. The WAP-7, with its robust design and advanced propulsion system, has been operational since its introduction in 2000, demonstrating remarkable versatility and efficiency in hauling heavy passenger trains. The WAP-7 electric locomotive has been the workhorse of Indian Railways’ passenger fleet for over two decades, with its robust design and propulsion system Utilizing MATLAB for simulation, we modeled the locomotive’s propulsion dynamics by incorporating critical parameters such as thrust, weight, drag coefficient, and braking forces.This also examines the evolution of the WAP-7’s propulsion system, the simulation provides insights into the impact of track conditions on WAP-7 performance.

Identification and Elimination of Hazards in Steel Industries by Hierarchy Control Method
Authors:-A. Tharanya, B. Balan

Abstract-The aim of this study is to Identification of hazards in various machineries in the steel industry and solution based on the hierarchy of controls. This will minimize the occupational health hazards of the workers. Hazard Identification and Risk Assessment (HIRA) is a process that involves examining what could cause harm to people in black bar to bright bar process workplace and evaluating whether the necessary precautions are in place. The goal is to ensure that no one becomes ill or gets hurt. Based on the risk assessment tool will Identify hazards, assess exposure, evaluate potential risks, and take precautions and to ensure that your workplace is safe and then decide what type of control measures shall be taken to control the employees are protected from harm by using the risk matrix to assist with the process.

DOI: 10.61137/ijsret.vol.11.issue1.147

A Study on the Effectiveness of Teaching Methods During Covid 19 in Secondary Schools of Lucknow
Authors:-Ravi Srivastava

Abstract-The COVID-19 pandemic dramatically accelerated the adoption of online learning methods in education. This abstract explores the various teaching methods employed during this period, including synchronous and asynchronous learning, flipped classrooms, and project-based learning. It also discusses the challenges and opportunities presented by these methods, such as the digital divide, student engagement, and assessment strategies. The abstract concludes by emphasizing the need for ongoing research and innovation in online teaching methods to ensure effective and equitable education for all students.

Conversational AI Chat Bot
Authors:-Mohamed Riyaz, Associate Professor Dr S R Raja

Abstract-This project aims to design and develop a conversational AI chat bot that can engage in basic conversations with users, providing helpful responses to frequently asked questions. Leveraging natural language processing (NLP) and machine learning algorithms, the chat bot will be integrated with a messaging platform to demonstrate its capabilities. The project’s objective is to create a functional chat bot that can understand user inputs, recognize intents, and generate appropriate responses.

Automated Canal Waste Collection System (ACWaCoS) for Canal Maintenance
Authors:-Nurina A. Lakian, Judy Norraine Banzon, John Arvin M. Bodlong, Gaezyll Lei C. Quong, George I. Salvador

Abstract-The study aimed to develop a prototype of an automated canal waste collection system. Specifically, it sought to determine the ultrasonic sensor’s capability to detect waste, servo motor’s spin to collect waste, system’s ability to update the serial monitor when the bin is full, average amount of time taken to complete a waste collection cycle, and average amount of waste detected and collected by the system for a certain period. The prototype used Arduino Uno R3, HC-SR04 ultrasonic sensors, MG995 servo motor, SG90 servo motor, jumper wires, breadboard, and a powerbank. The data were analyzed using frequency distribution, percentage, mean and Mann Whitney U-test. The results showed that the automated canal waste collection system prototype was 100% successful across three indicators; the prototype takes an average time of 14.6 seconds to complete a waste collection cycle; it detects an average amount of 7.50 wastes and collects an average amount of 7.20 wastes.  The amount of waste detected and collected does not significantly differ. This means that the prototype can collect a significant amount’ of detected wastes in the canal without human intervention. With these, the automated canal waste collection system has the potential in its functionality and consistency. Additionally, the device needs an IoT-based notification system for real-time monitoring.

Advancements in Plasma Physics for Space Propulsion [Core Reserach in Plasma Physics]
Authors:-Vishwanath. Barve, Pranav.D. Awate, Divyanshu.S. Yadav

Abstract-Plasma Physics, the study of charged particles and fluids interacting with electromagnetic fields, is increasingly gaining interest in the field of space propulsion. Traditional chemical rockets have limitations that restrict long-distance space travel, whereas plasma-based propulsion system promise higher efficiency and greater fuel academy. This paper explores the principles behind the plasma propulsion and examines the most recent advancement that bring this futuristic technology closer to practical applications. Additionally, it investigates the ongoing challenges, such as power requirements, fuel sources, and magnetic confinement, and how overcoming these challenges could open new frontiers for deep-space explorations.

DOI: 10.61137/ijsret.vol.11.issue1.148

Design of Battery Charging from Solar Using Buck Converters with MPPT Algorithm
Authors:-Professor Dr. S. Mani Kuchibhatla, K. Priyanka, V. Kavitha, M. Adithya

Abstract-Photovoltaic power generation system implements an effective utilization of solar energy, but has very low conversion efficiency. The major problem in solar photovoltaic system is to maintain the DC output power from the panel as constant. Irradiation and temperature are the two factors, which will change the output power of the panel. In this article it is shown that for charging lead acid batteries from solar panel, MPPT can be achieved by perturb and observe algorithm. MPPT is used in photovoltaic systems to regulate the photovoltaic array output. A buck converter is utilized as a DC-DC converter for the charge controller. It is used to match the impedance of solar panel and battery to deliver maximum power. Voltage and current from the solar panel is sensed and duty cycle of gating signal is varied accordingly by the algorithm to attain maximum power transfer. It is obtained by using MATLAB Simulink Model.

Smart Rides
Authors:-Anjali Dhunde, Srushti Anturkar, Vaishnavi Bhelkar, Vaishnavi Gudadhe

Abstract-The Smart rides is robotic car. The rise of smart transportation solutions is revolutionizing urban mobility, and the concept of “Smart Rides” is at the forefront of this transformation. Smart Rides integrate emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) with transportation systems to provide efficient, safe, and sustainable travel experiences. This review paper explores the development, implementation, and challenges of Smart Rides, focusing on key components like real-time data processing, autonomous vehicles, ride-sharing services, and predictive analytics. We analyse various smart transportation initiatives across global cities, highlighting their impact on reducing congestion, enhancing energy efficiency, and improving accessibility for diverse populations. The paper also examines the role of smart infrastructure, including sensors and communication networks, in enabling seamless mobility. Additionally, the environmental and social implications of Smart Rides are discussed, with an emphasis on sustainability and equity. Challenges related to data privacy, cybersecurity, and regulatory frameworks are also addressed, proposing solutions for overcoming these barriers. By providing a comprehensive overview of the current state of Smart Rides and future trends, this review aims to guide policymakers, engineers, and researchers in shaping the next generation of intelligent transportation systems.

DOI: 10.61137/ijsret.vol.11.issue1.149

Implementation of AC to DC Converter in Wind Power Generation Using Matlab
Authors:-Professor Dr.S Mani.Kuchibhatla, A.Sony, B. Nishith, B. Ruthwik

Abstract-Wind power generation has emerged as a crucial component of renewable energy systems, offering a sustainable and environmentally friendly alternative to fossil fuels. However, the integration of wind energy into the grid requires efficient power conversion mechanisms due to the variable nature of wind speed and the need for compatibility with existing infrastructure. A typical wind power system involves the conversion of mechanical energy into alternating current (AC) power using a generator, which is driven by wind turbines. This project focuses on the design and implementation of an AC to DC converter in wind power generation systems. The AC to DC converter plays a vital role in transforming the variable frequency AC output of wind turbines into a stable DC voltage. In this process, the generated AC power is first converted into direct current (DC) using power electronics, which enables efficient integration with batteries or facilitates smooth conversion. This conversion process is essential for stabilizing power output, minimizing losses, and ensuring the efficient transmission of energy over long distances. Advanced AC to DC converters and control systems enhance the reliability, efficiency, and scalability of wind power systems, making them a vital component in modern renewable energy infrastructures. This is implemented in the MATLAB/SIMULINLK.

Development of Center Pivot Irrigation Systems to Revolutionized Modern Agriculture Irrigation
Authors:-M.Tech. Scholar Suchita Gangele, Associate Professor Dr. Vivek Soni

Abstract-A well-designed main line is the backbone of any center pivot irrigation system. Ensuring it’s optimally sized and configured helps in achieving uniform water distribution, preventing pressure variations that could affect sprinkler performance. By using analytical methods such as hydraulic modeling, and optimization techniques, one can fine-tune pipe sizes, pump capacities, and valve configurations to ensure maximum efficiency. As you mentioned, AI-driven modeling could play a crucial role in real-time monitoring, helping predict system behavior under varying conditions. With real-time data, adjustments could be made on-the-fly to optimize water usage and reduce waste, even accounting for changing weather patterns or soil moisture levels.

Mathematical Modeling of Population Growth: A Comparative Study of Exponential Model
Authors:-Sharif Shabir

Abstract-Population growth is a multifaceted and evolving issue that has captivated the attention of demographers, ecologists, and policymakers for many years. The swift increase in the global population carries significant consequences for resource management, environmental sustainability, and socioeconomic development. This study seeks to enhance the current body of knowledge on population growth by creating and comparing mathematical models that reflect the fundamental dynamics of this phenomenon. In particular, this research examines the exponential and logistic models of population growth, which are commonly utilized in the fields of demography and ecology. The exponential model posits that population growth is influenced by a constant birth rate and death rate, whereas the logistic model considers the environmental carrying capacity and the effects of resource constraints on population growth. Employing a mix of analytical and numerical techniques, this study evaluates the advantages and drawbacks of each model in forecasting population growth across various scenarios. The findings underscore the necessity of accounting for environmental carrying capacity and resource limitations when modeling population growth, and illustrate how mathematical models can guide policy and decision making in areas such as demography, ecology, and resource management. The implications of this study are significant for our comprehension of population growth and its effects on both the environment and society. The outcomes can be leveraged to create more precise and realistic population models, which can aid in policy and decision-making at local, national, and global scales. Additionally, this research showcases the potential of mathematical modeling to deepen our understanding of intricate social and environmental issues, emphasizing the need for further exploration in this area.

DOI: 10.61137/ijsret.vol.11.issue1.150

Design of Single Phase Grid Connected Solar PV Inverter Using MATLAB
Authors:-Assistant Professor Ms. A. Sunantha, M. Ashwini, K. Geetha, B. Ajay

Abstract-This project presents the design, simulation, and performance analysis of a single-phase grid-connected solar photovoltaic (PV) inverter using MATLAB /SIMULINK. The primary objective is to develop an efficient and reliable inverter system that ensures maximum power extraction from the solar PV array and seamless integration with the grid. The main elements of the PV control structure are: a maximum power point tracker (MPPT) algorithm using the incremental conductance method: a synchronization method using the phase-locked-loop (PLL), based on delay: the input power control using the DC voltage controller and power feed-forward and the grid current controller implemented in two different ways, using the classical proportional integral (PI) and the novel proportional resonant (PR) controllers. The control strategy was tested experimentally on 2kW PV inverter.

An Investigation of Cloud Computing Security Concerns
Authors:-Research Scholar Ms. Anshul, Professor & HOD Dr.Mukesh Singla

Abstract-Distributed computing is a flexible, savvy, and proven conveyance stage for conveying corporate or purchaser IT administrations by means of the Internet. Distributed computing, then again, represents an extra danger on the grounds that basic administrations are regularly moved to an outsider, making information security and protection more hard to ensure, help with information and administration accessibility, just as show consistence. Distributed computing utilizes an assortment of advancements (SOA, virtualization, Web 2.0), and it acquires their security concerns, which we inspect here by distinguishing the most widely recognized shortcomings in these frameworks and among the most regularly referred to risks in the Cloud Computing writing and its environmental factors, just as to identify and associate shortcomings and dangers to possible cures.

Automated Dog Feeder System Using Arduino Uno for Efficient and Timely Feeding
Authors:-James Dalisay Baranggan, Haire Ato, Isaiah Smile B. Halik, Jennie Jorimocha, John Renwel Mauro, Rexel Gold D. Emuy, Judie L. Velasco

Abstract-This project aims to develop a prototype that could potentially assist dog owners in managing feeding schedules more efficiently and inclusively. The functionality of the prototype was assessed based on the aptness of the object detection using an ultrasonic sensor, the accuracy of its real-time clock, the precision of its servo motor for kibble dispensing, the audibility of its voice recorder, the visibility of the neon signage for aging dogs, the braille integration for visually impaired owners, and its overall system reliability. The automated dog feeder was built using an Arduino Uno R3, an HC-SR04 ultrasonic sensor, RTC Module DS3231, PCB matrix, jumper wires, SG90 servo motor, LCD I2C screen, 12V AC adapter, MP3 player, speaker, acrylic, PVC elbow, and some recycled materials such as wood and PVC pipe. The data analysis was based on user feedback and system performance parameters. The results indicated that the prototype accurately dispensed kibble upon detection of the dog’s presence, tracked and scheduled feeding times, and got favorable feedback on its overall functionality, usability, and inclusivity. The given results imply that the automated dog feeder has strong potential to assist diverse dog owners in maintaining regular feeding schedules, making it a more practical solution for busy homes.

DOI: 10.61137/ijsret.vol.11.issue1.151

Online Payment Fraud Detection Using Python
Authors:-Manya Rajvaidya, Hresth Narayan Mishra, Professor Shilpa Tripathi

Abstract-Online payment fraud detection is a critical area of research and development in the realm of financial security. With the rise of e-commerce and digital transactions, ensuring the integrity and safety of online payments has become paramount, This abstract explores various methodologies and techniques employed in the detection and prevention of fraud in online payment systems. The detection of online payment fraud involves the use of advanced machine learning algorithms, anomaly detection techniques, and behavioral analytics. These methods analyze transactional data in real-time to identify suspicious patterns or anomalies that deviate from normal user behavior or transaction patterns. Additionally, the integration of artificial intelligence (Al) and deep learning models has enhanced the accuracy and efficiency of fraud detection systems by enabling them to adapt and learn from new fraud patterns continuously. Moreover, the abstract discusses the challenges associated with online payment fraud detection, including the balance between security and user experience, the need for real-time decision-making, and the evolving nature of fraudulent tactics employed by cybercriminals. Furthermore, it highlights the importance of collaboration between financial institutions, payment service providers, and cybersecurity experts in combating fraud effectively. In conclusion, effective online payment fraud detection is crucial for maintaining consumer trust, safeguarding financial transactions, and mitigating potential financial losses for businesses. Continued advancements in technology and methodologies will play a pivotal role in strengthening fraud prevention strategies and adapting to emerging threats in the digital payment landscape.

DOI: 10.61137/ijsret.vol.11.issue1.152

The Biomatrix Beat Sensor: Advancement in Mr Cardiac Imaging
Authors:-Assistant Professor Mr. Bibin Joseph

Abstract-The Biomatrix Beat Sensor, developed by Siemens Healthineers, represents a significant leap forward in cardiac and respiratory MRI. By eliminating the need for traditional electrocardiogram (ECG) electrodes and respiratory belts, this contactless technology leverages electromagnetic navigation (EMN) and the Pilot Tone (PT) concept to provide real-time, artifact-free synchronization of cardiac and respiratory motion. This review explores the limitations of conventional methods, the working principles of the Biomatrix Beat Sensor, its clinical applications, and its potential to transform patient care in MRI.

DOI: 10.61137/ijsret.vol.11.issue1.153

Vivaldi Antenna Design for Cognitive Radio Communication
Authors:-Assistant Professor Dr.K.Jayanthi, B.Loganayaki

Abstract-This project presents a versatile antenna design suitable for various wireless communication platforms, including cognitive radio (CR) communication, 5G, and Wireless Local Area Network (WLAN) applications. A two-port Vivaldi antenna is designed using an FR4 substrate material with a dielectric constant of 4.4 and dimensions of 45.12 mm × 57.94 mm × 1.6 mm. This design is suitable for communication within a cognitive radio architecture. The antenna operates across multiple frequency bands, including the n79 band (4.4 GHz to 5 GHz) for 5G networks via port 1, and the 2.4 GHz band for Wi-Fi and Bluetooth communication via port 2. It achieves a return loss below -10 dB and a VSWR below 1.5 across the n79 band for 5G communication, and the 2.4 GHz band for WLAN applications.

DOI: 10.61137/ijsret.vol.11.issue1.154

Strengthening Cybersecurity in Uganda’s Electoral Commission through Multi-Factor Authentication and Single Sign-on Solutions across Organizational Applications
Authors:-Carolyn Nasimolo, Associate Professor Dr.S.R.Raja

Abstract-The increasing reliance on digital platforms for electoral processes in Uganda has brought to light significant cybersecurity issues that the Uganda Electoral Commission faces and this has made it imperative for the UEC to prioritize the strengthening of its cybersecurity. The Uganda Electoral Commission relies solely on traditional password-based authentication but hacking technologies have become more advanced and diversified. As a result, for security and authentication organizations are unable to rely on user ID and password-based authentication. (Hong, 2011). This single- factor authentication has been found to be vulnerable to attacks like malware, brute force, dictionary attacks, shoulder surfing, replay and phishing attacks etc. Much as passwords can easily be memorized and users at no cost are able to use them in their daily lives, these can be forgotten especially if the users have to log into multiple systems. (Mohammadreza Hazhirpasand Barkadehi, 2018). The UEC users log in to each of the systems independently which could lead to password fatigue. Cyberattacks can lead to unauthorized access to sensitive data, and data breaches and can therefore undermine the entire electoral process if the integrity of electoral data is compromised. This can lead to public distrust which could pose a threat to national stability. Therefore, by integrating MFA the UEC protects its sensitive electoral data and ensure secure access to its applications. Single sign-on is the ability for a user to authenticate once and access other protected resources where he has permissionwithout logging re- authentication. This system not only secures sensitive data but streamlines user experience through Single Sign-on (SSO) enabling users to log on once and gain access to multiple applications seamlessly.

The Role of AI in Enhancing Safety Standards in Autonomous Shipping: A Review of Collision Avoidance Systems
Authors:-Mohammad Anas Ahmed Rizwan, Ayaan Ali Ahmed Siddiqui

Abstract-The rise of autonomous ships allows for great opportunities in the search for greater efficiency, cost-effectiveness, and environmental sustainability in maritime operations. Safety, though, has always been a major concern, particularly with the risks of collision within increasingly congested lanes. This paper reviews the literature on how artificial intelligence is being used to transform safety standards, including, in particular, autonomous shipping, for a collision avoidance system. We examined how AI-driven methodologies such as machine learning, path-planning algorithms, predictive analytics, and decision-support systems should be integrated to advance minimal human intervention in the development of navigational decision-making processes. Sensor technologies such as radar, LiDAR, sonar, and satellite imagery are analysed for situational awareness, real-time risk assessment, and dynamic adaptation to the maritime environment. The paper discusses the use of sensor technologies, for example, radar, LiDAR, sonar, and satellite imagery, in support of situational awareness, real-time risk assessment, and dynamic adaptation to the maritime environment. Further, it shows a number of regulatory challenges, ethical considerations, and urgent international standardization issues that the development and integration of AI technologies may have for maritime industries.

DOI: 10.61137/ijsret.vol.11.issue1.155

Fuzzy Logsic Controlled System for Utilization of Renewable Energy Sources of Industry and Home Appliances
Authors:-Dr. A. R. Wadekar, Miss. Rutuja Bharat Lomate

Abstract-The per capita of power in India is insufficient compared to other developed countries in the world. Hence, the only way is the optimal utilization of available energy sources but the difference between production and consumption of electrical energy, during summer is very high, due to large utilization of cooling machines like Air conditioner, Air coolers in such case a software industry, like BPO call center or any office with large server and many systems need to have a 24 hours working Air conditioner. This leads to huge power consumption. Conservative measures need to be initiated and implement to decrease this gap to restrain this situation the concert of DSM has begun in power system planning and management. Therefore this paper included Fuzzy logic applied to Ac which results to calculate the actual hourly turn off period and reduction in energy consumption. By the optimal consumption of electrical power results increase saving by reducing the electricity bill and reduce the over load on live grid during peak hours and also calculate the cost of savings and playback period for the return of investment. In this paper, solar energy is used to run air conditioner. The cost of saving and playback period is calculated by considering only photo voltaic (PV) and photo voltaic with fuzzy controller, Results proved that usage of PV with fuzzy controller has better annual savings and lower pay back period compared with only considering PV.

DOI: 10.61137/ijsret.vol.11.issue1.156

Preparation and Characterization of Al-Cu Composite by Using Stir Casting Technique
Authors:-Assistant Professor K. K. Kishore

Abstract-Composite materials have emerged as a critical area of research and development, rapidly gaining importance as structural materials. Among polymer applications, composite materials are poised for significant advancements. Aluminum matrix composites (AMCs) are particularly favored in automotive and aerospace industries due to their exceptional mechanical properties, such as a high strength-to-weight ratio, superior wear resistance, increased stiffness, enhanced fatigue resistance, controlled thermal expansion, and stability at elevated temperatures. Stir casting is widely recognized as an efficient and cost-effective method for AMC fabrication. This study investigates the mechanical behavior of composites made from pure aluminum reinforced with copper, fabricated using the stir casting method. The composites were produced with reinforcement levels of 0%, 2%, 4%, and 6%. Results indicate that the inclusion of copper particles significantly enhanced the hardness, tensile strength, and wear resistance of the composites, though an increase in copper content resulted in decreased density. These findings highlight the potential of copper as a reinforcement material for aluminum-based metal matrix composites, offering valuable insights for diverse engineering applications.

DOI: 10.61137/ijsret.vol.11.issue1.157

Arduino-Based Rainfall and Flood Monitoring System With Real-Time Alert Notification
Authors:-Janreign G. Zamarro, Kris Martin C. Paquibot, Cristian John L. Espares, John Rey Maltizo, Judie L. Velasco

Abstract-The objective of this research is to develop an Arduino-based rainfall and flood monitoring system with real-time alert notification to address the challenges faced by the affected residents of the outlying areas of the Davao region. It focuses on using Arduino technology for the areas affected by floods that can be easily monitored with an SMS alert notification and a buzzer system. This research employed an experimental approach, starting with the design and assembly of the prototype, followed by sensor accuracy testing and data collection over multiple trials. The findings revealed that the prototype accurately measured water level according to three categories: caution, warning, and danger; this category also achieves a 100% success rate in sending SMS alerts and providing timely warnings to the buzzer during moderate and critical rainfall events. The data logged on a microSD card confirmed the system’s consistent performance in tracking environmental conditions. In conclusion, the prototype reliably shows a rainfall and flood monitoring solution that ensures real-time alerts to the affected communities, significantly contributing to disaster preparedness and response of the local communities. Some of the suggestions for future upgrades are further testing under a variety of conditions, integrating the system with IoT platforms to manage data better, programs across the community to understand and develop the most effective response mechanisms, and system expansion for a greater spatial coverage by having multiple sensors along with a monitoring station network.

“Aum: The Primordial Sound and its Resonance in Science, Spirituality, and Artificial Intelligence and Data Science”
Authors:-Associate Professor Dr. Suneel Pappala, Professor Dr K Venkata Naganjaneyulu

Abstract-The sacred syllable “Aum” (or “Om”) holds profound significance in Hinduism, Buddhism, Jainism, and other spiritual traditions. It is revered as the primordial sound of the universe, symbolizing the essence of ultimate reality, consciousness, and the interconnectedness of all existence. Explores the multifaceted dimensions of Aum, bridging its spiritual symbolism with modern scientific and technological paradigms, particularly in the realm of Artificial Intelligence (AI). By examining Aum’s representation of creation, preservation, and destruction, as well as its vibrational resonance with Earth’s natural frequencies and cosmic phenomena, Highlights the potential for harmonizing AI development with ethical principles, sustainability, and human well-being. Furthermore, it delves into the applications of Aum-inspired concepts in data science, neural networks, quantum computing, and AI-driven meditation tools, offering a holistic perspective on the convergence of ancient wisdom and cutting-edge technology.

DOI: 10.61137/ijsret.vol.11.issue1.158

Optimizing Recycling Stream Sorting Systems Using Machine Learning to Minimize Contamination
Authors:-Assistant Professor Dr. Pankaj Malik, Yashee Verma, Yashi Harne, Yuvraj Bhatnagar, Shreya Joshi

Abstract-The efficiency of recycling systems is crucial for promoting sustainability and reducing environmental impact. However, contamination in recycling streams remains a significant challenge, often leading to decreased recycling effectiveness and increased operational costs. This paper investigates the potential of machine learning (ML) to optimize sorting systems in recycling plants, aiming to minimize contamination and improve material recovery rates. We explore the application of various ML algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), and Random Forests, for automating the detection and classification of contaminants in waste streams. By leveraging sensor data, image recognition, and real-time decision-making, our approach enhances sorting accuracy, reduces human error, and supports the efficient separation of recyclable materials. Experimental results from simulations and real-world case studies demonstrate that ML-driven sorting systems can achieve higher contamination reduction and sorting efficiency compared to traditional methods. This study highlights the promising role of machine learning in transforming recycling processes and proposes future directions for integrating AI technologies in waste management to create more sustainable and effective recycling solutions.

DOI: 10.61137/ijsret.vol.11.issue1.159

Hypergraph Neural Networks for Robust Fingerprint Matching in Forensic Applications
Authors:-Assistant Professor Dr. Pankaj Malik, Lakshita Singh, Yashi Sethi, Dixika Verma, Dev Soni

Abstract-Fingerprint matching is a crucial task in forensic science, where the accurate and reliable identification of individuals is essential for criminal investigations. Traditional fingerprint matching algorithms often struggle with challenges such as occlusion, distortion, and partial prints. In this study, we propose a novel approach that leverages Hypergraph Neural Networks (HGNNs) to enhance the robustness and accuracy of fingerprint matching in forensic applications. By modeling fingerprint features as hypergraphs, we capture higher-order relationships between minutiae points and their spatial configurations, enabling more effective matching despite partial or degraded fingerprints. The HGNN framework integrates both local and global feature information, improving the system’s ability to recognize subtle and complex patterns in fingerprint data. Extensive experiments on benchmark fingerprint datasets demonstrate that our approach outperforms conventional methods in terms of matching accuracy and robustness to noise. The proposed HGNN-based model provides a promising solution for advancing forensic fingerprint identification systems, offering improved performance under challenging real-world conditions.

DOI: 10.61137/ijsret.vol.11.issue1.160

IoT and Computer Vision for Efficient Parking Management in Urban Areas: A Comprehensive Review
Authors:-Assistant Professor Mrs. Shikha Pachouly, Karan Solanki, Eeshaan Sawant, Aarya Rokade

Abstract-Urbanization and population growth have led to an exponential increase in vehicles, exacerbating parking-related challenges. Efficient parking management systems have become imperative to mitigate congestion, reduce fuel consumption, and minimize environmental impact. This paper reviews the integration of Internet of Things (IoT) technologies, computer vision, and Bluetooth Low Energy (BLE)-based indoor positioning systems for developing an efficient parking management system in urban areas. The proposed system is divided into three core modules: prediction of parking availability, real-time parking detection, and indoor navigation to guide users. This review evaluates existing approaches, highlights technological advancements, and discusses potential challenges in developing a proof of concept for the Indian context, emphasizing the cost- efficiency of the system.

DOI: 10.61137/ijsret.vol.11.issue1.161

Cyclooxygenases in Inflammatory Bowel Disease
Authors:-K. Anil Kumar

Abstract-Inflammatory Bowel Disease (IBD) is a long-term condition that presents as Ulcerative Colitis (UC), or Crohn’s Disease (CD) based on its manifestations. It is characterized by inflammation in the small intestine and colon, impacting millions of individuals globally. The development of IBD is influenced by genetic, environmental, and immunological factors. Various pro-inflammatory agents such as TNF-α, IL-1β, IL-6, IL-12, TGF-β, INF-γ, COX-2, and increased reactive oxygen species contribute to significant intestinal damage. Typical symptoms of IBD include fever, abdominal pain, vomiting, diarrhea, weight loss, blood in the stool, and an elevated risk of colon cancer. Changes in colonic motility linked to IBD can worsen discomfort and diarrhea. Prostaglandins, particularly elevated in IBD patients, may modulate these alterations. The enzyme Cyclooxygenase-2, responsible for producing prostaglandins, is targeted in IBD treatment. The role of PGE2 in the pathogenesis of IBD is intricate; while it can have anti-inflammatory effects by inhibiting pro-inflammatory cytokines, it can also act pro-inflammatory in IBD. Dysregulation of PGE2 production in IBD can lead to excess levels in inflamed gut tissue, perpetuating chronic inflammation by attracting immune cells, increasing blood vessel permeability, and causing tissue damage. The context-dependent role of PGE2 in IBD warrants further research for a comprehensive understanding. Modulating PGE2 levels or its signaling pathways may provide potential therapeutic options for managing IBD. This review specifically examines the involvement of Cyclooxygenases and coxibs in treating IBD.

DOI: 10.61137/ijsret.vol.11.issue1.162

Review on Accuracy Enhancement of Flower Classification Using Machine Learning
Authors:-Anshul Payasi, Assistant Professor Srashti Thakur

Abstract-The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) technologies has led to the development of increasingly sophisticated algorithms and models. In particular, these advancements have been pivotal in the domain of flower classification and recognition, aiming to identify and categorize the vast array of species of flowers present on our planet. This review delves into the convergence of AI and ML within the realm of flower classification, a domain that greatly benefits from the advancements in computer vision. As a sub-field of AI, computer vision plays a crucial role in extracting intricate features from floral specimens and subsequently utilizing classification algorithms to accurately label and categorize them. This literature review offers a meticulous and comprehensive exploration of the existing body of knowledge, aiming to elucidate the various methodologies and approaches employed in the taxonomic categorization of floral specimens. It encompasses an extensive survey of scholarly works, research papers, and innovative techniques that contribute to the advancement of flower identification systems. The review addresses diverse strategies, including but not limited to deep learning architectures, neural networks, feature extraction methodologies, and optimization techniques used in the classification of flowers. By synthesizing and critically analyzing the existing literature, this review aims to provide insights into the state-of-the-art techniques and emerging trends in the field of flower classification and recognition using AI and ML. This paper holds several benefits to the society such as: agriculture, environment conservation, education and tourism.

Parental Involvement and Academic Performance of Bachelor of Technology and Livelihood Education Students of the University of Science and Technology of Southern Philippines
Authors:-Ruby Pearl A. Maghanoy, Abegail B. Gaid, Mhea A. Galera, Jomar P. Flores, Jorie May Elevado

Abstract-Parental involvement is crucial in the cognitive and socioemotional development of student, and during the pandemic, parents played a vital role in shaping their student’s educational success. This study examines the relationship between parental involvement and the academic performance of Bachelor of Technology and Livelihood Education (BTLED) students at the University of Science and Technology of Southern Philippines. The study aims to determine the level of parental involvement and its correlation with the academic performance (GPA) of the students, specifically exploring the relationship between these two variables. A quantitative correlational research design was employed to assess how parental involvement correlates with academic performance. The study was conducted at the University of Science and Technology of Southern Philippines, Cagayan de Oro City, with a sample of 133 third year BTLED students. A two-part questionnaire was used to gather demographic data, parental involvement levels, and students’ GPA. Data were analyzed using descriptive statistics (mean, frequency, percentage) and Spearman’s rank correlation to determine the relationship between parental involvement and academic performance. The findings revealed that while parental involvement was generally high, the relationship with academic performance was weak and negative. Despite a high level of parental engagement, there was no significant correlation between involvement and GPA. The conclusion of the study indicates that while parental involvement positively influences student motivation, it did not significantly impact academic performance. Other factors, such as student self-motivation and program structure, likely play a more influential role. The study recommends that parents maintain active communication and structure in their student’s academic progress and that teachers and policymakers focus on strategies to enhance student self-motivation and independent learning.

Electronic Devices and Circuits: The Foundation of Modern Technology and Innovation
Authors:-Jayakarthi S R

Abstract-Electronic devices and circuits form the backbone of modern technological advancements, driving innovation across a multitude of industries. From consumer electronics such as smartphones and wearables to complex systems in aerospace, telecommunications, and healthcare, the applications of electronic circuits are vast and diverse. These circuits enable functionality, automation, and communication, making them an integral part of everyday life. This article explores the fundamentals of electronic devices, including key components like semiconductors, diodes, transistors, and capacitors, and delves into the operation and application of essential circuits such as rectifiers, amplifiers, and oscillators. It also covers digital electronics, providing insights into logic gates, flip-flops, microprocessors, and the interface between analog and digital systems. Furthermore, the paper examines the role of power electronics in energy management, renewable energy solutions, and industrial automation. Communication circuits, including RF systems, modulation techniques, and wireless communication, are also discussed, along with their crucial role in enabling modern-day connectivity. Advanced topics such as integrated circuits, VLSI, embedded systems, and emerging trends like IoT, AI, and quantum electronics are presented to highlight the trajectory of innovation in the field. Finally, this article concludes with a reflection on the impact of electronic devices and circuits on contemporary life and their future potential in shaping technological progress.

A Deep Learning Approach to Tomato Disease Classification Using a CNN-LSTM Hybrid Network
Authors:-Youssef Laatiri, Mohamed Ali Mahjoub

Abstract-Our work proposes a classification architecture based on deep learning techniques, particularly convolutional and recurrent neural networks, for the classification of tomato diseases from digital images. More specifically, the objective is to classify leaves infected by a disease using supervised learning on a pre-labeled image dataset from PlantVillage. One of the main challenges of using deep learning, however, is the need for a very large amount of annotated data, which is not always available. Therefore, the objective of our study is to develop a specific hybrid architecture, CNN-LSTM (Convolutional Neural Networks – Long Short-Term Memory), capable of leveraging small (frugal) and relatively imbalanced datasets. To assess the relevance of this approach, we propose to compare it with deep learning algorithms frequently described in the literature. The proposed model achieved better classification performance in terms of validation Accuracy of 94,16%,

DOI: 10.61137/ijsret.vol.11.issue1.163

Slope Stability Analysis of Landslide at Fudale, Gamo Zone, Ethiopia
Authors:-Amanuel Abera, Bisrat Gissila, Democracy Dila, Vasudeva Rao

Abstract-Landslides are significant natural disasters that pose threats to human life and the environment, particularly in hilly regions. This study contributes to the understanding of landslide dynamics by providing localized geotechnical data and stability analyses. After the landslide in 2023, this study looks into the geotechnical conditions and stability factors that led to landslides in the Fudale, Gamo Zone, Southern Ethiopia. The research paper aims to analyze the soil characteristics contributing to landslide occurrences and to assess slope stability using the Finite Element Method (FEM) through Plaxis 2D software. Ten soil samples were collected from various depths, and laboratory tests were conducted to determine their index and engineering properties. Test results indicate that the predominant soil types are fine-grained, comprising significant percentages of clay and silt, which are particularly susceptible to saturation and subsequent landslides. The analysis identified rainfall, slope geometry, soil permeability, and groundwater conditions as critical factors influencing slope stability. The computed factor of safety (FOS) for natural conditions was found to be 0.972, indicating an unstable slope.

Reviewing Mental Health in Perinatology, a FOGSI “Manyata” Initiative
Authors:-Kranti Kulkarni, Amit Phadnis

Abstract-Mental illnesses are a serious concern in India where every seventh person suffers from mental health problems[1,5]—with women more affected than men. While the burden of perinatal mental illnesses grows, India lacks exclusive policies to address it. Although postpartum depression or blues are restricted to the period of six weeks post-delivery, the roots of this condition are traced right from pre-pregnancy through the antenatal period to the period of one year post-delivery. We took up a study amongst postpartum mothers about their self-assessment of this condition, their awareness and their strategies to combat postpartum anxiety and reinforce the importance of psychological well-being as a part of routine assessment during antenatal period, fortified in the postpartum phase.

DOI: 10.61137/ijsret.vol.11.issue1.164

Design and Development of Tablet Making Machine Using IoT
Authors:-Associate Professor Dr.T.Sengolrajan, V.Dharshini, M.Swathi, A.Thabuna

Abstract-The pharmaceutical industry, precision and efficiency of tablet manufacturing are required to meet quality standards. In this project, the production process is being modernized by incorporating information and communication technology (IoT) into the production line. The machine performs auto-loading of all important steps including material feeding, compression and ejection and also IoT-powered sensors track parameters such as compression force, tablet weight, and humidity. In real-time, data is data is sent to a cloud-based server, enabling remote monitoring and predictive maintenance. This system guarantees of quality tablet, reduces downtime, and improves efficiency. The resulting machine is scalable and intuitive to use, making it suitable for both small- and large-scale production and brings Smart Manufacturing into the pharmaceutical industry.

DOI: 10.61137/ijsret.vol.11.issue1.165

Application for Agriculture Management
Authors:-Jasmine Saranya. P, Sabareeshwaran. S, Priya. A, Sairam. K, Dhivakar. M

Abstract-The worldwide economy relies vigorously upon horticulture, yet ordinary cultivating rehearses remember disadvantages like flightiness for the climate, ineffectual asset the board, and an absence of ongoing independent direction. The information driven brilliant cultivating application introduced in this examination advances farming administration by joining Enormous Information, Computerized reasoning (man-made intelligence), and Web of Things (IoT) sensors. The framework involves OpenCV for plant illness finding, TensorFlow and K-Closest Neighbors (KNN) for crop observing, and Choice Tree calculations for crop suggestion. Besides, LLaMA-fueled “Vigro Bot,” a chatbot, offers ranchers constant exhortation. The proposed procedure supports practical cultivating techniques, increments efficiency, and lessens asset squander.

DOI: 10.61137/ijsret.vol.11.issue1.166

Maintenance of High-Rise Buildings: Challenges, Strategies, and Future Directions
Authors:-Anuj Gautam, Assistant Professor Deepak Aggarwal, Assistant Professor Rahul Kumar

Abstract-High-rise buildings are a hallmark of modern urban development, offering solutions to space constraints and population density. However, the maintenance of these structures presents unique challenges due to their complexity, height, and the diverse systems they encompass. This paper explores the critical aspects of maintaining high-rise buildings, including structural integrity, mechanical and electrical systems, façade maintenance, and safety protocols. It also discusses the role of technology, such as Building Information Modeling (BIM) and Internet of Things (IoT), in enhancing maintenance practices. The paper concludes with recommendations for best practices and future research directions to ensure the longevity and safety of high-rise buildings.

Y2K TO IOT – Paradigm Shift in IT Industry in Last 25 Years and its Application
Authors:-Research Scholar Bhaskar Banerjee

Abstract-There was much hype and importance of the Year 2000 as known as Y2K Problem and all the legacy application Software needs to changed and incorporated with This and now we talk about IOT – Internet of Things that is Network of Physical Objects that can be connected and share data within themselves. So these changes are like Paradigm changes and it impacted a lot in our daily life, this article will talk about more About on this in details.

DOI: 10.61137/ijsret.vol.11.issue1.167

Optimization of Loading and Storage Mechanisms for Enhanced Material Handling in the Motorized Cart
Authors:-C. Gowrishankar, S.Girieshwaran, M.Keerthivarman, C.Naveen

Abstract-This project focuses on improving the cart’s utility by integrating advanced loading and storage features. A cylindrical roller mechanism is introduced to simplify the process of loading and unloading items, reducing the need for manual effort and improving efficiency. The inclusion of two spacious and organized compartments provides ample storage space, ensuring the safe and secure transportation of stationary items. Attention is given to the ergonomic design of these compartments to facilitate easy access and optimal space utilization. Additionally, this stage involves analysing the structural stability of the cart to ensure it can handle varying weights without compromising performance. By enhancing its functional capabilities, this phase ensures the cart is tailored to meet the material handling needs of a busy campus environment.

DOI: 10.61137/ijsret.vol.11.issue1.168

Development and Fabrication of Automatic Chakali Making Machine using PLC
Authors:-K. Karthik, R.Dhanush, V.Thirumalai, P.Dhayanithi

Abstract-This paper will design an Automatic Chakali Making Machine based on Programmable Logic Controller (PLC) technology to automate the traditional chakali making process. Automation is a major concern in contemporary food industries to overcome the limitations of quality control, production rate, shortage of manpower and profitability. The suggested system combines mechanical, electrical and control elements to execute primary operations such as dough extrusion, shaping, cutting and frying with high accuracy and efficiency. The process starts with a dough feeder, which transports the dough to an extruder, where PLC controls the extrusion process to deliver regular shape and size. Uniformity is achieved by a synchronized cutting system and the shaped chakalis are transported to a frying unit by a conveyor system, where PLC automation controls temperature and oil levels to deliver uniform cooking. The system also features real-time monitoring to deliver safety and efficiency. With increasing demand for food industry automation, manufacturers are continuously upgrading equipment to meet consumer demands, deliver hygiene standards and boost profitability. By minimizing manual intervention, delivering optimal utilization of ingredients and product uniformity, this automated system not only increases productivity and food safety but also enables small to medium-scale businesses to boost production on a large scale in an efficient manner. This project is intended to revolutionize chakali manufacturing by introducing automation, enhancing raw material traceability and delivering consistency in mass production.

DOI: 10.61137/ijsret.vol.11.issue1.169

Automated Hostel Management System
Authors:-Aravinth M, Nithin K

Abstract-The Hostel Management System (HMS) is an automated solution designed to streamline hostel operations, including student registration, room allocation, mess management, and attendance tracking. This system enhances efficiency, reduces manual workload, and ensures data security and accessibility. This paper presents an overview of the proposed system, its architecture, implementation, advantages, and future scope.For room allocation, Genetic Algorithm is used which allocates room to the students as per their preferences. Also, the web application consists of a generation of barcodes which can be used by the students to scan it while leaving/entering hostel premises. And the same can be used in mess also. Students will get endorsement notices in their mails which informs guardians about their ward’s presence in the hostel and their curricula using this model just in one touch. The student can raise leave requests as well as raise cleaning issues to the warden. The warden can monitor the student records and daily roll call list. The fee details and the due of the student can also be verified using this QR database management and inquiry method.

DOI: 10.61137/ijsret.vol.11.issue1.170

Innovative Drip Irrigation Techniques for Sustainable Agriculture
Authors:-Assistant Professor P. Sudheer Kumar, T. Chandrika, D. Sivanjaneyalu, M.Sumanth Reddy, Y.Venkata Suchithra, M.Deepak

Abstract-Drip irrigation is an advanced water delivery system designed to provide efficient irrigation by delivering water directly to the root zone of plants. 1This method involves a network of pipes, tubing, and emitters, ensuring that water is distributed evenly and precisely, minimizing water wastage. Compared to traditional irrigation methods, drip irrigation significantly reduces water consumption by preventing evaporation and runoff. Additionally, it promotes healthier plant growth by providing consistent moisture levels and reducing the risk of overwatering. This system is particularly beneficial for water-scarce regions and sustainable agriculture, offering advantages such as improved crop yields, reduced weed growth, and the efficient use of fertilizers. With its ability to optimize water usage and promote environmental sustainability, drip irrigation is a highly effective and cost-efficient solution for modern farming and gardening practices.

DOI: 10.61137/ijsret.vol.11.issue1.195

A Regression Model to Analyze the Impact of Macroeconomic Indicators on Bitcoin, Gold and the S&P500 Index
Authors:-Mayukh Ghosh

Abstract-This study examines the impact of key macroeconomic indicators—Consumer Price Index for All Urban Consumers (CPI-U) and Federal Reserve Rate (Fed Rate)—on the performance of Bitcoin (BTC), Gold (XAUUSD), and the S&P500. Through regression analysis, the research provides a comparative perspective on traditional and emerging asset classes (Wu, 2022). The findings indicate that inflation plays a dominant role in influencing asset prices, with the strongest effects observed in equities and Gold. Bitcoin, despite its perception as a digital hedge, exhibits moderate sensitivity to inflation alongside high volatility driven by speculative and external factors. The Fed Rate has a weaker influence on all three assets, particularly Bitcoin, suggesting that monetary policy alone does not dictate cryptocurrency price movements (Pinchuk, 2021). The study underscores the importance of inflation in shaping investment strategies, especially for traditional assets, while highlighting Bitcoin’s speculative nature. The research also introduces a model framework that can be adapted to assess various asset classes against different macroeconomic indicators. Future work should explore advanced analytical techniques and a broader set of variables to enhance market insights.

DOI: 10.61137/ijsret.vol.11.issue1.171

Green Solutions for Waste Water Management
Authors:-Assistant Professor P. Venkata Nagaraju, N Pavankumar Reddy, M Sathish, S Haseena Begum Munni , T Harinath

Abstract-Wastewater treatment is a crucial process for managing and purifying water contaminated by domestic, industrial, and commercial activities before it is safely discharged or reused. The treatment process involves multiple stages, including preliminary, primary, secondary, and tertiary treatment, each designed to remove solids, organic matter, harmful microorganisms, and chemical pollutants. 1Advanced techniques such as biological treatment, filtration, and disinfection further enhance water quality. Proper sludge management ensures the safe disposal or reuse of byproducts. Wastewater treatment plays a vital role in protecting public health, preserving ecosystems, and promoting sustainable water use. With growing concerns about water scarcity and pollution, innovative and efficient wastewater treatment technologies are increasingly essential for environmental sustainability and resource conservation.

DOI: 10.61137/ijsret.vol.11.issue1.192

Carbon Dioxide Utilization in Organic Synthesis
Authors:-Associate Professor Mr A Rajasekar Reddy

Abstract-Carbon dioxide (CO₂) is a sustainable, abundant, and non-toxic carbon feedstock, offering immense potential in organic synthesis. However, its thermodynamic stability and low reactivity necessitate innovative activation strategies. Recent advances have demonstrated CO₂’s utility in various transformations, including carboxylation, cycloaddition, hydrogenation, and carbonylation reactions. These processes enable the production of valuable compounds such as carboxylic acids, carbonates, carbamates, and heterocycles, often using transition metal catalysts, organocatalysts, or electrochemical methods. 1Catalytic systems such as metal complexes, N-heterocyclic carbenes, and metal-organic frameworks have been instrumental in overcoming the inherent challenges of CO₂ activation. Additionally, emerging approaches like electrocatalysis and photocatalysis provide sustainable pathways for CO₂ reduction and incorporation into organic frameworks. By converting a greenhouse gas into valuable products, CO₂ utilization not only addresses environmental concerns but also advances green chemistry. Ongoing efforts focus on improving reaction efficiency, selectivity, and scalability, paving the way for industrial applications and contributing to a circular carbon economy.

DOI: 10.61137/ijsret.vol.11.issue1.193

Calculating Rain Water Harvesting for a Building
Authors:-Research Scholar C.Chinna Suresh Babu, Professor C Rama Chandrudu, C.Shashidar B. Vasantha, K.Nagendra, T.Venkata Suresh, A.Gurappa

Abstract-Rainwater harvesting (RWH) is a sustainable method of collecting and storing rainwater for various uses, reducing dependence on conventional water sources. This paper discusses the potential for rainwater harvesting in buildings by calculating the amount of water that can be collected based on rooftop area, annual rainfall, and runoff efficiency. 1 The standard formula for estimating rainwater harvesting potential is outlined, considering key factors such as surface type and climatic conditions. Additionally, the benefits of RWH—including groundwater recharge, flood prevention, and cost savings—are highlighted. The study emphasizes the importance of designing efficient storage and filtration systems to maximize usability. Implementing RWH in urban and rural settings can contribute to water conservation and sustainability, making it a crucial component of modern water management strategies.

DOI: 10.61137/ijsret.vol.11.issue1.194

Empowering Marginalized Voices: The Influence of Muslim-Run Media Outlets in Shaping India’s Digital Public Sphere
Authors:-Anam Mobin, Professor Mohammad Shahid

Abstract-Muslim-run media outlets influence India’s online conversation by highlighting underrepresented voices, fighting false information, and encouraging open discussions. In India, the mainstream media is often accused of misleading or ignoring Muslim viewpoints. As a result, independent digital platforms created by and for Muslims have become important for sharing their stories, supporting their rights, and shaping their narratives. Independent internet platforms like TwoCircles.net and Maktoob Media are crucial spaces for representation, advocacy, and grassroots storytelling in India, while mainstream media have been condemned for reinforcing negative stereotypes and marginalizing Muslim voices. This study emphasizes the importance of editorial independence in unbiased reporting and helps us understand how independent Muslim media work in India’s changing digital ecosystem and how they democratize media representation and promote public equity.

DOI: 10.61137/ijsret.vol.11.issue1.172

Enhancing Collaborative Deep Learning with Swarm Intelligence and Federated Optimization
Authors:-Assistant Professor Dr. G. Babu, Sunil Kumar Nagar

Abstract-In the era of advanced artificial intelligence and machine learning, collaborative deep learning has emerged as a powerful approach to leverage distributed data and computational resources. However, a significant challenge that persists is ensuring the generalizability of models developed in collaborative environments. This project addresses the generalizability challenge in collaborative deep learning by proposing a novel framework that integrates advanced techniques in model training and validation. Deep learning models typically require data to be collected at a centralized location to learn effective representations, which introduces several issues such as communication costs and risks to data privacy. These issues are particularly critical in the case of clinical data, where patient privacy is paramount. In such contexts, distributed machine learning offers a viable solution where various data-holding sites can locally train a mutually agreed-upon model and share their knowledge. Federated learning (FL) facilitates this process using a client-server framework. Clients in the FL environment are independent small edge devices that retain their data locally, while the server acts as a central site that aggregates and distributes the knowledge learned by each client to others. The server receives locally trained weights from all participating clients, aggregates them, and then transfers the aggregated weights back to all clients before the next training round begins. This iterative process continues until the server achieves the desired accuracy. FL thus enables multiple clients to collaboratively train a shared global model without sharing their local data, preserving data privacy and addressing issues of limited data availability. However, FL faces challenges such as high communication costs for transferring weights, statistical data heterogeneity among clients, and the single point of failure of the server. Client heterogeneity arises mainly due to differences in data distribution among clients and their respective computational power. This project targets statistical data heterogeneity in the FL environment and proposes a simple yet effective attention-based approach to address this issue. Specifically, in the proposed setting, each client sends a mean representation to the centralized server along with the trained model’s weights. A similarity matrix is computed based on the similarity score of each client’s mean representation from every other participating client. This similarity matrix determines the weightage of each client’s model in the aggregated model. The centralized server computes the attention vector for each client using this similarity matrix and then broadcasts this attention vector to all clients. This attention mechanism is implemented both on the centralized server and the participating clients. We consider FedAvg, FedProx, and FedMomentum as baselines for comparison, and our proposed approach outperforms all of them. For statistical heterogeneity, we perform extensive experiments on FOOD101 and CIFAR10, demonstrating that our approachperforms well even with highly skewed data. To address the single point of failure issue in FL, we propose an efficient version of swarm learning. We demonstrate the effectiveness of context- aware swarm learning through experiments on the HAM10000 and ISIC Skin Lesion 2019 datasets. Additionally, to mitigate the high communication costs in FL, we propose BAFL (Federated Learning for Base Ablation), which introduces a fine-tuning approach to leverage the feature extraction ability of layers at different depths of deep neural networks. We evaluate the proposed approach using VGG-16 and ResNet-50 models on datasets including WBC, FOOD-101, and CIFAR-10, achieving up to two orders of magnitude reduction in total communication cost compared to conventional federated learning.

DOI: 10.61137/ijsret.vol.11.issue1.173

Detection of Ransomware Using Hardware-Based Honeypot Files with SMB Traps
Authors:-Abhirup Guha

Abstract-Ransomware attacks have escalated, posing significant threats to organizations by encrypting critical data and demanding ransoms. Traditional security measures often fall short against sophisticated ransomware variants. This paper explores the deployment of hardware-based honeypot files utilizing Server Message Block (SMB) traps as a proactive defense mechanism. By integrating deceptive SMB shares at the hardware level, organizations can detect, analyze, and mitigate ransomware activities more effectively.

DOI: 10.61137/ijsret.vol.11.issue1.174

Design and Development of Drone for Spraying Pesticides in Agricultural Lands
Authors:-Assistant Professor Siva Jothi S, Richard Lloid P, Suvarnalakshmi V, Ganesamoorthy S

Abstract-The design and development of a drone for spraying pesticides on agricultural lands have been described in this paper. The drone developed is a quadcopter integrated with a spraying mechanism. A quadcopter can be described as a mechanical device that can hover using propellers fitted into it is four arms. Hovering is achieved using one set of clockwise spinning propellers and another set of counter- clockwise spinning propellers that generate the thrust required to facilitate the taking off and hovering process. The agricultural industry contributes heavily to India’s GDP, thus making it one of the chief sources of revenue. It is the foundation of India’s economy and contributes to approximately one-fourth of its gross domestic product. It is inevitable that fertilizers and pesticides will be used to increase crop yields. However, few health-related problems can arise due to prolonged exposure to such chemicals during manual spraying. A few examples include mild skin irritation to congenital disabilities, changes in genetics, falling into a coma, or even death in severe cases. Drones have been used extensively in agriculture over the past few years. This paper describes the components required for the successful design and development of a quadcopter that can be utilized for spraying fertilizer on agricultural lands. The quadcopter is equipped with a container carrying a Direct Current water pump fitted with a pipe and nozzle arrangement. The liquid passes and is controlled using the instructions that the user provides the controller.

DOI: 10.61137/ijsret.vol.11.issue1.175

Artificial Intelligence in Business: From Research and Innovation to Market Deployment
Authors:-Associate Professor Dr Akhilesh Saini

Abstract-This paper examines the pivotal role of artificial intelligence (AI) in transforming business practices, tracing its evolution from foundational research and innovation to practical market deployment. As AI technologies rapidly advance, they are reshaping industries by enhancing productivity, enabling data-driven decision-making, and fostering the development of intelligent products and services. The study highlights the dual nature of AI’s impact, addressing both the opportunities it presents for economic growth and innovation, as well as the challenges and ethical considerations it raises for various stakeholders, including businesses, consumers, and policymakers. Through an analysis of key research breakthroughs and their implications for entrepreneurial activities, the paper identifies trends in AI start-ups and their contributions to the market. Ultimately, this research aims to provide a comprehensive understanding of how AI is not only revolutionizing business operations but also influencing the broader economic landscape, thereby offering valuable insights for practitioners and researchers alike. In recent years, the emergence of a multitude of intelligent products and services has sparked widespread interest in artificial intelligence (AI) and its commercial viability, raising critical questions about whether this trend represents genuine transformation or mere hype. This paper investigates the extensive implications of AI, exploring both its positive and negative impacts on governments, communities, companies, and individuals. By examining the journey of AI from research and innovation to market deployment, the study highlights significant academic achievements and innovations in the field, as well as their influence on entrepreneurial activities and the global market landscape. Additionally, the paper identifies key factors driving the advancement of AI technologies. To further explore entrepreneurial engagement with AI, two lists of the top 100 AI start-ups are analyzed. The findings aim to enhance understanding of AI innovations and their broader impact on businesses and society, ultimately providing insights into how AI can transform business operations and contribute to the global economy.

DOI: 10.61137/ijsret.vol.11.issue1.176

Vishwanath’s Law of Dynamic Mass-Energy Redistribution
Authors:-Vishwanath G.Barve

Abstract-This paper introduces Vishwanath’s Law of Dynamic Mass-Energy Redistribution, which proposes a novel framework to understand the adaptive behavior of mass in non-inertial reference frames. Traditional mass-energy equivalence fails to incorporate mass fluctuations due to high internal energy shifts and entropy variations. Using advanced tensor calculus and Lagrangian mechanics, we derive a modified mass-energy relationship. Applications in missile propulsion, quantum mechanics, and astrophysical anomalies are explored, providing new insights into mass-energy interactions.

DOI: 10.61137/ijsret.vol.11.issue1.177

Internship App for College
Authors:-Naman Singh, Tejas Ambekar

Abstract-The growing demand for internships among students has highlighted the need for an effective platform that connects students and teachers in a more organized manner. Currently, many colleges rely on WhatsApp groups to share internship opportunities, which often leads to confusion, missed messages, and a cluttered experience. This research proposes the development of an Internship Portal application designed to address these challenges by providing a dedicated space for students and teachers to manage internship postings and applications efficiently. The proposed Internship Portal aims to create a user-friendly application that allows students to browse available internships, apply directly, and keep track of their applications. Teachers will have the ability to post internship opportunities tailored to their students’ courses, ensuring that all relevant information is shared in an easily accessible format. By consolidating internship postings in one platform, we hope to eliminate the chaos of multiple messages in WhatsApp groups and create a streamlined process for both students and teachers. Another important aspect of this project is the focus on user experience. The application will feature a simple and intuitive interface that is easy to navigate, ensuring that both students and teachers can use the platform without difficulty. This is particularly important for students who may not be technologically savvy and need a straightforward solution to access internship information. By prioritizing user experience, we aim to encourage more students to engage with the platform and take advantage of the internship opportunities available to them.

Piezo Energy Harvesting Footstep Powered Electricity Genartion
Prof. C.K. Bakshi, Mr. Omkar R. Gaikwad, Mr. Atharva S. Alhate, Mrs. Siddhika S. Wagh, Mrs. Ritika R. TaydeAuthors:-Naman Singh, Tejas Ambekar

Abstract-Electricity usage is expanding at an exponential rate. This research recommends making use of human locomotion energy, which, despite being extractable, is largely wasted. This research presents an energy storage concept that employs human movement, skipping, and running as energy. The piezoelectric sensors are used in this innovative footstep power production system. The piezo sensors are positioned below the platform to generate a voltage from footstep. The sensors are arranged in such a way that maximum output voltage is generated, which is then sent to our monitoring circuitry. This energy is then stored in the batteries and can be used whenever it is convenient. A model like this is near suitable for India, which has a large pedestrian people. This method of generating charge and storing it for later use encourages an environmentally responsible approach to energy creation and the development of clean green energy.

Comparative Analysis of New VS Old Tax Regime
Authors:-Dr. Batani Raghavendra Rao, Rupesh M, Samruddhi Pattanashetti, Sanjay M, Shreevalli K M, Saravana Reddy Kunam, Shravana S Khodanpur, Shubham Pain, Simran Sharma

Abstract-This research paper conducts a comparative analysis of the old and new tax regimes for the financial year 2023-2024 in order to evaluate their impact on individual taxpayers, businesses, and government revenue. The study compares the main differences in tax slabs, deductions, and overall tax burden at different income levels. Further, it covers the compliance burden and administrative efficiency of both regimes, analysing how they affect taxpayer behaviour and economic decision making. This research will apply a combination of both qualitative and quantitative methodologies. The financial impact of each regime for different taxpayer groups is analysed by comparing tax liabilities under different income brackets, showing which regime provides more benefits for each group of taxpayers. Interviews and surveys with tax professionals and salaried people reveal information related to preferences, challenges, and practical implications associated with each regime. The study further analyses broader macroeconomic indicators, such as revenue generation, disposable income, and investment trends, in order to find out the broader economic implications of the tax reforms. The research results find that the old tax regime remains beneficial for those with significant investments that result in savings under the deduction sections: 80C, 80D, and HRA. The old regime is likable by high-income earners and those with complicated financial structures because it saves on taxes. On the other hand, middle-income earners and those without substantial investments prefer the new tax regime since it reduces complexity in tax filing and compliance. The new regime may also involve an increase in disposable income, which may fire up consumer spending, although it is less clear what the effect will be on long-term savings and investment patterns. This, therefore, implies that both regimes have their respective advantages and limitations, and the optimal choice would depend on an individual’s financial situation and tax saving strategy. Policymakers must continue to refine tax structures for better revenue generation and taxpayer convenience, ensuring economic stability. This detailed comparative assessment will help taxpayers make informed financial decisions and contribute to the ongoing discourse on tax policy improvements in India.

DOI: 10.61137/ijsret.vol.11.issue1.178

A Scientometric Analysis of Hemophilia Research: Evaluating the Current Status
Authors:-Dr. A. Vellaichamy, E. Amsan

Abstract-In the present study shows that global hemophilia research from 2018 to 2024, examining publication trends, authorship, collaboration, and citation impact. The study analysed that a steadily increase in research output, with 2024 being the most productive year (1,333 publications, 15.61%), followed by 2023 (1,279 publications, 14.98%). Articles (5,537 records) and reviews (1,339 records) are the dominant communication channels, while collaborative research is prevalent, with most papers having more than six authors (3,021). Most productive authors are Hermans, C. (165 papers) and Peyvandi, F. (146 papers), with European institutions leading contributions, alongside notable input from Japan. The United States is the leading contributor (2,414 papers, 28.27%), followed by the United Kingdom (9.72%) and Italy (9.67%), with China, Japan, and India also playing significant roles. Highly cited studies focus on immune checkpoint inhibitors, gene therapy, and RNA-based therapeutics, with the most cited article by Brahmer, Julie R., et al. (2018) having 2,761 citations. The study highlights the increasing global collaboration and evolving research priorities in hemophilia, emphasizing innovations in gene therapy and personalized medicine.

The Role of Authenticity in Consumer Purchase Decisions
Authors:-Vicky Prajapati, Neeraj Kumar Sharma

Abstract-Authenticity plays a crucial role in shaping consumer purchase decisions, influencing brand perception, trust, and overall satisfaction. In an era where consumers have access to vast information and numerous product choices, authenticity has emerged as a key differentiator for brands. This study explores the impact of authenticity on consumer behaviour, examining factors such as brand transparency, product originality, ethical practices, and emotional connection. By analysing consumer preferences and decision-making patterns, the research highlights how perceived authenticity fosters brand loyalty and drives purchasing intent. The findings suggest that businesses that prioritize authenticity in their branding, communication, and product offerings gain a competitive edge in the market. This study provides valuable insights for marketers and brand strategists aiming to build long-term consumer relationships based on trust and credibility.

DOI: 10.61137/ijsret.vol.11.issue1.179

Enhancing High-Performance Computing with Optimized Low-Power VLSI Circuits
Authors:-Arti Sahu, Professor Saima Khan, Professor Sandip Nemade, Dr. Divya jain

Abstract-The increasing demand for energy-efficient computing systems has propelled the research and development of low-power Very Large Scale Integration (VLSI) circuits, particularly in high-performance computing (HPC) applications. This paper explores a variety of design and optimization techniques aimed at minimizing power dissipation while maintaining high performance levels. We analyze key methodologies including Dynamic Voltage and Frequency Scaling (DVFS), multi-threshold voltage design, and power gating strategies that contribute to significant energy savings in VLSI architectures. The integration of these low-power techniques is crucial in responding to the rigorous challenges posed by growing transistor densities and the resultant heat dissipation concerns in modern computing systems. Furthermore, this research addresses the intersection of circuit-level optimizations with architectural design choices, offering insights into effective power management across various operational states. Through a comprehensive review of recent advances and case studies in low-power VLSI design, this paper underscores the critical importance of these innovations in meeting the evolving energy efficiency requirements of high-performance computing platforms, ensuring sustainability and cost-effectiveness in future technological landscapes.

Intelligent Pattern Based Communication Management Networking
Authors:-Nikhil A Rawool

Abstract-Network connection for systems with purpose of exchanging with collection of Mobile communication system with ground operating surface for allowing mobile devices with telecommunication network for transmitting data with use of underground devices While the research paper focuses on Self – evolving method for featuring Time – series analysis with use of magnetic field of lines for self-adaptive signaling recombining and readvancing patterns for distribution and maintaining automated Rekeying Technology for Wireless Communication system . Intelligent Ecosystem Networking with the use of Cloud or Hybrid Cloud environments with the future of wireless communication network involves solutions for users, applications and devices involving identity management with securing adaptive access, identifying governance and user experience with use of self – evolving patterns for allowing mobile communication while transmitting network through all medium. The Main objective of the paper is Readvancing patterns for self-adaptive signaling following approach for distribution patterns.

DOI: 10.61137/ijsret.vol.11.issue1.180

Automated Fish Feeding System for Nursing Ponds
Authors:-Christine Mae P. Niez, Lord Joseph T. Araneta, Ronden A. Donato, Jasson N. Collantes, Jacquelyn R. Mozo, Romel M. Sapitanan

Abstract-Feeding the fish at a very specified schedule has proven to be a really complicated task for the aquaculture farmers. This study aimed to develop an automated fish feeding system for nursing ponds. A functionality test was used in the conduct of the study. The automated fish feeding system used Arduino IDE to code the features such as delivering feeds, time interval, and the servo motors spin. Based on the results of the study, the automated fish feeding system had successfully passed the overall functionality test on its feeding mechanism in terms of delivering feeds, time interval, the servo motors spin and the system programming. Furthermore, the result showed that the actual masses of feeds dispensed on each aquarium had no significant difference compared to masses of feeds set on the device. The automated fish feeding system has the potential to greatly benefit aquaculture farmers by ensuring consistent and precise feeding schedules, reducing human intervention, optimizing feed usage, and promoting healthier fish growth, ultimately improving productivity and profitability.

Facial Emotion Detection Using Machine Leaning
Authors:-Sachin Mhaske, Vighnesh Thigale

Abstract-Facial emotion detection is an emerging field that leverages artificial intelligence (AI), machine learning, and computer vision to recognize and interpret human emotions based on facial expressions. This study explores the effectiveness of deep learning models, such as Convolutional Neural Networks (CNNs), in identifying emotions like happiness, sadness, anger, fear, surprise, and neutrality. The system’s applications span healthcare, marketing, security, and human-computer interaction. However, challenges such as cultural variability in expressions, mixed emotions, and privacy concerns necessitate further improvements. This research aims to enhance facial emotion detection by addressing accuracy, ethical considerations, and real-world implementation. The purpose of this is to make a study on recent work on automatic facial emotion recognition In deep learning. There are many different techniques for recognizing human emotion.

Effect of Variation in Gas Composition on the Growth Density and Size of the Carbon Nanostructures Deposited by RF-PECVD
Authors:-Dr. B. Purna Chandra Rao, R. Hari Babu, Dr.K Subbarao, V.Durga Prasadu, Dr. A. R. K. Murthy

Abstract-A focus on synthesizing different types of two-dimensional Carbon nanostructures using Methane and Argon without catalyst has been conducted in Radio Frequency Plasma Enhanced Chemical Vapor Deposition. This study reports the variation in growth density, size and morphological characteristics of Carbon nanostructures by varying the gas compositions. Field Emission Scanning Electron Microcopy (FE-SEM) and Atomic Force Microscopy (AFM) studies shows the high percentage of Methane gas in the composition is directly proportional to the density and inversely proportional to the size of the nanostructure. We report that the concentration of Methane usually offers more carbon species or driving force for the growth of the two-dimensional carbon nanostructures. This process enables to increase the density and decreases the size of the nanostructures. The results of Raman spectroscopy show the typical carbon features at 1321,1571 and 2639cm-1 respectively. The intensity ratio of these two peaks ID/IG increases with increase in the Methane gas percentage in the composition indicates the nanocrystalline nature of two-dimensional carbon nanostructures with a large number of defects.

DOI: 10.61137/ijsret.vol.11.issue1.181

Development of a Framework for Measurement of Municipal Construction Project Performance in Delta State, Nigeria
Authors:-Nancy Rosemary Amede, Professor Uche Ajator

Abstract-Performance measurement is essential for improving decision-making, aligning project outcomes with stakeholder’s objectives, and driving future improvements. In the context of municipal construction projects, construction sector in Nigeria in general, and Delta State in particular faces persistent challenges, including with delays, cost overruns, and failure in operational performance and stakeholders dissatisfaction these challenges underscore the need for comprehensive system that incorporates Critical Success Factors (CSF), Performance Measures (PMs), and Success Metrics to ensure project efficiency and stakeholder’s satisfaction. Hence, the goal of this research is to develop framework tailored to Delta State municipal construction sector. It identifies challenges; explore best practices from developed countries, and leverages input from key stakeholders. Data collected through surveys and analyzed using statistical tools, including mean analysis and ANOVA, informed the framework’s development. Findings reveal that significant gaps in performance measurement practices in the study area, highlighting the absence of a holistic approach to managing municipal construction projects. The proposed framework will address these gaps by offering a structured, stakeholder-focused approach to project evaluation. This research contributes to improving the effectiveness of municipal projects and offers a foundation for future studies on performance measurements in developing countries.

Mineral Mapping of Moon Using Chandrayaan-2: Review Paper
Authors:-Saurabh S Joshi, Md. Zeeshan R, Ganesh B Dongre, Shashikant R Dikle

Abstract-Lunar mineral mapping is crucial for understanding the Moon’s formation, geological evolution, and resource potential. This review paper examines the significant contributions of the Chandrayaan-2 mission to this field. Prior to Chandrayaan-2, missions like Clementine and Chandrayaan-1 provided foundational mineralogical data, revealing the Moon’s diverse composition dominated by minerals such as plagioclase feldspar, pyroxenes, and olivine, with regional variations reflecting magmatic differentiation and impact processes. Chandrayaan-2, equipped with advanced instruments including the Imaging Infrared Spectrometer (IIRS), significantly enhanced lunar mineral mapping capabilities. This review synthesizes key findings from Chandrayaan-2, highlighting its high-resolution spectral and spatial data that have refined our understanding of mineral distribution across the lunar surface. Methodologies employed encompass sophisticated spectral unmixing and analysis techniques applied to IIRS data, enabling the identification and mapping of subtle mineralogical variations, including hydration features and the composition of lunar geological units. The improved mineral maps generated by Chandrayaan-2 have profound implications for future lunar exploration, resource utilization strategies, and a more nuanced comprehension of planetary formation processes within our solar system. This paper underscores the enduring legacy of Chandrayaan- 2 in advancing lunar science.

DOI: 10.61137/ijsret.vol.11.issue1.182

Steganography
Authors:-Prem Balani, Tanmay Ambekar

Abstract-Steganography is a technique of hiding secret information within an innocuous carrier such as text, image, audio or video. Its purpose is to conceal the existence of the message and to prevent detection by an eavesdropper. Steganography has gained popularity as a means of secure communication due to its ability to hide the message in plain sight. This paper provides an overview of the concept of steganography, its history, and its applications. It also discusses different types of steganographic techniques, such as least significant bit (LSB) embedding and transform domain techniques. The paper then examines the importance and limitations of steganography, such as the security and legal compliance, and the vulnerability to attacks. Finally, the paper explores some of the emerging trends in steganography research, the difference between steganography and cryptography and real-life examples. Overall, this paper provides a comprehensive understanding of steganography, its applications, advantages, and future directions.

The Use Social Media Platforms and Learners’ Classroom Engagement
Authors:-Aberia, Charllote, Beros, Lalaine Cyril Mae

Abstract-communication, collaboration, and the sharing of ideas. It can also help students develop critical thinking and digital literacy skills. The prevalence of social media in modern society has raised questions about its implications for educational contexts. This study aims to investigate whether social media usage contributes positively to classroom engagement or serves as a distraction. The primary objectives are to analyze patterns of usage, identify benefits and drawbacks, and propose methods for effective integration. A quantitative-descriptive research design was utilized to investigate social media engagement levels and academic performance among elementary pupils at Eugenio A. Abunda Sr. Elementary School during the 2024-2025 school year. This research tries to conduct an investigation by paying attention to and considering the Profile of Respondents According to Reading Level which consists of Reading Level which consists of Frustration, Instructional, Independent. In this study, the data was divided into various categories of respondents. This aligns with Sivakumar (2020), who characterized social media engagement as an obsessive fixation with social media and an insatiable desire to access or utilize it. The notion of social media engagement as a condition of reliance leading to excessive use and difficulties in abstaining resonates with the observed moderate cognitive engagement among respondents.

A Study of Anatomy of Breast Cancer Detection and Diagnosis Using a Support Vector Machine and a Convolutional Network
Authors:-Research Scholar Ishu Goel, Associate Professor Dr Ravindra Kumar Vishwakarma

Abstract-This study investigates the effectiveness of integrating Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) for the diagnosis of breast cancer through mammographic image analysis. Recognizing breast cancer as a leading cause of mortality among women, early and accurate detection is crucial for better treatment outcomes. The research focuses on the development of a hybrid model that leverages the strengths of both SVM for classification and CNN for feature extraction. The model is tested on a comprehensive dataset of mammographic images, employing advanced preprocessing techniques to enhance image quality and reduce noise. It meticulously compares the performance metrics, such as accuracy, sensitivity, and specificity, of the proposed hybrid approach against traditional methods. Initial findings indicate the hybrid model outperforms individual classifiers in terms of diagnostic accuracy, showcasing its potential application in clinical settings for effective breast cancer screening. This research not only contributes to understanding the anatomical nuances in imaging but also emphasizes the importance of machine learning in medical diagnostics, paving the way for enhanced early detection strategies.

A Study on the Factors Affecting Quality of Work Life of Women Employees in the Education Sector
Authors:-Assistant Professor Priyanka Tripathi, Assistant Professor Sushma Singh

Abstract-The quality of work life (QWL) is a critical aspect of employee satisfaction, productivity, and overall well-being. In the education sector, where women constitute a significant portion of the workforce, understanding the factors that influence their QWL is essential for fostering a conductive work environment. This research paper aims to explore the various factors affecting the QWL of women employees in the education sector, including work- life balance, job satisfaction, organizational support, career development opportunities, and workplace culture. The study employs a mixed- methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data. The findings reveal that work-life balance, organizational support, and career development opportunities are the most significant factors influencing QWL. The paper concludes with recommendations for educational institutions to enhance the QWL of women employees, thereby improving their overall job satisfaction and productivity.

Industrial Pollution: A Global Challenge
Authors:-Himanshu Pawar, Sanskriti Singh

Abstract-Industrial pollution is a major global issue affecting human health, the environment, and economic development. This paper explores the pervasive nature of industrial pollution, particularly its impact on developing nations, and presents an analysis of its sources, types, health, and environmental consequences. The paper highlights the significant challenges posed by industrial pollutants, such as heavy metals, particulate matter, and chemical discharges, that affect air, water, and soil quality. It further emphasizes the importance of technological innovations, stringent regulations, and international cooperation in mitigating industrial pollution. Ultimately, a transition toward sustainable production and consumption is crucial to addressing this global crisis and ensuring a more equitable future for all nations.

Student Management System
Authors:-Sairaj Pabale, Aniket Yamgar

Abstract-The Student Management System (SMS) is a revolutionary web-based system aimed at simplifying the management of student-related information with unparalleled ease. As a central repository for schools, SMS makes it easy to manage student records, attendance, grades, and academic progress.With the frontend designed based on HTML, CSS, and JavaScript, the system provides an interactive and responsive user interface that fascinates its users. The strong backend, developed in Java, proficiently handles business logic and handles API requests, while MySQL provides secure and organized data storage.This study explores the system’s architecture, development process, security measures, and performance criteria. Through extensive testing, the system has established its outstanding capability to handle vast student datasets with the utmost level of security and scalability. Amidst an environment where efficiency and reliability are most valued, the SMS is an anchor of contemporary educational management.

Exploring Clustering Techniques: Hierarchical VS. K-Means in Unsupervised Learning
Authors:-Research Scholar G.DIVYA, Associate Professor Dr.V.Maniraj

Abstract-Unsupervised learning algorithms play a crucial role in discovering hidden patterns and structures within the data This paper delves into two prominent clustering approaches K-means and Hierarchical clustering. Evaluating their performance, strengths and weakness and their methodology and their process. The results highlight the strength of Hierarchical clustering in identifying complex clusters and k-means in handling well separated clusters. This study provides the insights for choosing the suitable algorithm for specific clustering tasks.

Clinical Evaluation of Saussurea-costus in the Treatment of Respiratory and Digestive Disorders: A Study on 50 Patients
Authors:-Lecturer Dr. Salim Khan Yunus Khan, Associate Professor Dr Shaikh Mohd Naeem Rafiuddin, Associate Professor Dr. Saba Nazli Md Masood, Associate Professor Dr Parveen Akhtar Shaukat Ali

Abstract-Saussurea costus (قسط, ہندی عود) is a medicinal herb widely used in traditional medicine, including Ayurveda, Unani, and Chinese medicine, for its therapeutic effects on respiratory and digestive ailments. This study aims to evaluate the efficacy and safety of Saussurea costus in a cohort of 50 patients suffering from chronic respiratory or digestive conditions. The study employs a randomized clinical trial (RCT) approach, analyzing symptomatic relief, biochemical markers, and side effects over a 12-week treatment period. The findings suggest significant improvements in patient conditions with minimal side effects, supporting the continued use and potential integration of Saussurea costus in modern therapeutic applications.

Blockchain and E-Voting Systems: A Review of Progress and Research Opportunities
Authors:-Nikhlesh Kumar Badoga, Sumesh Sood

Abstract-In modern society, electronic and online voting systems are emerging as significant advancements in electoral technology, offering the potential to reduce organizational costs and increase voter turnout. Electronic Voting Machines (EVMs) have already revolutionized the electoral process by improving voter participation and enhancing the speed and accuracy of elections compared to traditional methods like paper ballots, punch card voting, and optical scan systems. These conventional approaches often face challenges such as fraud, voter manipulation, inaccuracies, and inefficiencies. Similarly, online voting systems promise to further streamline elections by eliminating the need for physical infrastructure, enabling voters to cast their votes from any location with internet access. However, despite their advantages, online voting solutions are met with caution due to vulnerabilities to cybersecurity threats. Risks such as Man-in-the-Middle (MitM) attacks, Denial-of-Service (DoS) attacks, and malware injection jeopardize the integrity and reliability of elections, highlighting the need for more secure and robust solutions. Blockchain technology offers a transformative approach to modernizing voting systems by providing a decentralized, transparent, and tamper-proof framework. Its decentralized architecture eliminates single points of failure, ensuring higher levels of security and reliability. This paper explores how blockchain technology addresses the limitations of conventional voting systems, including EVMs and online voting systems, by leveraging its inherent characteristics—speed, accuracy, immutability, and transparency. By distributing control across a network of nodes, blockchain-based voting systems enhance the integrity, accessibility, and trustworthiness of elections. Furthermore, the paper examines the potential of blockchain to modernize the existing voting framework, significantly improving the efficiency and trust in electoral processes while safeguarding democratic values in the digital age.

DOI: 10.61137/ijsret.vol.11.issue1.183

Solar Based Seed Sowing Robat
Authors:-Ms.Deepanjali Chitalkar, Mr.Ashutosh Bari, Ms.Tejaswini Chaudhari, Mr.Aniket Tele

Abstract-In India nearly about 70 percentage of people are depending on agriculture. Numerous operations are performed in the agricultural field like seed sowing, grass cutting, ploughing etc. The present methods of seed sowing, pesticide spraying and grass cutting are difficult. The equipment’s used for above actions are expensive and inconvenient to handle. So the agricultural system in India should be encouraged by developing a system which will reduce the man power and time. This work aims to design, develop and design of the robot which can sow the seeds, cut the grass and spray the pesticides, this whole system is powered by solar energy. The designed robot gets energy from solar panel and is operated using Bluetooth/Android App which sends the signals to the robot for required mechanisms and movement of the robot. This increases the efficiency of seed sowing, pesticide spraying and grass cutting and also reduces the problem encountered in manual planting.

NextGen LMS: Empowering Personalized Education Solutions
Authors:-Dr. M. Senthilkumar, PG.Gayathri, S.Rithika, S.Rosini, C.Vinothini

Abstract-In order to provide a structured and interactive learning environment, a Learning Management System (LMS) is essential to modern education and training. This project entails designing and developing a feature-rich LMS using Django for backend development and Tailwind CSS for a responsive and user-friendly interface. The system offers a centralized dashboard for administrators, instructors, and students, integrating essential functionalities like secure user authentication, dynamic course enrollment, and real-time attendance tracking. The platform enhances the learning experience by enabling personalized learning pathways, robust assessment tools, automated certification, and role- based access control for administrators, instructors, and students. The LMS is built with scalability and seamless third- party integrations, including payment gateways and video conferencing solutions, supporting both self-paced and instructor-led learning models. This LMS solution is intended to transform online learning by creating an efficient and captivating digital learning ecosystem. By emphasizing usability, security, and efficiency, this project seeks to improve educational outcomes, automate administrative workflows, and increase learner engagement. Django integration guarantees a stable and scalable backend, while Tailwind CSS offers an aesthetically pleasing and highly responsive design.

DOI: 10.61137/ijsret.vol.11.issue1.184

AR-Tifact-Genai and AR in Cultural Heritage
Authors:-Dr. K. Baskar, Mr. R. Sathyaraj, N. Prashanth, M. Vishwanathan, S. Yogeshkumar

Abstract-Developing a GenAI-enabled AR platform for museums to offer personalized, interactive experiences, enhancing visitor engagement and educational value, thereby preserving and promoting the heritage and culture of the nation. The system proposes the development of a novel GenAI-enabled Augmented Reality (AR) platform tailored for museums, aimed at delivering personalized and interactive experiences to visitors. Leveraging Unity Vuforia Area Target/Image Target technology, C# API, GenAI, langchain, and the OpenAI API, the platform seeks to revolutionize traditional museum visits by offering enhanced engagement and educational value. While existing solutions such as audio guides, mobile apps, and interactive displays have improved visitor experiences, they often lack interactivity and personalization. The proposed platform addresses these limitations by employing Generative AI to power a virtual assistant that delivers detailed information about exhibits and aids in navigation. Accessible via a cross- platform AR application on web, Android, and iOS devices, the solution promises to create a more immersive and enriching museum experience, ultimately contributing to the preservation and promotion of cultural heritage.

DOI: 10.61137/ijsret.vol.11.issue1.185

Enhanced Robust Control of a 3-DOF Helicopter System Utilizing an Unknown Input Observer
Authors:-Ashis De, Barun Mazumdar, Sandip Karmakar, Anjani Kumari Shaw, Bristi Mondal, Debjani Bar

Abstract-In this paper, a generalized matrix inverse-based unknown input observer (UIO) has been developed for a benchmark 3-DOF helicopter system to manage unknown, time-varying nonlinear dynamics and disturbances. The goal is to ensure the helicopter accurately follows the specified elevation and pitch references. To achieve this, we introduce a novel, simplified UIO to estimate the combined unknown dynamics, which are subsequently incorporated into the control design as a compensator. By introducing an auxiliary system, an invariant manifold is derived and utilized in the UIO design. The full-order observer, constructed using the g-inverse, is expanded and implemented to achieve this purpose. This new estimator requires setting only a single scalar and achieves exponential convergence. Consequently, the proposed control design utilizing the estimator can achieve precise output tracking. This control method is implemented on a benchmark 3-DOF helicopter, and its efficacy is validated through simulations and results.

DOI: 10.61137/ijsret.vol.11.issue1.186

Resume Screening Using Natural Language Processing
Authors:-Omkar Singh, Femenca Noroaha, Sravani Nirati, Sweety Rawa, Anjali Rasal

Abstract-The paper presents a solution to the issue of manually filtering out resumes from a large number of applications on the internet. The system uses Natural Language Processing to extract relevant information from unstructured resumes, creating a summarised form of each application. This simplifies the screening process and allows recruiters to analyze each resume in less time better. After the text mining process, the solution employs a vectorization model and uses cosine similarity to match each resume with the job description. The calculated ranking scores can then be used to determine the best-fitting candidates for a specific job opening. This approach addresses the challenges of manual filtering and fairness in resume screening, ensuring that the right candidates are selected for the job.

Advanced Encryption Methods for Enhancement in Safety of Big Data Using Cloud Computing
Authors:-Assistant Professor Ms. Nidhi Ruhil, Assistant Professor Ms. Ankita

Abstract-Big data is a combination of structured, semi structured and unstructured data. Also we discuss about intrusion detection system. The introduction of Big Data into the field of information technology has made the process of managing and analyzing data a great deal more difficult. It is essential to take everything into consideration, including aspects such as volume, diversity, pace, importance, and complexity. The processing of enormous amounts of data is simplified with the use of clustering. When dealing with unstructured data, this is a very useful skill to have. It is possible to offer a wide range of computer services, such as servers, storage, databases, and networking, in addition to analytics and intelligence, at a cheaper cost by using cloud computing, which makes use of the Internet as its delivery route. This makes it feasible to give a variety of cloud services at a reduced cost. The protection of such vast quantities of data is the primary challenge.

Water Level Management System Using GSM Technology
Authors:-Omkar Rajesh Shirsat, Nidhi Piyush Shah

Abstract-In the past few decades urbanization has seen an exponential growth. This gave rise to idea of ‘Smart Cities. The ‘Water level management system using GSM technology’ Model introduces a cutting-edge approach to tackle the escalating challenges of urban water management. In response to the burgeoning urbanization and burgeoning fresh water usage, conventional water management systems have proven insufficient. This model harnesses hardware and software technology to create an intelligent and efficient water management system. The core of the system comprises an array of electronic sensors strategically positioned in water storage tanks, continuously monitoring water level in real-time. These sensors communicate with a centralized server through a network, providing live updates on water level on one or more devices. The proposed model includes deployment of prototype of actual model which helps to reduces the water wastage.

Yatra Saathi – Study of Travel Tourism Planner
Authors:-Pushpendra Verma, Manish Nagar, Nitesh Solanki, Nikhil, Krapali

Abstract-The following research work captures the development of Travel Tourism Planner Application – An integrated system for effective trip planning. It provides an LBS feature which enhances the pace of planning, personalization and creating efficiency in traveling among the users. Form for trip planning. The application integrates location-based services, which empower users to effectively plan, customize, and optimize their travel experiences. The frontend of our application is developed in HTML, CSS, and JavaScript; however, to enable us to develop our application for various platforms and still be compatible, we use a platform called React. The backend uses Node.js and Express.js to facilitate communication with external APIs like Sky Scanner, Booking.com, & Google Maps, to offer live data. Developed with an Agile approach, this application is well optimized for user experience, secure and performance oriented. Measures of user security comprising of Auth 2.0 for user’s authenticate and SSL/TLS for protection of users’ data have been established. In addition to that, there is the use of features like lazy loading and code mini fication for improvement of the performance. In regard to this, this paper shall give an account of the development process of the system alongside the various difficulties faced and measures put in place to contain the. It seeks to focus on tool design and development processes that lead to a credible and effective travel planner for today’s travelers.

Synthesis and Characterization of Poly Vinyl Alcohol (PVA) Based Nano Composites Using Silver (Ag) Nanoparticles, Aimed at Improving the Performance Characteristics of Footwear Insoles
Authors:-Research Scholar Preeti Sahu, Professor Dr. N.P. Rathore

Abstract-The study focuses on the development and analysis of polyvinyl alcohol (PVA) nanocomposites incorporating silver (Ag) nanoparticles, aimed at improving the performance characteristics of footwear insoles. The thesis abstract presents a comprehensive overview of research dedicated to the formulation and evaluation of polyvinyl alcohol (PVA) nanocomposites infused with silver (Ag) nanoparticles, with the primary objective of enhancing the functional properties of insoles used in footwear. The introduction outlines the significance of integrating nanotechnology into material science, particularly in the context of footwear applications, where comfort and durability are paramount. The materials and methods section details the synthesis of PVA nanocomposites, the incorporation of Ag nanoparticles, and the various analytical techniques employed to assess their performance characteristics. The conclusion summarizes the findings, highlighting the potential of these nanocomposites to significantly improve the quality and longevity of footwear insoles, thereby contributing to advancements in the field of wearable technology.

DOI: 10.61137/ijsret.vol.11.issue1.187

Heart Disease Prediction Using Machine Learning
Authors:-Joshua Adewole, Dr. Patrick S. Olayiwola

Abstract-This research focuses on using machine learning and data analysis tools to determine the possibility of a heart disease problem in an individual. A predictive model for Heart diseases using XGBoost was developed using features from blood sample tests and habitual factors. Several other models were used to validate the efficiency of the result from the XGBoost model. The performance of the model was then evaluated and finally a web application with an intuitive user interface was developed to serve the model for public use. XGBoost model is under a family of extreme gradient boosting models – which are known for remarkable results. Hence, it was used in this project as a classification tool. With an accuracy of over 90%, XGBoost was able to successfully classify the result, other models fell short within ranges of -2 to -20%; therefore, even further justifying the use of XGBoost. A web application was then hosted allowing medical practitioners and public users, run their features and get results on the possibility of a heart disease problem. In conclusion, the model was sufficient enough to yield possibilities of a heart disease problem which is in that regard, successful. Albeit, future work would be needed on further making variations on the interface – mobile, desktop e.t.c. making such solutions more accessible, and also including more important fields – especially habitual factors like sleep schedule etc.

Green Synthesis and Characterisation of Iron and Cobalt Oxide Nanoparticles Using Piper Dravidii Leaves Extract
Authors:-Yogita Shinde

Abstract-Manufacturing green nanoparticles is a safe, secure, and promising technique. In the current study, piper dravidii leaves extract was used to prepare iron oxide nanoparticles (Fe2O3-NPs) and cobalt oxide nanoparticles (CoO-NPs). UV-visible spectroscopy, scanning electron microscopy (SEM), dynamic light scattering (DLS), vibrating sample magnetometer (VSM), and differential scanning calorimetry (DSC) were used to evaluate the produced Fe2O3-NPs and CoO-NPs. The surface plasmon resonance effect was used to validate the synthesis of FeONPs. FeONPs have an average particle size of about 163.5 nm, a polydispersity index of 0.091, and a zeta potential of -13.8 mV, according to dynamic light scattering (DLS). At 176.91°C, differential scanning calorimetry (DSC) revealed an endothermic peak. With a magnetization value of 3.483 emu/g at ambient temperature, iron nanoparticles were shown to have superparamagnetic characteristics by the Vibrating Sample Magnetometer (VSM) examination, suggesting that they might be used in a magnetically targeted medication delivery system. It has been shown that this biosynthetic method is economical, environmentally benign, and has a lot of potential for use in biomedical research.

DOI: 10.61137/ijsret.vol.11.issue1.188

Smart GFM Monitoring System Using AI and ML
Authors:-Prachi Navnath Khartode, Sanika Sandeep Sawalkar, Sakshi Jitendra Wakade, Vidya Sandeep Ahire

Abstract-This paper presents a solution to the inefficiencies of traditional manual attendance systems by proposing a face recognition-based attendance system. project aims to enhance the manual attendance process by using a mobile platform and face recognition technology. The design consists of three main modules: inputting attendance information, signing in with facial recognition, and maintaining attendance records. It begins by explaining the principles of face detection and classification, followed by an analysis of how to build a face recognition classifier. The system is then implemented on an Android platform, allowing for practical use in workplaces.

DOI: 10.61137/ijsret.vol.11.issue1.189

Gravity Location Model of Blood Supply Chain Network Design: A Case Analysis
Authors:-Research Scholar Namita Rani Mall

Abstract-The gravity model to the blood supply chain is a conceptual framework that seeks to explain and optimize the distribution of blood products within a healthcare system. It is useful when identifying suitable geographical location within arrange. It is also used to find location that minimizes the cost of transporting raw material from the supplier and finished goods to the markets served. This model also assumes that the transportation cost grows linearly with the quantity shipped. All distances are calculated as the geometric distance between two points on the plane. Using a numerical example, the applicability of the proposed network is analyzed.

DOI: 10.61137/ijsret.vol.11.issue1.190

Integration of Electric Vehicles in Smart Grid: A Comprehensive Analysis
Authors:-Sushil Kumar Panda

Abstract-A rapid shift towards sustainability and clean energy is evident in this decade. The fervent adoption of EVs is acting as a catalyst for the same. Technologies such as smart power grids, communication, V2G, and integration systems render market growth—EVs as mobile power systems can serve as a potential market in the coming years. The paper focuses on the current scenario’s innovative grid technologies, VGI, and the literature on ML algorithms that aid in optimizing the integration configurations. The paper proposes a popular ML model in various bright grid areas that make VGI feasible.

Deep Learning Approaches in Solving Battery Health Problems in Electrical Vehicles
Authors:-Sushil Kumar Panda

Abstract-EVs offer technology and a smooth driving experience while reducing tailpipe emissions. EV adoption has been increasing both by volume and market share. Batteries and Battery technology constitute vital components of smooth functioning. However, battery degeneration and practical management issues remain significant challenges in the EV industry. Evolving Deep learning and machine learning approaches are being applied to solve these challenges. The current study focuses on using deep learning approaches to battery health management and explores the role of neural networks in predicting battery health.

EV Power Train Market Trends and Impact of Battery Management System on Powertrain Performance
Authors:-Sushil Kumar Panda

Abstract-The Market is on the rise now due to the heavy adoption of clean energy and the availability of flexible options for all target consumers. The EV market is gaining a grip in the automotive industry due to new innovations around battery technologies. It reviews the market trends, challenges and strengths of the ICE and EV powertrain in the current global market. The paper focuses on Powertrain performance and its relationship with battery optimization. The Tesla 3 Long Range Model is studied and analysed to find out the impact of battery performance on Power train performance.

A MWB Antenna Design with Tunable Notch Band for 5G Communication
Authors:-Madhuraneni Sai Dinesh, Sare Kulayappa, Thumu Sashidar, Mr.R.Venkatesan

Abstract-A Multiple Input Multiple output (MIMO)-fed circular slot antenna with wide tunable dual band-notched function and frequency reconfigurable characteristic is designed, and its performance is verified experimentally for high-frequency millimeter-waveband (MWB) communication 6G application s. The dual band-notched function is achieved by using an Ring-shapedresonator inserted the circular ring radiation patch and by etching a parallel stub loaded resonator in the MIMO transmission line. There are a wide range of approaches that have been advanced in the literature for adding reconfiguration to metamaterial devices all the way from the RF through the optical regimes, but some techniques are useful only for certain wavelength bands. A tunable range of almost one octave can be achieved if the R-SRR is loaded in its center with a slot. Furthermore, it has been demonstrated that a reconfigurable device can be achieved if a pair of shunt connected slots are introduced across the slots of the host MIMO. This feature, in conjunction with the tunability of a loaded R-SRR, has been used to achieve a reconfigurable and tunable structure. Finally, in order to demonstrate the potential 6G application of the proposed structure, a MWB antenna design with tunable notch band for Future 6G Communications. The design methodology has been validated through electromagnetic simulations.

DOI: 10.61137/ijsret.vol.11.issue1.191

Performance Evaluation and Analysis of Cement Stabilized Fly Ash–GBFS Mixes as A Highway Construction Material
Authors:-Aman Ghagre, Professor Shashikant B. Dhobale

Abstract-Fly ash and granulated blast furnace slag (GBFS) are major by-products of thermal and steel plants, respectively. These materials often cause disposal problems and environmental pollution. Detailed laboratory investigations were carried out on cement stabilized fly ash-(GBFS) mixes in order to find out its suitability for road embankments, and for base and sub-base courses of highway pavements. Proctor compaction test, unconfined compressive strength (UCS) test and California Bearing Ratio (CBR) test were conducted on cement stabilized fly ash-GBFS mixes as per the Indian Standard Code of Practice. Cement content in the mix was varied from 0% to 8% at 2% intervals, whereas the slag content was varied as 0%, 10%, 20%, 30% and 40%. Test results show that an increase of either cement or GBFS content in the mixture, results in increase of maximum dry density (MDD) and decrease of optimum moisture content (OMC) of the compacted mixture. The MDD of the cement stabilized fly ash-GBFS mixture is comparably lower than that of similarly graded natural inorganic soil of sand to silt size. This is advantageous in constructing lightweight embankments over soft, compressible soils. An increase in percentage of cement in the fly ash-GBFS mix increases enormously the CBR value. Also an increase of the amount of GBFS in the fly ash sample with fixed cement content improves the CBR value of the stabilized mix. In the present study, the maximum CBR value of compacted fly ash-GBFS-cement (52:40:8) mixture obtained was 105%, indicating its suitability for use in base and sub-base courses in highway pavements with proper combinations of raw materials.

Road Safety Audit Based Design Issues Mitigation Plan in 4 Laning of Khalghat –MP/ Maharashtra Border Section of NH-52 (Old NH-3)
Authors:-Prince Kumar, Professor Shashikant B. Dhobale

Abstract-Transportation plays a key role in the development of an area, but it happens only when the transportation is safe, rapid, comfortable and economy. A road is considered safe when only a few, or no accidents occur. Road and its surroundings, road users and vehicles are the elements contributing to road accidents. Pedestrians, bicyclists and two-wheeler motorized riders are the vulnerable road users. The loss of human life due to accident is to be avoided. Road safety audit (RSA) is a formal procedure for assessing accident potential and safety performance in the provision of new road schemes and schemes for the improvement and maintenance of existing roads. These Audit studies or analysis give scope for the reduction of accidents and helps us to provide safe, self-explaining and forgiving roads. By this we can save the precious human life as well as the nation’s economy. The selected for this study is part of 4 Laning of Khalghat – MP/ Maharashtra Border Section of NH-52 (Old NH-3). Knowledge of accidents that have occurred on roads helps us to improve the design of the roads or to influence the behavior of road users, so that similar accidents do not occur again. Literature review will be done for the safe movement of the Road safety audit and will check the merits and demerits of the techniques used previously.

Analysis on Adversity Quotient (AQ) and Emotional Intelligence
Authors:-Dr Jakka Pradeep

Abstract-Physical adversities such as illness, obesity, accidents and psychological adversities such as emotional, social and play hazards and family and relationship adversities or personality threats like formation of unfavourable self-concept can lead to low self-esteem and low emotional intelligence. Adversity quotient is a score that measures the ability of a person to deal with setbacks, challenges, and problems. Focus on adversity quotient, as it is positively correlated with emotional intelligence. Focus on self-awareness, self-regulation and empathy. Today’s youths are tomorrow citizens.

DOI: 10.61137/ijsret.vol.11.issue1.196

Analysis of Human Disease Prediction Using Machine Learning Models
Authors:-Pavani sakthima S, Sangamithra Saravanan

Abstract-Disease prediction with machine learning is one of the areas that is very rapidly developing with the help of historical medical data to find the patterns and diagnose early symptoms of diseases, hence predicting the diseases. This study covers a wide range of machine learning algorithms, from traditional methods like Naïve Bayes, K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) to more advanced techniques such as Random Forest and deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Real-world medical datasets have been applied to train and evaluate these models; those contain medical histories of the patient, life habits, genetic conditions, and findings from diagnostic tests. Accuracy, precision, recall, and F1 score metrics measure the efficiency of each algorithm in predicting diseases. Experimental results show that deep learning algorithms, specifically CNN and RNN and hybrid models perform much more accurately than traditional machine learning techniques. This superiority is especially observed in complex and unstructured data, such as medical images; deep learning models happen to very effectively extract difficult and intricate features and patterns. Traditional algorithms used are best suited for structured data but are incompetent in handling the complexity and variability that characterize most of the medical datasets. The paper further emphasizes gathering heterogenous data sources, like genetic information, lifestyle, and other contributing factors, to enhance predictive accuracy.

Assessing Emotional Intelligence in the Indian Hospital Workplace: A Study of Knowledge and Practice among Employees
Authors:-Dr. Jessy Palal Ithappiri

Abstract-Purpose: The goal of this study is to ascertain how well-informed Indian hospital staff members are regarding emotional intelligence (EI) principles, how well EI skills are implemented in various work environments, and which EI competency areas require staff development. Design/Methodology/Approach: A sample of 715 Indian hospital employees participated in the study, which employed a quantitative research approach. Being under the leadership of the HODL, the sample was kept stratified to ensure job title and division diversity. Using surveys standardized by the EQ-MAP organizations, research participants were assessed on their EI knowledge and practice. Findings: This study was conducted, in which 51.6% of participants answered they understood their emotions and influenced the performance of a professional setting “quite well” or “extremely well.” Similarly, it was 57.1% successful in sensing and deciphering the emotions of his or her colleagues. Furthermore, 59.4% reported being able to properly manage their emotions, whereas 58.7% could effectively communicate their thoughts and feelings. Conclusion: The study explains levels of understanding and application in practical settings of EI among hospital employees. It further highlights the important role of focused interventions for the development of EI competencies that can enhance workplace dynamics and the quality of care for patients. Originality/Value: Hospital employees’ understanding of and use of Emotional Intelligence (EI) underscores the inclusion of a new study in hospitals, greatly expanding the corpus of information on medical care and improving the standard of care that patients receive. The study gives insights for targeted interventions towards improving workplace dynamics and patient care quality, thereby highlighting a vital area of focus in healthcare management.

ERP Post-implementation Challenges and Solutions
Authors:-Sagar Gupta

Abstract-Enterprise Resource Planning (ERP) systems have become essential tools for organizations seeking to integrate and streamline business functions such as finance, human resources, sales, and manufacturing. However, ERP implementation remains a complex, multi-phase process characterized by both technical and organizational challenges. This study systematically reviews the critical success factors (CSFs) that influence successful ERP implementations, drawing insights from extensive literature and case studies. Key factors identified include effective change management, robust data management, strong management commitment, comprehensive project planning, proactive risk assessment, and strategic vendor partnerships. These elements play a pivotal role in addressing challenges such as resistance to change, system integration issues, and process reengineering complexities. By focusing on these CSFs, organizations can enhance operational efficiency, improve decision-making, and ensure a positive return on investment. This review provides valuable guidance for practitioners and scholars, offering a consolidated perspective on achieving successful ERP deployment in today’s competitive business landscape.

DOI: 10.61137/ijsret.vol.11.issue1.197

Design and Fabrication of Hand-Operated Pneumatic Hydraulic Metal Sheet Cutter
Authors:-Joy Sarker

Abstract-In this project, a hand-operated hydraulic Metal Sheet cutter machine has been fabricated. A hydraulic jack is used as the hydraulic component here. The project was started to minimize the effort required in shearing metal sheets of various thicknesses compared to that required when using a simple hand-operated mechanical sheet cutter. The cutting of metal sheets is an essential process in various industries, but conventional cutting machines are often expensive, energy-intensive, and cumbersome to operate. This thesis presents the design and fabrication of a cost-effective, hand-operated hydraulic metal sheet cutter aimed at providing a simple yet efficient solution for small-scale industries and workshops. The device operates using a hydraulic mechanism, eliminating the need for electrical power, and can be manually operated with minimal physical effort. The cutter is designed to handle a range of metal sheet thicknesses, offering versatility while maintaining precision and durability. The design focused on optimizing the cutting force and mechanism to achieve high cutting efficiency with reduced human exertion. The project encompasses the entire development process, including the design calculations, material selection, and fabrication techniques. Performance tests were conducted to assess the functionality and efficiency of the cutter under various conditions. The results demonstrate that the hand-operated hydraulic cutter can effectively cut metal sheets with minimal deformation and high accuracy, making it a practical tool for small workshops or environments with limited resources. This study concludes that the developed system is not only economical and environmentally friendly but also provides an innovative alternative to conventional electrically powered cutting machines. Further optimization could potentially enhance its applications across various industries.

DOI: 10.61137/ijsret.vol.11.issue1.270

Intelligent Infusion Anesthetic Dispenser Using Smart Iot
Authors:-Sai Kumar N, Vishnu Vardhan S, Bharath Chand N, Brahma Reddy B

Abstract-In hospitals, maintaining safe anesthesia levels during long surgeries is vital. Manual administration poses risks, as overdosing may be fatal, and underdoing could cause the patient to wake mid-surgery. This project proposes an automated, microcontroller- based anesthesia injector that precisely delivers doses using a syringe infusion pump. The anesthetist sets the dosage in milliliters per hour based on sensor feedback monitoring patient vitals. The microcontroller adjusts a DC motor to control the infusion pump accurately, ensuring steady anesthesia throughout the procedure. This automation reduces manual dependency and enhances patient safety. The system also incorporates safety mechanisms, including alarms and fail-safe operations. In case of anomalies such as syringe blockages, motor malfunctions, or irregular patient vitals, the system triggers an alert to notify the anesthetist immediately. Additionally, the microcontroller stores real-time data, which can be accessed later for review and analysis, contributing to improved surgical procedures and patient outcomes. By integrating advanced monitoring and control features, this solution ensures precision, reliability, and adaptability in critical medical environments.

DOI: 10.61137/ijsret.vol.11.issue1.198

AI-Enhanced Shunt Active Power Filters for Minimizing Harmonics in Microgrid System
Authors:-Faruk J. Sayyad, Professor Shivaji S. Bhosale

Abstract-The increasing penetration of renewable energy sources and the rise in non-linear loads in microgrids have led to the growing concern of harmonic distortion in power systems. Harmonics can deteriorate power quality, affect system performance, and damage sensitive equipment. Shunt Active Power Filters (SAPFs) are commonly used to mitigate harmonic distortion. However, conventional SAPF methods face challenges in dynamic microgrid environments, especially when dealing with changing loads and renewable energy variations. This paper presents an AI-enhanced SAPF approach for minimizing harmonic distortion in microgrids. By integrating machine learning and optimization algorithms, the proposed approach provides real-time harmonic detection and compensation, adapts to fluctuating conditions, and improves power quality. Simulation results demonstrate the effectiveness of the AI-based method in reducing harmonic distortion, enhancing system performance, and optimizing computational efficiency compared to traditional approaches.

Incorporation of Indigenous Knowledge & Skills within School Curriculum
Authors:-Dr.Laxmiram Gope, Assistant Professor, & Sujit Kuiry, Research Scholar

Abstract-The quality of education reflects the quality of life. This quality is not only confined to a particular dimension, but it also has an expansive connotation. In the words of Bernard (1999), the rights of all the children to survival, protection, development, and participation are at the centre of the discourse, which covers all aspects of the school and its surrounding community. This means that the focus is on learning to strengthen the capacities of children to act progressively on their own through acquiring relevant knowledge, valuable skills and appropriate attitudes; this builds up a safety network permeated by a sense of security and healthy interaction. Primarily, the present paper focuses on the quality enhancement and skill development by incorporating community-centered indigenous knowledge within the school’s curriculum. In this paper, the researchers made a humble attempt to explore the components of indigenous knowledge within the community network space and thereby suggest the inclusion of community-based indigenous knowledge for the objective of an inclusive school curriculum through skill-development techniques and community-participative indigenous knowledge. An attempt has been made to determine why community-based knowledge is crucial for various kinds of risk management, which is situational knowledge, and is highly pertinent for shaping survival strategies within the community. Overall, researchers perceive that it is also helpful for re-constructing and re-orienting our ongoing education system because it has built-in cultural support and cultural value with ancient spiritual essence. With the help of document analysis and analysis of primary and secondary data the researchers sought to reveal that the indigenous knowledge cum community knowledge has many important aspects in respect of educational goals and it also helps to improve the educational instructional strategy. Even such community-centric knowledge is essential for the individual perspective, because it celebrates the diversity of learning. This study is an avenue for policymakers, educators, and activists associated with quality education to advance the vocational system in school education.

DOI: 10.61137/ijsret.vol.11.issue1.199

Artificial Intelligence in Cybersecurity
Authors:-Rushi Bhayani, Darshna Sonani, Professor Bhoomika B. Chauhan

Abstract-Even in the past few decades, cyberattacks have grown tremendously in number as well as quality. Consequently, creating a cyber-resilient mechanism is significant. Traditional security measures cannot prevent data breaches during cyberattacks. Cybercriminals have devised new and sophisticated methods and high-end gadgets in their hacking and data breaching capabilities. As both sides resort to Artificial Intelligence (AI) technologies to bring smart models to prevention systems from attacks in cyberspace, it is now feasible to rely on these emerging technologies, themselves able to rapidly adapt to such situations, as core cornerstones in the field of cybersecurity. The AI-based techniques provide the best cyber defense tool, efficient and powerful enough to discover malware attacks, network intrusion case, spam and phishing emails, breaches of data, and many more, and issue alerts when security incidents happen. In this paper, we evaluate how AI is impacting cybersecurity and summarize relevant research toward understanding the benefits of AI in cybersecurity.

DOI: 10.61137/ijsret.vol.11.issue1.200

Artificial Neural Network
Authors:-Divya Maheta, Dhyanee Kanojiya, Professor Bhoomika B. Chauhan

Abstract-An ANN is an information- processing paradigm inspired by the way natural nervous systems similar as the brain process information. The crucial element of this paradigm is the unique structure of the information- processing system. It consists of multitudinous largely connected processing rudiments( neurons) wanting to work with each other to break particular problems. ANNs learn by exemplifications like humans. It’s through learning that an ANN is set to work on a particular operation sphere, say, for case, pattern recognition or bracket of data. Learning in natural systems refers to change in the synaptic connections being between the neurons. The same holds for ANNs. This paper gives an overview of artificial neural networks, their working, and training. It mentions the operation and advantages of AANN.

Automation in Banking: Simplifying Operations and Enhancing Customer Experience
Authors:-Kinil Doshi

Abstract-The banking industry is undergoing a significant transformation with the integration of automation technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and advanced data analytics. Automation streamlines banking operations by reducing manual intervention, increasing efficiency, and minimizing errors. AI-powered chatbots enhance customer service with instant support, while automated fraud detection systems strengthen security and compliance. Additionally, automation improves regulatory adherence by facilitating real-time monitoring and reporting, ensuring transparency and risk mitigation. The implementation of automation also leads to cost savings, operational scalability, and seamless digital banking experiences. As the industry moves towards fully automated banking ecosystems and blockchain integration, automation is set to redefine the financial landscape, making banking more accessible, secure, and customer-centric.

DOI: 10.61137/ijsret.vol.11.issue1.201

Published by: vikaspatanker

Authors: Assistant Professor Dr. Sheetal Bhasin Kapoor

Abstract: The banking sector has experienced unprecedented transformation due to rapid technological advancements, evolving customer expectations, regulatory reforms, and increasing sustainability concerns. Digital banking, artificial intelligence, financial technology (FinTech), data analytics, cloud computing, and customer-centric innovations have fundamentally reshaped banking operations and business models. Business transformation has become a strategic imperative for banks seeking sustainable growth, operational excellence, and long-term competitiveness. This study examines the key business transformation strategies adopted by the banking sector and analyses their contribution to sustainable organizational growth. Using a qualitative research approach based on secondary data, the study explores digital transformation, innovation, leadership, customer experience, operational efficiency, and sustainable banking practices. The findings indicate that banks embracing digital transformation and strategic innovation achieve enhanced customer satisfaction, operational resilience, improved financial performance, and long-term sustainability. The study concludes that business transformation should integrate technological innovation, customer-centricity, environmental responsibility, and strategic leadership to achieve sustainable growth in the banking sector.

DOI: https://doi.org/10.5281/zenodo.21454295

Published by: vikaspatanker

Authors: Sai Raghu Ram Gummadidala

Abstract: The fast adoption of hybrid cloud ecosystems incorporating Software as a Service (SaaS), Infrastructure as a Service (IaaS) and on-premise infrastructures has increased significantly the complexity of enterprise networks. The integration between the components of this ecosystem creates serious security concerns associated with uncontrolled connectivity, shadow networking, lateral movement attacks, covert communications via APIs, and low visibility among other issues. Current perimeter-based security models cannot provide the required level of protection to current cloud infrastructures based on the principle of trust and lack of real-time monitoring. The objective of this paper is to propose a Zero Trust Shadow Networking Detection Framework to identify the risk of hidden communications within hybrid cloud ecosystems. The proposed framework relies on trust evaluation, adaptive anomaly detection, microsegmentation, behavior analysis, and threat monitoring leveraging machine learning for protecting communications in SaaS, IaaS and on-premise infrastructures. A dynamic connectivity graph is built to evaluate communication links and identify hidden channels. Mathematical trust modeling and risk propagation analysis have been introduced for the purpose of increasing threat detection efficiency and minimizing unauthorized access. Evaluation based on experiments conducted via simulation of hybrid cloud traffic conditions reveals that the presented framework is more effective than conventional firewalls, virtual private networks, and other Zero Trust frameworks in terms of detection efficiency, decreasing false positives, responding to threats, preventing lateral movement, and mitigating risks on the network.

DOI: http://doi.org/10.5281/zenodo.20305176

Published by: vikaspatanker

IoT Enabled Solutions for Women Safety and Health Monitring
Authors:-Sudeshna P, Vivekanandan K

Abstract-Women and children today deal with a number of problems, including sexual attacks. The victims’ life will undoubtedly be greatly impacted by such atrocities. It also has an impact on their psychological equilibrium and general wellbeing. The frequency of these acts of violence keeps rising daily. Even schoolchildren are victims of sexual abuse and abduction. In our society, a nine-month-old girl child is not protected; she was abducted, sexually assaulted, and ultimately killed. Seeing the abuses of women makes us want to take action to ensure the protection of women and children. Therefore, we intend to present a device in this project that will serve as a tool for security and guarantee the safety of women and children. GSM microcontroller.

DOI: 10.61137/ijsret.vol.10.issue5.224

The Generative AI Industry is Flawed!
Authors:-Isha Syed, Aryan Purohit, Yash Malusare

Abstract-Generative Artificial Intelligence (GenAI) has evolved rapidly, creating transformative opportunities across sectors, particularly in healthcare and marketing. Despite the promise of improved patient care, streamlined medical workflows, and enhanced customer engagement, GenAI faces significant challenges. Key obstacles include high computational costs, data-privacy concerns, and ethical accountability in content generation. Moreover, the open-source initiatives by leading firms like Meta have intensified competition, pushing GenAI models toward commoditization, impacting revenue structures and sparking a “race to the bottom” in pricing. The market is further complicated by monopolistic dependencies on critical hardware providers, particularly Nvidia, which dominate GPU supplies essential for AI training. With a rapidly growing market projected to reach trillions by 2030, the industry must navigate these barriers to realize the full potential of GenAI. This study explores GenAI’s current applications, fiscal and ethical challenges, and the strategic imperatives needed to foster sustainable, profitable growth within an increasingly crowded and commoditized industry landscape.

DOI: 10.61137/ijsret.vol.10.issue6.325

Predicting Customer Success in Digital Marketing with Data Mining and Naive Bayes Classifier Using Google Analytics
Authors:-Rohini Sharma, ER. Vanita Rani (HOD)

Abstract-In the era of digital transformation, organizations are increasingly leveraging data analytics to optimize marketing strategies and enhance customer engagement. Predicting customer performance is critical for businesses aiming to tailor marketing efforts, improve customer retention, and maximize revenue. This study presents a comprehensive data mining framework utilizing the Naive Bayes classifier to forecast customer performance based on historical behavior and interaction data. Employing Google Analytics as the primary data collection tool, we evaluate the model’s effectiveness by analyzing metrics such as accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and the area under the Receiver Operating Characteristic (ROC) curve. The results illustrate the framework’s potential to provide actionable insights into customer behavior, thereby facilitating more informed marketing strategies and decision-making processes.

DOI: 10.61137/ijsret.vol.10.issue6.326

Vertical Farming (Hydroponics)
Authors:-Hemlata Karne, Shane D`Costa, Aryan Chaure, Vaibhav Bhuwaniya, Abhinandan Daga, Vaibhavi Chavan

Abstract-IIn the current times, conventional farming which is the most widely used type of farming has been affected by several problems such as decrease in the availability of space due to the increasing population, wastage of water, destruction of crops due to insects, rains, etc. Furthermore, in the future where the population is expected to grow further, these problems in farming can be disastrous as it can decrease the availability of food and can lead to the starvation of a big part of the population. Hydroponics which is another method of farming can be a solution to most of the problems associated with conventional farming. In this type of farming, crops are grown without the requirement of soil, instead it utilizes a growing medium and water is directly supplied to the roots of the plants. Further fertilizers are dissolved in the water itself. This type of farming can save a lot of space as the plants are grown in vertical slots and they can be stacked upon each other and water requirement is also very low for this type of farming as most of the water is recycled. In this paper, we are going to discuss the various factors which affect the growth rate of the plants in vertical farming. The plants we have taken are jalapeno plants. The trail period is of 7 weeks where we have compared different factors affecting the growth rate of the plants.

DOI: 10.61137/ijsret.vol.10.issue6.327

AI Based Smart Chatbot
Authors:-Ansh Jaiswal, Reecha Daharwal, Muskan Dwivedi, Riddhima Mudgal, Srashti Garg

Abstract-Chatbots function as software that allows users to ask questions and receive assistance through appropriate responses. This paper explores an AI-based chatbot designed specifically for students experiencing suicidal thoughts or at risk of suicide. The aim of this chatbot is to help reduce the number of suicides among students by providing them with timely support and guidance. Leveraging the expansive and rapidly evolving field of AI, this technology can contribute positively to addressing societal challenges and promoting well-being.

DOI: 10.61137/ijsret.vol.10.issue6.328

Enhancing Beyond-5G and 6G Network Backhaul through Hybrid RF-FSO Communication: An Examination of HAPS and LEO Satellite Integration
Authors:-Aakash Jain, Prakhar Vats, Priyanshu Singh, Shreya Tiwari, Mohammed Alim

Abstract-As data demands increase with the evolution toward beyond-5G and 6G communication systems, achieving efficient network backhaul is crucial to support high data rates, minimized latency, and broad geographic coverage. Traditional backhaul networks, reliant on radio frequency (RF) communications, face limitations in scalability and bandwidth, particularly in dense urban and rural remote areas. This paper explores a hybrid RF-Free-Space Optical (FSO) communication model, integrating Low Earth Orbit (LEO) satellites with High Altitude Platform Stations (HAPS) to enhance backhaul network efficiency. The proposed HAPS-LEO cooperative model mitigates atmospheric disruptions and offers scalable, high-bandwidth solutions. We further examine Contact Graph Routing (CGR) as a protocol for optimized data routing in variable connectivity conditions, presenting simulated performance results that demonstrate the advantages of this architecture.

DOI: 10.61137/ijsret.vol.10.issue6.329

Heart Disease Detection Using Machine Learning
Authors:-Assistant Professor Ms. Pragati, Mr. Shivam Chawla, Mr. Yash Mittal, Mr. Shivam Mishra

Abstract-Cardiovascular diseases (CVDs) are a leading cause of death worldwide, posing a significant health threat not only in India but across the globe. This highlights the critical need for a dependable, precise, and accessible system to diagnose such conditions promptly, enabling timely treatment. Machine learning algorithms have become invaluable tools in healthcare, automating the analysis of extensive and complex datasets. Recent studies demonstrate that various machine learning techniques can aid healthcare professionals in diagnosing heart-related conditions. The heart, second only to the brain in importance, plays a vital role in circulating blood throughout the body. Predicting heart disease occurrence is thus essential in the medical field. Data analytics enhances the prediction accuracy by analysing large volumes of patient data, often maintained on a monthly basis, which could be utilized to anticipate potential future diseases. Techniques such as Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM) are widely applied to predict heart conditions. Diagnosing and predicting heart diseases remain a considerable challenge for both doctors and hospitals globally. To mitigate the high mortality rate associated with these diseases, efficient and rapid detection methods are essential. Machine learning and data mining techniques hold a crucial role in this context. Researchers are accelerating efforts to develop machine learning-based software that can assist doctors in both predicting and diagnosing heart diseases. This research project aims to leverage machine learning algorithms to predict the likelihood of heart disease in patients.

DOI: 10.61137/ijsret.vol.10.issue6.366

Traffic Safety Assessment and Design Improvement

Authors:-Dr. G. Tabitha, Korada Lakshman

Abstract-This project focuses on traffic safety analysis, aiming to enhance road user safety through a comprehensive evaluation of various factors that influence accident rates and driving conditions. By assessing parameters such as skid resistance, surface texture, visibility, and roadway geometry, the study identifies critical factors that contribute to traffic incidents and offers insights into effective safety measures. Field data was gathered from selected road sections, and laboratory tests were conducted to analyze surface characteristics. Statistical analysis was applied to understand the correlation between these factors and accident frequency, enabling the development of targeted recommendations to improve safety standards. The project underscores the importance of proactive road maintenance and design improvements in reducing accidents and enhancing the overall safety and efficiency of transportation infrastructure. This project aims to enhance road safety by conducting an in-depth analysis of factors contributing to traffic accidents and assessing the effectiveness of potential interventions. Through examining elements such as pavement skid resistance, surface texture, road geometry, and visibility, the study explores their influence on accident frequency and severity. Field data collected from high-risk road sections, along with laboratory testing of pavement properties, provide a foundation for evaluating existing conditions. Using statistical and spatial analysis, the study identifies patterns in accident data, highlighting critical areas for improvement. Recommendations are developed based on these insights to propose cost-effective strategies that prioritize safety, such as optimized pavement materials, better signage, and improved road design. This research underscores the role of systematic traffic safety analysis in advancing safer, more resilient transportation systems. This project undertakes a comprehensive traffic safety analysis aimed at reducing accidents and improving road safety through a detailed examination of key factors affecting driving conditions. By focusing on parameters such as skid resistance, pavement surface texture, visibility, road geometry, and traffic flow, the study seeks to identify elements that significantly impact accident rates and driving safety.

Traffic Safety Assessment and Design Improvement

Authors:-Dr. G. Tabitha, Korada Lakshman

Abstract-This project focuses on traffic safety analysis, aiming to enhance road user safety through a comprehensive evaluation of various factors that influence accident rates and driving conditions. By assessing parameters such as skid resistance, surface texture, visibility, and roadway geometry, the study identifies critical factors that contribute to traffic incidents and offers insights into effective safety measures. Field data was gathered from selected road sections, and laboratory tests were conducted to analyze surface characteristics. Statistical analysis was applied to understand the correlation between these factors and accident frequency, enabling the development of targeted recommendations to improve safety standards. The project underscores the importance of proactive road maintenance and design improvements in reducing accidents and enhancing the overall safety and efficiency of transportation infrastructure. This project aims to enhance road safety by conducting an in-depth analysis of factors contributing to traffic accidents and assessing the effectiveness of potential interventions. Through examining elements such as pavement skid resistance, surface texture, road geometry, and visibility, the study explores their influence on accident frequency and severity. Field data collected from high-risk road sections, along with laboratory testing of pavement properties, provide a foundation for evaluating existing conditions. Using statistical and spatial analysis, the study identifies patterns in accident data, highlighting critical areas for improvement. Recommendations are developed based on these insights to propose cost-effective strategies that prioritize safety, such as optimized pavement materials, better signage, and improved road design. This research underscores the role of systematic traffic safety analysis in advancing safer, more resilient transportation systems. This project undertakes a comprehensive traffic safety analysis aimed at reducing accidents and improving road safety through a detailed examination of key factors affecting driving conditions. By focusing on parameters such as skid resistance, pavement surface texture, visibility, road geometry, and traffic flow, the study seeks to identify elements that significantly impact accident rates and driving safety.

Study of Evaluation of Kraft Lignin and Wood-Based Modifiers in Mitigating Rutting in Porous Asphalt Concrete
Authors:-Mrs. M. Gowri, Allada Ravindra

Abstract-This study explores the potential of Kraft lignin and wood-based additives to mitigate rutting in porous asphalt concrete (PAC), a material widely used for its water permeability and noise-reducing properties. PAC, however, suffers from rutting, a type of pavement distress that leads to deformations and reduced performance under traffic loads. The research evaluates the impact of incorporating Kraft lignin and wood-based modifiers into PAC to enhance its rutting resistance. Experimental investigations, including wheel-tracking and Marshall stability tests, were conducted on asphalt samples with varying concentrations of these modifiers. Results indicated that both Kraft lignin and wood-based additives significantly improved rutting resistance, with lignin contributing to greater binder stiffness and wood additives enhancing aggregate bonding. These findings suggest that bio-based modifiers could offer a sustainable solution to improving the durability of porous asphalt pavements, reducing maintenance costs and environmental impact.

DOI: 10.61137/ijsret.vol.10.issue6.365

Automation and Control Systems for Lifting Bridges
Authors:-Dr. B. Raghunath Reddy Professor, Avula Gurappa, Tupakula Harinath, Danduboina Sivanjaneyulu, D. Ganga Amrutha

Abstract-Lifting bridges, also known as movable bridges, are crucial for enabling both road and maritime traffic, especially in regions where waterways intersect with busy transportation corridors. These bridges, including types such as bascule, swing, and vertical lift bridges, allow for efficient passage of vessels while maintaining road connectivity. Research into lifting bridges spans a range of disciplines, from structural engineering and materials science to automation and environmental impact studies. One primary focus is on the design and mechanics of movable bridges, with emphasis on the structural integrity, materials, and load-bearing capacities of these complex systems. Innovations in materials science have led to the exploration of corrosion-resistant alloys and high-performance composites, improving the durability and lifespan of lifting bridge components. Additionally, advanced automated control systems are becoming increasingly important, with research on robotic mechanisms and smart sensors aiming to streamline bridge operations and enhance safety. These innovations are complemented by studies into the impact of lifting bridges on traffic flow, which examine the operational challenges and disruptions posed by the periodic lifting and lowering of bridges. Another key area of research involves the environmental impact of lifting bridges. Studies have been conducted on the ecological effects of bridge operations on aquatic ecosystems, particularly in relation to waterway traffic and habitat disruption. Moreover, with the rise of sustainable infrastructure, researchers are exploring ways to reduce energy consumption and carbon footprints associated with the mechanical lifting process. Further, lifting bridges present unique challenges in extreme environments, such as those found in cold and hot climates, where materials and mechanisms face additional stresses due to thermal expansion, corrosion, or ice formation.

Fabrication and Simulation of Multi-Purpose Agriculture Machine
Authors:-Mullu Pavani, Peda Baliyara Simhuni Indhu, Yendamuri Venkataramana, Potnuru Dileep, Thota Tirumala Srinivas Manjunath, Assistant Professor Dr. Gorti Janardhan

Abstract-The machine is a double-purpose unit proposed to chop and crush forage crops in an efficient way, to cut down on waste and inefficiency in agricultural practices. It discusses evaluation related to the performance of the machine, with emphasis on its productivity in trimming different forages. The study discusses the advantages the use of this machine would bring about, such as minimum labor costs and efficient crop management. Testing results show that the machine achieves the basic standards of operation for agricultural purposes. The main objective of the project was to develop a machine that efficiently performs chopping and crushing work simultaneously with the ability to overcome the weaknesses of machines that can only perform the two functions separately. This multi-purpose functionality aims at increased productivity and saving on operational costs. An increased need for environmentally friendly economical machines capable of delivering agricultural needs effectively, therefore, is essential to achieve economic sustainability.

Online Chatbot Based Ticketing System
Authors:-Priya Kumari, Shruti Kumari, Simran Jaiswal, Siddhant Chaturvedi, Sahil Kumar Jha, Pratham Chaturvedi

Abstract-Chatbots function as software that enables users to ask questions and receive assistance through appropriate responses. This paper explores an AI-based chatbot designed to serve as an online ticketing system, streamlining the process of issue reporting, resolution, and user assistance across various domain. It also includes features like customer support, IT helpdesks, and event management. Natural language processing (NLP) is used by this proposed chatbot to understand user queries, categorize tickets, and provide instant responses. The aim of this chatbot is to enhance efficiency, reduce response times, and improve user satisfaction.

DOI: 10.61137/ijsret.vol.10.issue6.330

Hybrid Approaches in AI and Soft Computing: The Future of Intelligent Systems
Authors:-Ramprasath K, Dr. Subitha S

Abstract-Artificial Intelligence (AI) has become a pivotal technology for automating complex processes, while Soft Computing provides innovative ways to manage imprecise and uncertain data. By combining the two, hybrid systems leverage the strengths of AI’s precision and Soft Computing’s adaptability. This paper delves into the principles behind these hybrid models, emphasizing their use in healthcare, autonomous systems, finance, and smart cities. It also highlights the challenges of scalability and interpretability and outlines potential research directions, including integrating quantum computing and promoting explainable models.

DOI: 10.61137/ijsret.vol.10.issue6.331

Industrial Production Productivity Analysis with Respect to Labors
Authors:-Research Scholar Sachin Kachhi, Assistant Professor Ranjeet Singh Thakur

Abstract-Low productivity of workers is the most significant factor behind delivery slippages in manufacturing industries. As manufacturing is a laborer predominant industrial sector, this paper focuses on worker output and their efficiency in the manufacturing sector. It covers the definitions of productivity, efficiency of the workers, its perspectives and the factors influencing the productivity. Proposed ANOVA method optimize performance of productivity and worker production parameters. Also observed more sensible case to increase production productivity.

Intelligent Traffic Management System for Urban Conditions
Authors:-Satyraj Madake, Kopal Naramdeo, Janhavi Patil, Priti Patil

Abstract-The challenges of urban areas with ever-increasing traffic congestion, emergency response, and maintaining road safety are the basis of this paper. The ITMS proposed in this paper treats optimization of timings at the traffic signals based on real-time vehicle counts, along with the detection of emergency vehicles and accidents, as its prime mandate. To achieve these objectives of optimal traffic management, advanced technologies, such as sensor detectors, algorithms for processing data, and communicating networks, were adopted. With simulations and evaluations, the ITMS holds great promise in enhancing traffic flow efficiency as well as reducing congestion while shortening emergency vehicle response times vis-a-vis fixed-time signal control. The research performed here addresses the development of more sustainable and resilient urban transportation systems.

DOI: 10.61137/ijsret.vol.10.issue6.332

Design and Analysis of Shaft for Electric Go-Kart Vehicle
Authors:-Dr. B. Vijaya Kumar, L. Manoj Kumar, G. Ashok, D. Jithendar

Abstract-This study focuses on the design and analysis of a hollow shaft for an EV go-kart, optimizing weight reduction and structural integrity. Using SolidWorks for design and ANSYS for Finite Element Analysis (FEA), the shaft’s performance under mechanical stresses and cyclic loads was evaluated. Results demonstrated significant weight savings while maintaining strength, rigidity, and durability, enhancing the go-kart’s efficiency and reliability. This work highlights the potential of hollow shafts in improving EV performance through lightweight design.

DOI: 10.61137/ijsret.vol.10.issue6.333

Colourization of SAR Image Using Generative Adversarial Network
Authors:-Dr. D. Suresh, P. Rakshitha, V. Manasa Aparna, V. Chaitanya Sai Kumar, S. Vamsi Krishna

Abstract-Employing generative adversarial networks, specifically with regard to cycle consistency loss and mask vectors, mainly concentrates on the colorization of Synthetic Aperture Radar (SAR). Most SAR imagery is devoid of chromatic information. Contemporary deep learning techniques are the predominant approach for SAR colorization. The methodology proposed herein employs a multidomain cycle-consistency generative adversarial network (MC-GAN). It enhances performance through the integration of a mask vector and cycle-consistency loss. The approach does not necessitate the availability of paired SAR-optical imagery. The multidomain classification loss contributes to the precision of the color output. The methodology has been evaluated using the SEN1-2 dataset for urban and terrain areas.

DOI: 10.61137/ijsret.vol.10.issue6.334

FairShare – A MERN Stack Solution for Ride Sharing
Authors:-Atharva Tupe, Aditya Gaikwad, Rohan Soni, Vivek Chhonker

Abstract-The cost of commuting to and from school is a burden for many people, especially in urban areas. While ride-hailing services are popular worldwide, most students face issues with accessibility and convenience. The aim of this work is to create and use fairShare. A web platform that allows students to connect and share rides, thereby reducing transportation costs and reducing the environment around them. Users can register, post trips,and compete with other students using the same route. Early tests of the platform have shown that it reduces student travel costs and provides a good user experience. The platform also promotes sustainable practices for students. fairShare demonstrates the potential of student-friendly carsharing to reduce transportation costs and improve social interaction. The platform has the ability to measure a broader and more effective way for students to take action.

DOI: 10.61137/ijsret.vol.10.issue6.335

Review: Cyber Insight – Illuminating Cyber Security for all
Authors:-Ayush Kore, Kushal Hirudkar, Palak Jaiswal, Shravani Ambulkar, Shaarav Kamdi, Shalini Kumari

Abstract-With the advent of the “e-” revolution starting in 2000, the issue of cyber security, cyber-attacks and cyber threats which included domains, but not e-business, e-government, e-; commerce etc. only occurred because for the issue of cybersecurity in e- learning is under-explored, the aim of this paper is to present methods that focus on monitoring cybersecurity issues related to e- learning processes on. In addition, this article aims to present some good examples of cybersecurity management strategies in e- learning and cybersecurity trends in this area.[2] This paper will present possibilities for increasing information security and cyber- security awareness in education and e-learning that will inspire future cybersecurity professionals to navigate their career path.[3].

DOI: 10.61137/ijsret.vol.10.issue6.336

Elephant Herd Feature Optimization Based Intrusion Detection System
Authors:-Shivani Meena, Assistant Professor Rani Kushwaha, Professor Jayshree Boaddh

Abstract-The growing dependence on technology for a wide range of activities has dramatically increased computational demands, driving significant growth in computer network usage over the past few decades. This surge in demand for processing and storage capabilities has opened up business opportunities for companies but has also drawn the attention of cybercriminals. In response to these threats, researchers have developed various attack detection and prevention models. This paper introduces a new intrusion detection model that operates in two phases. The first phase involves building a feature ontology to train a convolutional neural network (CNN), and the second phase tests the trained model. For feature selection, the model uses an Elephant Herd Optimization-based genetic algorithm, which efficiently identifies a strong feature set for classifying network sessions. Experiments on a real-world dataset show that the proposed model can detect various types of attacks within normal sessions. Results demonstrate improved accuracy and performance metrics compared to existing models.

Random Forest Based Edge Load Balancing of IOT Devices
Authors:-Swati Jat, Assistant Professor Rani Kushwaha, Professor Jayshree Boaddh

Abstract-IoT device-based communication boosts monitoring, business operations, and daily activities but also increases the load on servers and clouds. To handle this, edge computing acts as an intermediary layer. Efficient job management is critical for large-scale IoT networks, but existing models often fail to adapt based on past job sequences. This work introduces a model using a modified wolf Optimization algorithm to dynamically balance loads without prior training. It also incorporates a Random Forest model to generate initial job sequences. Experiments show that the proposed approach reduces job makespan time and enhances edge resource utilization compared to other models.

Summraize: Smart Meeting Assistant for Automated Summaries
Authors:-Assistant Professor Karmbir Khatri, Swastik Goomber, Sushil Verma, Shivam bansal, Piyush

Abstract-Virtual meetings have become an essential mode of communication in contemporary professional environments. However, the fast-paced nature of virtual meetings undermines the ability to remember critical information accurately as even making notes is an imperfect mundane task, manual note-taking is both time- consuming and error-prone, often resulting in overlooked decisions and action items. SummrAIze is an AI-powered meeting assistant designed to address these challenges by automating the transcription, [1]summarization, and extraction of actionable insights during virtual meetings on platforms like Google Meet and Microsoft Teams. Using advanced machine learning algorithms, SummrAIze produces real-time summaries, highlights key points, and identifies action items, enabling participants to engage fully in discussions without sacrificing documentation accuracy. Integrated with productivity tools, SummrAIze not only reduces manual effort but also ensures that all essential information is recorded and accessible, enhancing team collaboration and workflow continuity. This paper presents the design, methodology, and potential impact of SummrAIze, a tool that redefines productivity in the context of virtual meetings.

DOI: 10.61137/ijsret.vol.10.issue6.337

Raman Spectroscopy: Diagnostic Tool for Cancer Cell Identification
Authors:-Rakshit pandey, Deepak Rawat, Professor Himmat singh

Abstract-Non-destructive spectroscopic techniques represent the top-choice for any kind of process monitoring . Among all of the available techniques, Raman spectroscopy is one of the most solid and versatile tools to analyze several materials, both in lab and on-field conditions . Raman analysis has grown, reaching several industrial sectors such the food and textiles sectors .Raman spectroscopy displays several advantageous features over other techniques like infrared spectroscopy. For example, the quality of the signal collected is barely affected by the presence of water, allowing for use in plenty of applications where infrared analyses are not reliable . A representative case study is the in-situ monitoring of a fermentative process where Raman techniques outperformed any other spectroscopic approach .Molecular-level tissue characterization is highly potent for cancer diagnosis. As a tissue starts becoming cancerous, specific biomolecules are overexpressed or aberrantly expressed, which can be used as cancer molecular markers. If we can detect these molecular markers spectroscopically, it would lead to a new molecular-level cancer diagnosis with high objectivity.

From Survival to Thriving: AI-Powered Pathways for Homeless Children’s Adoption and Healing
Authors:-Syeda Aynul Karim, Md. Juniadul Islam, Mir Faris

Abstract-The plight of homeless children remains one of the most urgent global challenges, with millions of vulnerable children deprived of basic human rights such as shelter, healthcare, and education. Despite the rapid advancement of technology, child welfare systems in many developing countries still face significant hurdles, marked by inefficiencies and fragmented services. This paper proposes an innovative AI-driven system for adoption and rehabilitation that aims to address these systemic challenges holistically. By harnessing cutting-edge artificial intelligence (AI) algorithms, the system streamlines the adoption process, delivers personalized healthcare recommendations, and optimizes resource allocation for child welfare organizations. Through the integration of predictive analytics, data-driven decision-making, and a robust ethical framework, the system ensures transparency, fairness, and scalability. Early simulations and case studies highlight the transformative potential of AI in enhancing adoption success rates and improving healthcare outcomes for homeless children. The findings emphasize the system’s ability to drive meaningful improvements in global child welfare efforts, offering a scalable, ethical solution that can have a lasting impact on vulnerable children worldwide.

DOI: 10.61137/ijsret.vol.10.issue6.338

Smart Shields against Cyber Threats: Machine Learning-Driven Phishing URL Detection
Authors:-Syeda Aynul Karim, Md. Juniadul Islam, Ishtiaq Hoque Farabi

Abstract-Phishing attacks remain a prevalent cybersecurity threat, exploiting vulnerabilities in digital platforms to compromise sensitive user data. This paper introduces a novel machine learning-based framework for phishing URL detection, combining advanced feature engineering techniques and classification algorithms. By integrating lexical attributes, WHOIS data, and ranking metrics like PageRank and Alexa Rank, our approach enhances detection accuracy and minimizes false positives. Experimental results demonstrate superior performance across classifiers, achieving an accuracy of 99.8% using Support Vector Machines. The framework’s modular design ensures adaptability to evolving phishing tactics and scalability for enterprise deployment. This research lays the foundation for future advancements in AI-driven cybersecurity solutions.

DOI: 10.61137/ijsret.vol.10.issue6.339

Virtual Security Realized: An In-Depth Analysis of 3D Passwords
Authors:-Md. Juniadul Islam, Syeda Aynul Karim, Ishtiaq Hoque Farabi

Abstract-The demand for robust authentication systems has risen significantly as cyberattacks become increasingly sophisticated. Current authentication mechanisms, such as textual passwords, biometrics, and graphical systems, each have unique vulnerabilities. This research explores the concept of a 3D password system, which integrates various authentication schemes into a virtual 3D environment to enhance security. The system allows users to interact with objects in a 3D space, forming unique and complex passwords based on sequences of interactions. This paper elaborates on the system’s design, implementation, and potential applications in critical and non-critical systems. Detailed analyses reveal that the 3D password provides superior resistance to timing attacks, brute force attempts, and well-studied schemes, while maintaining user-friendliness. Future research avenues include the incorporation of AR/VR and IoT technologies to further expand the utility of the 3D password system.

DOI: 10.61137/ijsret.vol.10.issue6.340

Enhanced Flower Recognition via Transfer Learning with ResNet-50
Authors:-Syeda Aynul Karim, Md. Juniadul Islam

Abstract-This paper proposes a flower recognition system using transfer learning with the ResNet-50 architecture. By utilizing pre-trained weights from ResNet-50, the system classifies ten species of flowers, drawing on an extended dataset with over 8,000 labelled images. The study addresses challenges in deep convolutional neural networks, such as overfitting and local optimality, by fine-tuning the ResNet-50 model. Initially, only the final layers of the model are retrained on the flower dataset, while the pre-trained layers remain frozen. After achieving initial convergence, all layers are unfrozen for full model fine-tuning. The dataset is divided into training, validation, and test sets to evaluate the model’s performance, which is measured using accuracy, and F1-score. The experimental results demonstrate that the transfer learning approach significantly improves classification accuracy and generalization, outperforming traditional methods. This approach proves especially effective in handling visually similar flower species and diverse environmental conditions. The study highlights the potential of transfer learning in enhancing the efficiency and robustness of flower recognition systems, contributing to broader applications in image classification tasks.

DOI: 10.61137/ijsret.vol.10.issue6.341

Shoe Theory: Embracing Individual Differences in Management
Authors:-Arjita Jaiswal, Manish Chaudhary

Abstract-The concept of Shoe Theory emphasizes that everyone is comfortable in their own shoes and should not be forced to wear someone else’s shoes. This theory posits that individual differences, including the effects of various elements such as time and generational perspectives, significantly impact workplace dynamics and organizational effectiveness. The theory highlights the importance of recognizing the unique experiences and backgrounds of team members to foster an inclusive and productive environment. Keeping creative destruction in mind, everything has its loophole to be breached. Although the answer may be yes or no, there always exists a condition of if/situation and but/exception.

DOI: 10.61137/ijsret.vol.10.issue6.342

Optimizing k for k-NN: A Polynomial Regression Approach
Authors:-Pari Gupta, Sparsh Shukla, Dr. Shalini Lamba

Abstract-The k-Nearest Neighbors (k-NN) algorithm is a widely used non-parametric method for classification tasks, where the selection of the optimal value of k (the number of neighbors) plays a critical role in model performance. Traditional methods for selecting k, such as cross-validation or heuristic approaches, can be time-consuming and computationally expensive. This paper proposes an alternative approach to determining the optimal k for k-NN using polynomial regression. By treating the relationship between the value of k and the performance metric (such as classification accuracy) as a continuous function, we use polynomial regression to model this relationship and identify the k that results in the best performance. The polynomial regression model is trained on a set of performance data for different values of k, allowing for a smooth and accurate estimation of the optimal k across various datasets. Our experimental results demonstrate that the polynomial regression-based approach provides an efficient and effective method for selecting k, outperforming traditional techniques and reducing the computational cost associated with hyperparameter tuning. The proposed method also offers several advantages over traditional hyperparameter optimization techniques. By modelling the performance of k-NN as a continuous function of k, polynomial regression avoids the need for exhaustive grid search or cross-validation, making it particularly suitable for scenarios where computational resources are limited or time is constrained. Furthermore, the flexibility of polynomial regression allows for capturing complex, non-linear relationships between k and model performance, which can lead to more accurate predictions of the optimal value. Our approach is demonstrated one dataset, where it not only achieves higher accuracy but also reduces the overall time spent on model selection, making it a practical and scalable solution for hyperparameter tuning in machine learning applications.

A Review Paper on Alumni Portal
Authors:-Ansari Ayaan Najmul Kalam, Shaikh Aliya Ambreen, Khan Abdul Rehman Mohammed Mukhtar

Abstract-This paper reviews current research on Alumni Portal, the connections between alumnus and students, college interaction between alumnus, past records, event updates and records. The review covers 30 research papers, investigating database of Alumnus, students, past and present events held, interaction of alumnus in college events, interaction of alumnus and students. For improving the previous Alumni portals and projects related to Alumni.

DOI: 10.61137/ijsret.vol.10.issue6.343

AR Storytelling Application
Authors:-Sakshi Davkhar, Sreya Kurup, Dipali Sanap

Abstract-This paper explores the transformative potential of an Augmented Reality (AR) storytelling application designed to enhance traditional storytelling methods by integrating interactive digital animations, text, and audio into physical environments. The app offers a dynamic and immersive experience, particularly for children, by enabling real-time interaction with animated characters, voice narration, and engaging, interactive scenes. Unlike static books or conventional digital content, this app allows users to actively participate in the narrative, creating a more engaging and educational experience. By overlaying digital elements onto the real world, the app fosters increased interactivity and encourages deeper emotional and cognitive engagement with the story. Children can interact with animated characters, explore rich 3D environments, and receive instant feedback through audio cues and animations that respond to their actions. The app also supports educational growth by offering interactive learning modules, promoting reading comprehension, and allowing customization of story elements to accommodate multiple learning styles. The application leverages cutting-edge AR technologies to transform traditional narratives into immersive experiences, providing both entertainment and educational value. By integrating AI-driven components for voice recognition and dynamic content generation, the app can offer personalized experiences and adaptable content based on user preferences and interactions. This survey examines the underlying technologies and design choices that contribute to the app’s ability to engage users, as well as the broader implications of AR in storytelling for enhancing educational tools and creative learning platforms.

DOI: 10.61137/ijsret.vol.10.issue6.344

The Impact of Robotics on Modern Manufacturing
Authors:-Rithwik Agarwal

Abstract-This paper dives into how robotics is transforming manufacturing today. It looks at how robots are making processes faster, safer, and more efficient while also tackling some challenges like high costs and technical complexity. By exploring industries like automotive and consumer goods, and through examples from companies like Toyota and Unilever, the paper highlights both the advantages and limitations of using robots. It also touches on important issues like job impacts and cybersecurity risks, suggesting that thoughtful planning is essential for making the most of robotics in manufacturing.

DOI: 10.61137/ijsret.vol.10.issue6.345

Mechanical Engineering Innovations in Transportation
Authors:-Rithwik Agarwal

Abstract-This paper examines the pivotal role of mechanical engineering in advancing transportation through innovations like electric vehicles, lightweight materials, and dual-fuel systems. It highlights their impact on sustainability, efficiency, and safety while addressing challenges such as costs, regulations, and public acceptance. Emerging technologies like Hyperloop and hydrogen propulsion are also explored, emphasizing their potential to redefine global mobility.

DOI: 10.61137/ijsret.vol.10.issue6.346

Diabetes Prediction Using Neural Network
Authors:-Anand Singh, Vedant Urkudkar, Ruchi vairagade, Ketaki Punjabi

Abstract-Diabetes is one of the most frequent diseases worldwide where yet no remedy is discovered for it. Every year a great deal of money has to be spent for caring for patients with diabetes. Therefore, it is crucial that prediction should be very accurate and a very dependable method must be adopted for doing so. One of these methods is the use of artificial intelligence systems, and in particular, the use of Artificial Neural Networks, or ANN. So, in this paper, we used artificial neural networks in order to predict whether or not a person has diabetes. The criterion was to minimize the error function in neural network training with the help of a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 70%

DOI: 10.61137/ijsret.vol.10.issue6.347

Image Manipulation Web Application: A Next JS Implementation
Authors:-Assistant Professor Ms. Priyanka Kapila, Mr. Mayank Kumar Grade, Mr. Shubham, Mr. Himanshu Shahoo

Abstract-The enhancement in web technologies has contributed to the evolution of web applications that are very dynamic and engaging. This research work focuses on the creation of an online image editing application that is based on cloud infrastructure and modern web layouts/development tools such as Next.js, TailwindCSS, and Cloudinary’s APIs, among other resources, to deliver advanced image editing features. The application incorporates Clerk to allow users to create login accounts and easily register, while data is managed using MongoDB to facilitate the security of users and edited pictures across several devices. Necessary and basic features such as object removal, editing backgrounds, recoloring pictures, restoring, and changing the size of images are handled within the cloud and therefore benefit the functionality of the application and users as well. In addition, a contact form utilizing EmailJS has been integrated to enable communication with users. This research work highlights the legitimacy of cloud-based solutions as well as their expanded geographic reach in catering to an advanced user experience within image editing applications, thus supporting the growth of cloud computing and web technology.

DOI: 10.61137/ijsret.vol.10.issue6.348

Automatic Text Summarisation
Authors:-Sahil Damke, Shreya Telang, Nidhi Tadge, Sanskruti Burkule, Professor Manisha Mali

Abstract-Due to the large amount of information generated every day, automatic writing is an important part of knowledge management. The discipline has made great progress, especially with the emergence of abstraction, abstraction and hybrid content models. In the extraction method, the main idea is preserved by selecting the main sentence or phrase from the text, while in the abstraction method, all the information is repeated to create new sentences. As the name suggests, hybrid models include the features of both extraction and abstraction systems to get the best of both approaches. However, issues remain, particularly in how to address the authenticity, coherence, and length of the text. This article examines the current state of writing concepts and topics in practice and future research.

DOI: 10.61137/ijsret.vol.10.issue6.349

Car Surveillance System
Authors:-Kushagra Paliwal, Mohit Verma, Nilesh Panchal

Abstract-This study introduces the Car Surveillance System (Driver Negligence and Dissuader System), integrating advanced lane detection, drowsiness detection, pedestrian detection, and object detection technologies to boost road safety. Much like the luggage storage website, it presents a user-friendly interface and real-time alerts to avert accidents. Intelligent functionalities ensure efficacy and security, simplifying driving experiences and encouraging hassle-free travel. Tailored settings and transparent pricing cater to individual driver requirements, tackling prevalent challenges and nurturing safer roads for all users.

DOI: 10.61137/ijsret.vol.10.issue6.350

Weapon Detection Using Yolo
Authors:-1Assistant Professor Ms. Monika, Nikhil Tiwari

Abstract-In light of the increasing gun violence incidents worldwide, there is a pressing need for automated visual surveillance systems capable of detecting handguns. This paper presents a method for real-time handgun detection in video streams using the YOLO algorithm, comparing its performance in terms of false positives and false negatives against the Faster CNN algorithm. To enhance detection accuracy, we compiled a custom dataset featuring handguns from various angles and merged it with the Roboflow dataset. The YOLO model was trained on this combined dataset and validated using four different videos. The results indicate that YOLO effectively detects handguns across diverse scenes, demonstrating superior speed and comparable accuracy to Faster CNN, making it suitable for real-time applications.

DOI: 10.61137/ijsret.vol.10.issue6.351

Appointify: Doctor Appointment Booking System
Authors:-Assistant Professor M Ayush, Mr. Pawan Bhatt

Abstract-The field of healthcare is turning more towards tools to improve access, to services and make the experience better for patients and providers alike. A specific example is “Appointify,” a web platform for booking doctor appointments that was created using the MERN technology stack— MongoDB, Express.js, React and Node.js—with a goal of simplifying the appointment process and connecting patients, with healthcare professionals seamlessly. This document provides an outline of “Appointify ” a system created to tackle the issues encountered in appointment handling like extended waiting periods and disorganized scheduling well as the absence of efficient communication, between patients and healthcare providers.”Appointify” allows patients to search for doctors based on their expertise area request appointments access their history and update their profiles. It also equips doctors with functions to control their availability, schedule appointments. Engage with patients effectively. The platform includes functions such, as role based access control for security measures and encryption to safeguard data privacy It also features responsive design for user friendly interaction, on various devices

DOI: 10.61137/ijsret.vol.10.issue6.352

AI-Driven Portable Device for Authenticating and Identifying Denominations for the Visually Impaired
Authors:-Assistant Professor Ms. Suman, Ms. Surbhi, Mr. Shishir Gupta

Abstract-In this research paper we have proposed a device that helps visually impaired people recognise currency denomination in order to detect the denomination of Indian currency. The members of this community have challenges particular to them when it comes to dealing with money, and as such there is an ever-growing need for quick and accurate identification tools appropriate for their scenario. We describe the process we have followed to develop the device, offering a blend of image processing and machine learning to allow currency identification in real time. Surveys of potential users revealed important preferences and needs for accessibility and ease of use, guiding the design of a new device system. According to test results, the device achieves high accuracy in denominations recognition and effective user satisfaction, demonstrating a potential device providing financially independent life for visually impaired users. These findings underscore the value of blending cutting-edge technology with user-centered design to create impactful solutions for underserved communities. The paper hence concludes with recommendations for the further enhancements and future research to expand the device’s features and accessibility.

DOI: 10.61137/ijsret.vol.10.issue6.353

Device to Measure Gas Cylinder Level Using Internet of Things (IoT)
Authors:-Anup kumar, Anand Prakash, Anek Singh, Rupesh Anand, Shivam Badkur, Assistant Professor Ambika Varma,

Abstract-This system is designed to solve a common problem: running out of gas without knowing when it’s about to happen. The system keeps track of how much gas is left in the container by continuously checking its weight. If the gas is running low, it can automatically place a new gas order using the Internet of Things (IoT) technology. A device called a load cell is used to measure the weight of the gas container, and this data is sent to an Arduino Uno (a small computer) to compare with a standard weight. If the gas is low, the system sends a message to the user via SMS, using a GSM modem. For safety, the system also has sensors to detect gas leaks (MQ-2 sensor) and monitor the surrounding temperature (LM35 sensor). If any unusual changes are detected by these sensors, such as a gas leak or a sudden change in temperature, a siren will sound to alert the user.

DOI: 10.61137/ijsret.vol.10.issue6.354

Liver Damage Prediction: Using Classification Machine Learning Models
Authors:-Assistant Professor Ms. Rekha Choudhary, Mr. Himanshu Sharma, Mr. Yash Vachhani

Abstract-Liver diseases like cirrhosis and hepatitis are major causes of global morbidity and mortality, highlighting the need for early detection. Traditional diagnostic methods often identify liver damage at later stages, limiting preventive interventions. This study develops a machine learning model to predict liver damage earlier using clinical features and lab results. By analyzing a data-set with patient demographics and biochemical markers, we apply machine learning algorithms, including Random Forest, Decision Tree, and Logistic Regression, and evaluate their performance using metrics like accuracy, precision, recall, F1 score, and ROC-AUC. The Random Forest model outperformed others, showing high accuracy and robustness. Feature importance analysis revealed critical clinical factors, such as serum bilirubin and liver enzymes, in predicting liver damage. These results suggest that machine learning, especially Random Forest, could aid in the early detection of liver disease, improving patient outcomes. Future work will focus on using larger, more diverse data-sets and advanced models to improve predictive accuracy.

DOI: 10.61137/ijsret.vol.10.issue6.355

Reliable Machine Learning and Intelligent Computing for Complex Financial Systems
Authors:-Associate Professor Nagaraj Gadagin, Assistant Professor Anita Kori

Abstract-Financial systems have become more complicated than ever before due to their fast growth, which calls for creative methods of managing, analyzing, and forecasting system behavior. In order to solve problems in intricate financial systems, this study investigates the use of intelligent computing and trustworthy machine learning models. The goal of the project is to improve decision-making, risk assessment, and anomaly detection in dynamic financial contexts by fusing cutting-edge computational techniques with reliable AI frameworks. The dependability and interpretability of machine learning models are given special attention in order to make sure they satisfy the exacting standards of accuracy and transparency that are necessary for financial stakeholders. The implications of these technologies for reducing systemic risks and enhancing operational effectiveness are also covered in the study. This study demonstrates the revolutionary potential of intelligent computing and reliable machine learning in creating robust and flexible financial ecosystems via case studies and experimental validations. The results highlight how important they are in determining how finance and economic stability develop in the future.

Liver Disease Recognition Using Machine Learning
Authors:-Atharva Tupe, Suraj Gandhi, Rajesh Prasad

Abstract-For more effective treatment, early diagnosis of liver disease is crucial. Detecting liver disease in its early stages is challenging due to its subtle symptoms, often becoming apparent only in advanced stages. This research leverages machine learning techniques to address this issue by enhancing liver disease detection. The primary objective is to differentiate between liver patients and healthy individuals using classification algorithms. Liver disease has seen a global increase in prevalence in the 21st century, with nearly 2 million annual deaths attributed to it according to recent surveys. It accounts for 3.5% of global deaths [1]. Early diagnosis and treatment can significantly improve outcomes for patients with chronic liver disease, which is among the most fatal illnesses. The advancement of artificial intelligence, including various machine learning algorithms like Regression, Support vector machine, KNN, and Random Forest, offers the potential to extend the lifespan of individuals with Chronic Liver Disease (CLD).

DOI: 10.61137/ijsret.vol.10.issue6.356

Concurrency and Synchronization: Detection, Reasons, Tools and Applications
Authors:-Govind Khandelwal, Shriram Sonwane, Sachin Ware

Abstract-Concurrency and Synchronization in digital electronics where algorithms are use to comprehend the all the calculations for work. Digital machines ranging from Embedded Systems, IOT, Computers, Smartphones, Servers and Networking systems. Synchronization has became a very crucial part of basic programs running in the background of any operating system, that is the “Kernel”. These algorithms are the basic part of the OS for its smooth working in multi-programming, load balancing, time synchronization, data I/O ops within and out of the system, parallel computing with GPUs, I/O ops with IOT and cloud systems, Network and data security, mathematical calculations, etc. Synchronization programs are used to prevent conditions such as data races, deadlock, network latency, data corruption, manipulation and many more. Conditions created by these bugs can be visible or invisible in the user space. This Research paper is a comprehensive analysis on Concurrency and Synchronization. Source code examples of such conditions are given below from the original source code of some of the common linux distros. Applications of solutions to some of these issues in programs and systems to help progress for development of the performance and results.

DOI: 10.61137/ijsret.vol.10.issue6.357

Dynamic Ride Pricing Model Using Machine Learning
Authors:-Assistant Professor Ms. Preeti Kalra, Mr. Jitesh Pahwa, Mr. Anirudh Sharma, Mr. Dev Malhotra, Mr. Kunal Pandey

Abstract-Dynamic Ride Pricing is a vital feature in the ridesharing industry that allows companies to adjust ride fares based on shifts in supply, demand, weather conditions, and other relevant factors. This study details the development of a machine learning-driven dynamic pricing model designed to optimize fare adjustments in real time. By analyzing key variables such as trip distance, weather, and historical patterns of supply and demand, the algorithm can deliver pricing that is both contextually relevant and responsive. The model aims to achieve a balance between profitability and customer satisfaction by swiftly adapting to fluctuating market conditions. Leveraging advanced machine learning techniques, it ensures pricing that is not only accurate but also fair and responsive. By integrating these factors into a unified pricing strategy, the model provides an optimized solution that enhances operational efficiency and meets consumer needs, ultimately contributing to a more equitable and efficient pricing system in the ridesharing sector.

DOI: 10.61137/ijsret.vol.10.issue6.358

Ship with Windmill
Authors:-Pasinipali Balaji Prasad

Abstract-The use of wind power and conversion into energy, methodology regarding implementation of the idea, Advantages and Disadvantages and the scope for future.

DOI: 10.61137/ijsret.vol.10.issue6.359

Enhancing Real-World Experiences: A Study on Augmented Reality Technology
Authors:-Assistant Professor Mahesh Tiwari, Ayush Kumar Gour, Syed Murtaza Hasan Rizvi

Abstract-Augmented Reality, also known as AR technology, is a tool that employs computer graphics to superimpose a different layer of information onto the real world. Traditionally, virtual reality provided more interactive experiences when compared with other methods. In this paper, we explore the current state and future prospects of AR with a focus on its application in sectors such as medicine, education and retail among others. The functioning mechanisms of AR systems; sensors involved, processing algorithms required, rendering techniques for visual output and user interaction are discussed along with recent innovations like improved AR hardware or mobile applications. A literature review has been done to illustrate how AR enhances engagement in education, assists surgeons enhance precision during operations, changes customer experience in retail shops and provides entertainment through immersiveness. Moreover, AR technologies are also being explored for use in sectors such as tourism, automotive, and manufacturing, where they have the potential to revolutionize customer service, design processes, and workflow management.But there are obstacles that still hinders growth of AR such as technical barriers, privacy issues and expensiveness . Additionally, it discusses ways to overcome these challenges while pointing out things to research on so that maximum utility of AR can achieve. In conclusion, we find out that AR has great potential to alter different industries since it leads to more practical applications and encourages ongoing innovation.

DOI: 10.61137/ijsret.vol.10.issue6.360

Chronic Kidney Disease Prediction Using Federated Learning
Authors:-Assistant Professor Mrs.Suje.S.A, Chinmaya.S, Harini.S

Abstract-Chronic kidney disease (CKD) is a global health challenge, affecting millions of individuals and often leading to kidney failure when not detected early. The application of machine learning (ML) for CKD prediction has gained significant attention, enabling timely diagnosis using clinical data. This paper explores various ML techniques used for CKD prediction, focusing on preprocessing challenges such as missing data, data imbalance, and feature selection. Additionally, the paper discusses the emerging role of Federated Learning (FL), a decentralised approach to ML that allows for privacy-preserving collaborative model training across institutions.

DOI: 10.61137/ijsret.vol.10.issue6.361

Streamlit Powered Multi-Disease Prediction with Machine Learning
Authors:-Minal Dhankar

Abstract-Machine learning techniques are doing wonders in every sphere of life but using predictive analysis in healthcare is a challenging task. However, if implemented properly these techniques help in making timely judgements about the health and treatment of patients. Globally, diseases including diabetes, heart disease, and breast cancer are major causes of death; yet, the majority of these deaths are due to failure to have regular checkups for these conditions. Low doctor-to-population ratios and a lack of medical infrastructure are the root causes of the above-mentioned issue. Thus, early detection and treatment of these diseases can save many lives. Machine Learning, Deep Learning and Streamlit is an effort concentrated on the development of healthcare using in-depth engines to forecast several sicknesses. Streamli Cloud and Streamlit Library facilitate deployment of prediction models like a breeze for developers. This has made accessing and using prediction capabilities of the system easily done by any layman. The paper focuses on forecasting three major diseases namely diabetes, heart failure and Parkinson’s disease by using an advanced ensemble of deep learning models as well as traditional machine learning techniques. Then again, merging Support Vector Machine (SVM) algorithm together with Logistic Regression models will form one such integration scheme.

DOI: 10.61137/ijsret.vol.10.issue6.362

Intelli Search: Dual API-Powered Search Platform
Authors:-Assistant Professor Mr. Ayush, Mr. Amarjeet, Mr. Prakash Rai, Mr. Bhupender

Abstract-The goal of the web-based search engine “Intelli Search” is to give users accurate and pertinent content by combining personalized video recommendations with sophisticated AI-driven response production. The platform imitates Gemini’s capabilities by leveraging the YouTube API to suggest pertinent films arranged by comment engagement and the Gemini API to produce theoretical answers based on user inquiries. By using MongoDB to store and show user search history in a sidebar, the project allows users to view past queries after entering their login information. Auth0 securely manages authentication, guaranteeing a quick and secure user login. Through the integration of these technologies, Intelli Search provides a dynamic and customized user experience, enhancing search relevance by fusing multimedia resources with theoretical knowledge. The architecture is examined in this work.

DOI: 10.61137/ijsret.vol.10.issue6.363

Medical Image Analysis Using Deep Learning: A Comprehensive Review of Techniques and Applications
Authors:-Bramhanand Gaikwad

Abstract-Medical image analysis is a critical component in modern healthcare, enabling more accurate and timely diagnoses. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown impressive capabilities in automating medical image interpretation. This paper reviews the latest advancements in deep learning methods for medical image analysis, covering key applications such as image classification, segmentation, and object detection. We discuss the challenges in applying deep learning models to medical imaging, such as the need for large annotated datasets, generalization to diverse datasets, and model interpretability. Additionally, we provide an overview of state-of-the-art architectures and their performance in different medical imaging tasks. Finally, we address the future directions and potential clinical applications of these techniques.

DOI: 10.61137/ijsret.vol.10.issue6.364

A Review of AI & Robotics in Space Exploration Missions
Authors:-Ayush Santwani, Associate Professor Alka Rani

Abstract-Deep reinforcement learning has emerged as a transformative technology in AI and robotics, finding new answers to challenging problems in space exploration missions. This review details the latest developments within the DRL framework with applications in space robotics, exploring aspects such as autonomous navigation and resource optimization as well as mission planning. In this study, we do some case studies on strategies like AlphaNavNet, AstroPlannerNet, and open-source SpaceRL framework. We review how the DRL-based system addresses some key issues such as unpredictable terrain, delay in communication and exploration versus exploitation. In addition, this paper covers the embedding of simulation-to-reality translation in robotics and astrophysical modeling and the application of deep learning techniques such as Double Deep Q- Networks (DDQN) and Reinforced Deep Markov Models (RDMM) in augmenting the decision- making power of space missions. Although DRL has proved to outperform other approaches in simulaions and prototype testing, the review also emphasizes experimentation for added robustness and reliability within extraterrestrial condition. Through this analysis, we gain insight into the potential and limitations of DRL in advancing space exploration, using new architectures and real-world validation.

A Review of Accountability and Ethics in Artificial Intelligence: A Technical and Legal Synthesis Based on Current Research
Authors:-Anshul Kachhwal, Associate Professor Alka Rani

Abstract-AI has deeply penetrated even the most critical domains, including healthcare, finance, and governance, making it possible with its transformative potential to reach unprecedented efficiency and innovation. Still, this widespread diffusion poses ever more urgent challenges related to ethics and accountability that should not be ignored. Synthesizing insights from five seminal studies on “Ethical Approaches in Designing Autonomous and Intelligent Systems,” “Accountability of AI Under the Law: The Role of Explanation,” “Explainable AI as a Tool for Accountability,” “AI Accountability in Financial Decision-Making,” and “Ethical Implications of Artificial Intelligence (AI) Adoption in Financial Decision-Making,” this paper explores the interplay between accountability frameworks and explainable AI (XAI), regulatory compliance, and societal impacts by combining theoretical and practical perspectives. This paper explores the necessity of explainable models in terms of handling ethical dilemmas, such as bias mitigation, fairness, and transparency, through technical methodologies like sensitivity analysis, counterfactual reasoning, and Shapley values for feature importance. Case studies in health care, finance, and governance -AI-driven diagnostics, credit risk assessments, and algorithmic decision-making in welfare systems- will be explored to illustrate consequences of opacity and betterment facilitated by accountability-driven approaches. In terms of these elements, this paper discusses emerging regulatory landscapes, including the AI Act in the European Union and global data protection laws, as importance factors forming the ethical practices of AI. Public trust erosion due to biased or opaque AI systems is a further societal impact, and inclusive design and multi-stakeholder accountability are put forward as important aspects in this context. A balanced framework of ethical considerations to guide AI innovation should encompass both technical and normative dimensions. Various practical recommendations are laid out, such as standardized practices of XAI, robust accountability mechanisms, and proactive approaches to compliance and regulatory matters. The research brings the technological advancement closer to the imperatives of ethics in AI, toward trust, equity, and justice in its use.

A Review on the Advancements in Plant Disease Detection Using Deep Learning
Authors:-Divya Kanwar, Dy HOD Assistant Professor Uday Pratap Singh

Abstract-The use of DL algorithms revolutionizes the approach towards the detection of plant disease, making this most critical agricultural technology develop towards accuracy and efficiency that were not possible even with earlier methods. Apart from the benefits that an automated system may have over a manual intervention one, such as quicker identification of disease and less manual efforts, DL techniques, and CNNs in particular, allow the diagnosis of the diseases on plants with precision. The potential of AI-powered systems for plant disease detection is the ability to automatically analyze a plant image to recognize the symptoms and classify diseases with high accuracy. These systems also have the potential to provide real-time support by analyzing complex images and suggesting management recommendations for diseases. Thus, with DL algorithms, the system can identify diseases in plants, detect slight changes in texture and color, and recommend the corrective action to optimize crop health. Further, with the recent advancement in optimized models like YOLOv5 and hybrid techniques by integrating CNN with traditional classifiers such as Support Vector Machines (SVMs), the accuracy in detection has increased. Although the approaches present promising outcomes, challenges abound, especially in dealing with complex image backgrounds, low-quality datasets, and computational efficiency. This paper discusses approaches designed to overcome these hurdles, thus indicating the future direction of plant disease detection systems. This work will, therefore contribute towards the advancement of AI-driven agricultural solutions in terms of the accuracy and speed of plant disease detection and enable better crop management practices around the world.

Unified Adaptive Few-Shot Learning in Computer Vision
Authors:-Rahul Jangid, Assistant Professor Mohnish Sachdeva

Abstract-With the increasing prevalence of limited labelled data in many real-world applications, few-shot learning (FSL) has become an essential approach to enable effective learning from minimal examples. However, scalability, domain generalization, and adaptability to new tasks remain significant challenges. This paper introduces “Unified Adaptive Few-Shot Learning”, a novel framework that combines the strengths of metric learning, graph neural networks (GNNs), and meta-learning. By extending Prototypical Networks with GNN- based prototype refinement, our approach improves the quality of class representations and captures complex inter-class relationships. Meta-learning further enhances task-specific adaptation, while self-supervised pretraining boosts feature robustness. Additionally, integrating class metadata facilitates seamless transitions between few-shot and zero-shot tasks. Experimental evaluations on benchmark datasets like Mini-ImageNet and Meta-Dataset demonstrate that our framework outperforms existing methods in accuracy, scalability, and cross-domain generalization, offering a promising solution for real-world FSL applications.

Smart Contracts for Supply Chain Management
Authors:-Abhishek Sharma, Dr. Budesh kanwar

Abstract-The manufacture of raw materials to deliver the product to the consumer in a traditional supply chain system is a manual process with insufficient data and transaction security. It also takes a significant amount of time, making the entire procedure lengthy. Overall, the undivided process is ineffective and untrustworthy for consumers. If blockchain and smart contract technologies are integrated into traditional supply chain management systems, data security, authenticity, time management, and transaction processes will all be significantly improved. Blockchain is a revolutionary, decentralized technology that protects data from unauthorized access. The entire supply chain management (SCM) will be satisfied with the consumer once smart contracts are implemented. The plan becomes more trustworthy when the mediator is contracted, which is doable in these ways. The tags employed in the conventional SCM process are costly and have limited possibilities. As a result, it is difficult to maintain product secrecy and accountability in the SCM scheme. It is also a common target for wireless attacks (reply to attacks, eavesdropping, etc.). In SCM, the phrase “product confidentiality” is very significant. It means that only those who have been validated have acc ess to the information. This paper emphasizes reducing the involvement of third parties in the supply chain system and improving data security. Traditional supply chain management systems have a number of significant flaws. Lack of traceability, difficulty maintaining product safety and quality, failure to monitor and control inventory in warehouses and shops, rising supply chain expenses, and so on, are some of them. The focus of this paper is on minimizing third-party participation in the supply chain system and enhancing data security. This improves accessibility, efficiency, and timeliness throughout the whole process. The primary advantage is that individuals will feel safer throughout the payment process. However, in this study, a peer-to-peer encrypted system was utilized in conjunction with a smart contract. Additionally, there are a few other features. Because this document makes use of an immutable ledger, the hacker will be unable to get access to it. Even if they get access to the system, they will be unable to modify any data. If the goods are defective, the transaction will be halted, and the customer will be reimbursed, with the seller receiving the merchandise. By using cryptographic methods, transaction security will be a feasible alternative for recasting these issues. Finally, this paper will demonstrate how to maintain the method with the maximum level of safety, transparency, and efficiency.

Cross Site Scripting Research: A Review
Authors:-Ankit Jangid, Associate Professor Bhawana Kumari

Abstract-Cross-site scripting is one of the severe problems in Web Applications. With more connected devices which uses different Web Applications for every job, the risk of XSS attacks is increasing. In Web applications, hacker steals victims session details or other important information by exploiting XSS vulnerabilities. We studied 412 research papers on cross-site scripting, which are published in between 2002 to 2019. Most of the existing XSS prevention methods are Dynamic analysis, Static analysis, Proxy based method, Filter based method etc. We categorized existing methods and discussed solutions presented on papers and discussed impact of XSS attacks, different defensive methods and research trends in XSS attacks.

Reducing Digital Distraction through an AI-Driven Anti-Distraction Application
Authors:-Assistant Professor Ms. Rekha Choudhary, Mr. Abhishek Baghel, Mr. Vicky, Ms. Mona

Abstract-The Focus Pro Anti-Distraction Application is a productivity-enhancing tool designed to help users maintain focus by reducing distractions from digital platforms like social media, videos, and other time-wasting activities. With the increasing prevalence of digital distractions, this app provides a structured, customizable solution to improve concentration and task completion for students, professionals, and anyone seeking better focus. The app offers multiple focus modes, each tailored for specific tasks: Learning Mode, Assignment Mode, and Notes Mode. These modes feature task management tools, reminders, progress tracking, and a calendar to organize tasks and goals effectively. Users can customize their experience based on their specific needs, whether they are studying, working on assignments, or taking notes. A standout feature is the app’s blocking functionality, which allows users to create a customized list of websites and apps to block during use. This helps users avoid distractions and stay on task by preventing access to non-productive content on both mobile and desktop devices. In addition, the app integrates an AI-powered Filtering system that intelligently analyzes content on platforms like YouTube and Google. It uses keyword and hashtag analysis to allow access only to study-related content, ensuring users remain focused on educational materials. The app also includes performance analytics, which tracks user productivity and provides insights into task completion. Users earn points for completing tasks on time, and these points contribute to earning badges. This gamification approach encourages users to stay motivated and improve their focus. In addition, the app offers a streamlined profile section that allows users to monitor their achievements, track badges earned. The interface is designed to be user-friendly and visually engaging, making it easy for users to navigate modes.

Real-Time Soil Monitoring in Agriculture
Authors:-Priyanshu Kumawat, Assistant Professor Mohnish Sachdeva

Abstract-Within the face of world populace increase, sustainable and efficient crop production has come to be important. the mixing of emerging technologies consisting of the net of things (IoT), cloud computing, and machine mastering is revolutionizing agriculture through permitting actual-time soil tracking, crop selection, and predictive analytics for more desirable choice- making. This paper offers a comprehensive framework for IoT-enabled precision agriculture, which employs numerous sensors to reveal soil parameters—including moisture, pH, and temperature—and leverages advanced machine learning algorithms for crop advice and soil nutrient management. The proposed structures now not best optimize irrigation and fertilization but additionally provide a low-value, electricity-efficient method to information collection via wi-fi sensor networks. additionally, cloud-primarily based structures and cell programs provide farmers with far flung get entry to real-time data, permitting well timed interventions. by way of combining reinforcement learning fashions, multi-sensor information fusion, and modular hardware setups, this machine supports sustainable farming practices and will increase crop productiveness. The consequences show sizeable upgrades in prediction accuracy, decreased environmental effect, and more advantageous selection-making skills for farmers, contributing to the modernization of agriculture.

From Data to Diagnosis: A Review of Deep Learning’s Technological and Ethical Implications in Medical Innovation
Authors:-Arjunsingh Kuldeepsingh Rana, Assistant Professor Mr. Ebtasam Ahmad Siddiqui

Abstract-The rapid advancements in deep learning (DL) techniques have transformed the healthcare sector, leading to notable improvements in diagnostic accuracy, personalized treatment, and ongoing patient monitoring. One particularly promising application of deep learning in healthcare is Human Activity Recognition (HAR), which uses wearable and mobile sensors to track and categorize individuals’ daily activities. HAR, especially within the framework of the Internet of Healthcare Things (IoHT), has demonstrated significant potential in enhancing elder care, rehabilitation processes, and chronic disease management. However, despite these advancements, several challenges persist in fully leveraging deep learning for healthcare applications. A major challenge is the dependence on large, labeled datasets for training models. In real-world scenarios, obtaining labeled data for HAR tasks can be time-consuming, costly, and often impractical, leading to a reliance on weakly labeled or unlabeled data. To tackle this issue, recent strategies in deep learning, particularly semi-supervised and reinforcement learning techniques, have been introduced to make efficient use of the vast amounts of unlabeled data available. These methods, such as Deep Q-Networks (DQN) and auto-labeling schemes, significantly lessen the manual labeling burden while preserving high model accuracy. Additionally, deep learning’s capability to integrate multi-modal data from various sensors (like accelerometers, gyroscopes, and context sensors) is vital for HAR tasks. This integration of sensor data offers a more thorough understanding of human activity and improves the accuracy of activity classification models. Among the most promising deep learning models for HAR are Long Short-Term Memory (LSTM) networks, which excel at processing sequential data typical in human activity monitoring. LSTMs effectively capture temporal dependencies in sensor data, making them well-suited for identifying complex motion patterns and contextual changes.

Impact of Emotional Intelligence in Managing Stress: A Critical Analysis in Respect to Healthcare Sector through Literature Review
Authors:-Dr. Pramit Das, Assistant Professor Ms. Subhasree Ray

Abstract-The COVID-19 pandemic has had an unprecedented impact on health systems in most countries, and in particular, on the mental health and well-being of health workers on the frontlines of pandemic response efforts. The purpose of this study is to provide an evidence-based overview of the adverse mental health impacts on healthcare workers during times of crisis and other challenging working conditions and to highlight the importance of prioritizing and protecting the mental health and well-being of the healthcare workforce, particularly in the context of the emotional intelligence.

DOI: 10.61137/ijsret.vol.10.issue6.367

Detection of Phishing Websites Using Machine Learning
Authors:-Manish Gujral, Harsh Kumar, Annu Sharma, Dr.Monika

Abstract-Phishing is a category of cyberattack that includes the theft of credit card numbers, passwords, and other private data. We have employed machine learning algorithms to identify phishing websites in order to prevent phishing fraud. The availability of several services, including social networking, software downloads, online banking, entertainment, and education, has sped up the development of the Web in recent years. Consequently, enormous volumes of data are downloaded and uploaded to the Internet on a regular basis. Attackers can now obtain private information, including social security numbers, account numbers, passwords, and usernames, as well as financial information. This is one of the most important problems with web security and is referred to as a “phishing” attack on the internet. To identify these malicious websites, we employ a variety of machine learning methods, including KNN, Naive Bayes, Gradient Boosting, and Decision Trees. The study is broken down into the following sections. The introduction outlines the tools, methods, and concentrated zones that are employed. The process of gathering the data needed to proceed is described in depth in the preliminary section. Subsequently, the paper highlights the thorough examination of the information sources.

DOI: 10.61137/ijsret.vol.10.issue6.368

A Review on Matlab Simulink Modeling of Solar Based EV System with Control of its Utility Parameters
Authors:-Ajay Yadav, Assistant Professor Abhay Awasthi

Abstract-Emerging topics such as environmental protection and energy utilization have pushed research and development of electric vehicles. In the last few decades, numerous technologies have been developed for EV importance. In this article, key research topics in the area of EVs, namely electric machines, electrochemical energy sources, wireless charging infrastructure, and latest EV/HEV models are covered. This Review paper aims to consolidate the key emerging technologies in this field and provide the readers a blueprint to begin their own journeys.

Youtube Video Summary Generator
Authors:-Ms. Sumalata Bandri, Mr. Abhishek Pandey, Mr. Bhushan Mahadule, Mr. Om Satpute, Mr. Vaibhav Jawade

Abstract-This project introduces the YouTube Video Transcribe Summarizer, a tool designed to automatically extract transcripts and generate concise summaries from YouTube videos. By leveraging the YouTube Transcript API, the system retrieves accurate video transcripts and utilizes Google Gemini Pro’s advanced text-based model to create coherent summaries.
Users can input a YouTube video URL, which displays the video thumbnail for context. The application features a customizable prompt template to tailor the summary generation process, ensuring relevance to individual needs. Built on a user-friendly Streamlit interface, this tool aims to enhance content accessibility and engagement. Additionally, the project explores the possibility of executing local models for improved performance and user control. By streamlining the summarization of video content, the YouTube Video Transcribe Summarizer facilitates more efficient information consumption, empowering users to navigate the vast landscape of online video more effectively.

DOI: 10.61137/ijsret.vol.10.issue6.369

Why Do We Need So Many Programming Languages
Authors:-Kajal Nanda

Abstract-If we attempt to measure the need for the proliferation of so many programming languages, we will get an answer but it is a serious question in itself: why do we need so many programming languages?! Albeit there are existing so many dominant programming languages which can perform almost every task specifically, we are developing and depending upon a variety of them. Through this paper, the rationale behind developing diverse programming languages will be explored and the other factors like performance optimization, ease of use, specification and demand of the evolution of the era of technology will be discussed. It will also examine the distinguished categorisation of computer languages.

DOI: 10.61137/ijsret.vol.10.issue6.370

Indian Man Made Islands Idea to Save Wildlife
Authors:-Deepak Singh

Abstract-This research paper explores the concept of man-made islands as a potential solution to address habitat loss and environmental degradation. By creating artificial islands, we can provide new habitats for wildlife, protect existing ecosystems, and mitigate the impacts of human activities on the environment. The paper will delve into the design principles, construction techniques, and ecological considerations involved in creating sustainable man-made islands. It will also examine the potential benefits of these islands, such as increased biodiversity, improved water quality, and coastal protection. Additionally, the research will discuss the challenges and limitations associated with man-made islands, including their environmental impact, economic feasibility, and potential conflicts with other land uses. Ultimately, this paper aims to contribute to the ongoing dialogue on innovative solutions for conservation and environmental sustainability.

DOI: 10.61137/ijsret.vol.10.issue6.371

Nanorobotics: The Future of Medicine
Authors:-Snehal More, Aishwarya Deshmukh, Dipti Gade

Abstract-Nanorobotics is an exciting field that combines nanotechnology and robotics to revolutionize medicine. These tiny robots, smaller than a speck of dust can navigate through our bodies to deliver targeted treatments perform precise surgeries and even repair damaged cells . With their ability to access hard to reach areas and perform tasks at the molecular level nanorobotics hold immense potential in improving outcomes healthcare and transforming the future of medicines.

DOI: 10.61137/ijsret.vol.10.issue6.372

Nano Material Based Optical and Electrochemical Sensors
Authors:-M.Suriya Prasath Murugan, Dr. P.Selvamani Palaniswamy, Dr.S.Latha

Abstract-Nanomaterials display unique features such as Excellent physical and chemical stability, lower density and high surface area. This chapter focus on nanomaterials such as graphene and carbon Nanotubes, how it is electrically and optically sensored with Nanomaterials. Multiple complex biosensors has been focused and even the application of Nanaomaterials also. In past few years a major disease has been affected throughout the world that is COVID-19, how nanomaterials has been used in curing the disease.

DOI: 10.61137/ijsret.vol.10.issue6.373

DNA Computing
Authors:-Yash Malusare, Aditya Deshmukh, Saurabh Kumar Prabhakar

Abstract-DNA data storage is revolutionizing technology to fill up the voids in existing data storage systems with higher density and durability. The paper deals with DNA comput- ing, especially with the concept of using DNA sequences for data storage with emphasis on encoding digital data in DNA sequences and discussion on the latest developments in DNA storage technologies, challenges facing it, such as scalability and cost, and also the problem of error correction. The paper also highlights the advantages of DNA as a storage medium, including high information capacity and stability in the long term but discusses existing challenges. As a conclusion, we enumerate some directions for further research needed to make DNA data storage more practical. Another key challenge explored in the paper is error correction. DNA sequences, while robust, are prone to errors during synthesis, amplification, and sequencing processes. These errors can compromise the integrity of the stored data, necessitating the development of advanced error correction mechanisms. The paper examines current strategies for mitigating these errors, including the use of redundancy, coding theory, and error-tolerant storage architectures, while also identifying gaps that require further exploration.

DOI: 10.61137/ijsret.vol.10.issue6.374

Energy Efficiency by Optimizing Power Sharing with Clustering
Authors:-Ms. Umi Roman, Assistant Professor Mr. Kamaljeet Singh, Assistant Professor Mr. Parwinder Singh

Abstract-Conserving energy of power grid within wireless power grid nodes network (power grid) is crucial in different applications including wearable devices. To this end, proposed work uses sleep and wakeup protocol for conserving energy of power grid nodes. The protocol first of all examines the nodes that are not used for transmission of packets for longer period of times. After that detected node will be put to sleep. The nodes energy will play a crucial role to make it a cluster head. Euclidean distance will be used to elect node as cluster head. The experimental setup involves random node distribution, initial energy allocation, and the formation of clusters based on Euclidean distance. The proposed sleep and wakeup mechanisms strategically put nodes to sleep after periods of inactivity, conserving energy resources. A comprehensive evaluation, comparing the protocol’s performance with the widely used low energy aggregate cluster head (LEACH) selection protocol, stable election protocol (SEP), time based stable election protocol (TSEP) and distributed energy efficient clustering protocol (DEEC), reveals superior results in terms of fewer dead nodes, prolonged network lifetime, and efficient packet transmissions. The proposed method showcases a controlled and sustained pattern in communication to cluster heads and base stations, outperforming LEACH, DEEC, SEP and TSEP. Remaining energy analysis indicates a more gradual and sustainable reduction in energy levels, highlighting the protocol’s effectiveness in maintaining operational nodes over prolonged network. The study concludes with insights into future research directions, emphasizing parameter optimization, scalability considerations, integration of energy harvesting methods, and enhanced security measures.

Advanced Load Flow Analysis Techniques in MATLAB the Swing Equation and Newton-Raphson Method
Authors:-Mr.Barkat Ali Lone, Assistant Professor Mr. Kamaljeet Singh, Assistant Professor Mr. Parwinder Singh

Abstract-This paper presents a brief idea on load flow in power system, bus classification, improving stability of power system, flexible ac system, various controllers of FACTs and advantages of using TCSC in series compensation. It presents the modelling scheme of TCSC and the advantages of using it in power flow network. The plots obtained after simulation of network using MATLAB both with and without TCSC gives fair idea of advantages on use of reactive power compensators. load flow studies are fundamental in power system analysis for ensuring efficient and stable operation of electrical networks. This thesis investigates the application of the swing equation and the Newton-Raphson method in performing load flow analysis, aiming to enhance the accuracy and efficiency of power system evaluations. The swing equation, representing the dynamic response of a generator’s rotor to changes in system conditions, is used to model the transient behaviour of generators in power systems. This dynamic model is crucial for understanding how generators respond to load variations and network disturbances. However, for steady-state analysis, which is essential for system planning and operation, the swing equation’s role is more implicit, focusing on power balance and network equilibrium. In this study, we integrate the swing equation into a comprehensive load flow analysis framework, combining it with the Newton-Raphson method—a robust iterative technique for solving nonlinear algebraic equations. The Newton-Raphson method is employed to solve the power flow equations, which describe the relationship between generator outputs, load demands, and network configurations. The thesis details the formulation of the power flow equations and the application of the Newton-Raphson method to solve these equations efficiently. The integration of the swing equation helps refine the analysis by incorporating generator dynamics into the power flow study. The effectiveness of this approach is demonstrated through various case studies on different network configurations, showing improvements in both accuracy and convergence speed compared to traditional methods.

Automatic Detection of Traffic Violations Using Yolo Model and Challan Generation
Authors:-Kishan Singh, Kunal Lohar, Pratham Bagora

Abstract-As the rate of traffic violations is on the rise, there arises the need for automated enforcement systems. This project is about the implementation of an automated system of e-challan generation based on the license plate detection system. Cameras positioned at the intersections take images of the vehicles violating traffic rules; using computer vision techniques, the number plates are identified and read. The system now fetches the registered mobile number of the violator and sends out an e-challan by itself, thus although removing the manual efforts with more precision [1] and effective enforcement. By using tools like OpenCV and YOLO in major towns, the project can make the roads safer and traffic flow manageable.

DOI: 10.61137/ijsret.vol.10.issue6.375

Robotics Neurosurgery: A Transformative Approach to Precision Medicine
Authors:-Lakshya Jain

Abstract-Robotics in neurosurgery has completely changed the game, and now there is much greater accuracy, higher efficiency levels, and greater safety of the patient. Robotic systems such as ROSA, NeuroMate, and Stealth Autoguide have taken minimally invasive approaches within surgery to an entirely different level, allowing for complex sutures to be performed with great ease. This paper discusses the history of development of robotic systems, the specifics of their application in different neurosurgical procedures, and their advantages related to the lesser invasiveness, better results for the patients, and shorter periods of the recovery. Limitations such as costs, the need for training, and ethical issues are in the analyses, and also expected advances such as autonomous operations driven by AI and tele-robotics. There is great potential with the use of robotics in the development of neurosurgical practice towards more accurate and patient-centered clinical activities.

Impact of Machine Learning on High Frequency Trading: A Comprehensive Review
Authors:-Dipanshu Jain

Abstract-High-Frequency Trading (HFT) is a critical component of modern financial markets, characterized by the execution of large volumes of orders within fractions of a second. The integration of machine learning (ML) techniques has revolutionized HFT by enhancing decision-making, optimizing trading strategies, and mitigating risks. This study explores the transformative impact of ML on HFT, focusing on methodologies such as Support Vector Machines (SVM), Random Forests (RF), Deep Learning architectures like Convolutional Neural Networks (CNNs), and advanced techniques including Reinforcement Learning and hybrid models. The research examines these methods in terms of their effectiveness in predictive modeling, pattern recognition, and real-time analytics. Additionally, a comparative analysis of these ML models highlights their advantages, limitations, and adaptability to the dynamic nature of financial markets. By addressing the challenges and opportunities of integrating ML into HFT, this paper provides insights into the future potential of automated trading systems and their implications for market efficiency and stability.

Review on Simulation Model To Reduce The Fuel Consumption Through Efficient Road Traffic Modelling
Authors:- Md Muneer Alam, Dr. Sunil Sugandhi

Abstract- Traffic control strategy plays a significant role in obtaining sustainable objectives because it not only improves traffic mobility but also enhances traffic management systems. It has been developed and applied by the research community in recent years and still offers various challenges and issues that may require the attention of researchers and engineers. Recent technological developments toward connected and automated vehicles are beneficial for improving traffic safety and achieving sustainable goals. There is a need to develop a survey on traffic control techniques, which could provide the recent developments in the traffic control strategy and could be useful in obtaining sustainable goals. This survey presents a comprehensive investigation of traffic control techniques by carefully reviewing existing methods from a new perspective and reviews various traffic control strategies that play an important role in achieving sustainable objectives. First, we present traffic control modeling techniques that provide a robust solution to obtain reasonable traffic and sustainable mobilities. These techniques could be helpful for enhancing the traffic flow in a freeway traffic environment. Then, we discuss traffic control strategies that could be helpful for researchers and practitioners to design a robust freeway traffic controller. Second, we present a comprehensive review of recent state-of-the-art methods on the vehicle design control strategy, which is followed by the traffic control design strategy. They aim to reduce traffic emissions and energy consumption by a vehicle. Finally, we present the open research challenges and outline some recommendations which could be beneficial for obtaining sustainable goals in traffic systems and help researchers understand various technical aspects in the deployment of traffic control systems.

Budget-Beacon
Authors:- Assistant Professor Princy Shrivastava, Sejal Raghuwanshi, Supraja Krishnan

Abstract- The ‘Budget Beacon’ is an unadorned web application designed to make it easy for people to manage their finances and monitor their expenses. It provides users with the facilities to make financial decisions and strategies. Incorporating advanced features makes it easier for users to maintain their finances with precision and make more financial decision with precision. The web application gives users the ability to keep track of their daily expenses and break down their spending by category [1].It helps users keep their financial information digitally eliminating the traditional book keeping system.

DOI: 10.61137/ijsret.vol.10.issue6.376

Service-Hub: An On-Demand Home Services Platform
Authors:- Ishika Joshi, Ishwar Rajput, Mohit Deshmukh, Professor Garima Joshi

Abstract- Managing data for diverse types of home service providers can be challenging for users due to communication gaps between providers and recipients. This often leads to unexpected inconveniences for service recipients and missed opportunities for providers to showcase their skills effectively. ServiHub, an on-demand home services platform, bridges this gap by facilitating seamless two-way communication between service providers and recipients. The platform simplifies the process of finding the right service provider and ensures efficient job scheduling for providers. Additionally, a feedback-based rating system enhances the skills of service providers and ensures users receive improved and reliable services over time.

Automated Temperature Control System by Using Atmega 328 Micro-Controller and DC Fan
Authors:- Deepavarthini S, Subaranjani B S, Karpagam P

Abstract- The main aim of this project is to design the system by using the micro-controller (ATmega328) and temperature sensor for sensing the room temperature with a small DC fan. The system was designed to maintain the constant and comfortable room temperature by automatically activating the DC fan when the temperature exceeds the normally fixed temperature value and deactivates the DC fan when the temperature value falls below the fixed value. The temperature sensor used here will statically monitors the temperature value of the room. By using the reading data the controller makes the decision either to activate the DC fan or to deactivate the DC fan. This system is the energy saving way that activate the DC fan when only the temperature exceeds the fixed value else the fan will be deactivated. It is one of the best solution for maintaining indoor conditions, minimizing the manual interaction of the user and provide the overall comfort to the user.

DOI: 10.61137/ijsret.vol.10.issue6.377

A Survey of Machine Learning Approaches for High-Quality Image Restoration and Reconstruction
Authors:- M. Tech Scholar Shubhangi Mansore, Professor Kamlesh Patidar

Abstract- The restoration of damaged images has become an essential and highly valuable tool in a wide range of technical applications, including space imaging, medical imaging, and numerous other post-processing techniques. These applications often involve the challenging task of correcting images that have been degraded by factors such as blur and noise. Most image restoration methods begin by simulating the processes that cause image degradation, typically focusing on the effects of blur and noise, and then work to approximate the original image. However, in more realistic real-world scenarios, the challenge is to estimate both the true image and the associated blur based on the characteristics of the degraded image, without relying on any prior knowledge of the blurring mechanism. This situation reflects the complexities encountered when dealing with real-world data. This thesis introduces and develops an innovative approach to digital image restoration, utilizing punctual kriging and various machine learning algorithms. The focus of this research is on restoring images that have been degraded by Gaussian noise, achieving a balance between two competing objectives: maintaining smoothness while preserving edge integrity. This approach aims to enhance the effectiveness of image restoration techniques, particularly in situations where the image has been compromised by environmental and other factors.

Structural Design and Analysis of Wind Turbine
Authors:- Md Fakhor Uddin

Abstract- This thesis presents a comprehensive exploration into the design, modeling, and analysis of a wind turbine, employing a multidisciplinary approach to optimize its performance. The blade geometry was generated using QBlade software, a robust tool for blade design in wind turbine applications. The 3D model was then meticulously crafted using SolidWorks, integrating aerodynamic principles and structural considerations. The heart of this project lies in the utilization of SolidWorks Flow Simulation for a detailed analysis of the aerodynamic characteristics of the designed wind turbine. The simulation facilitated a thorough examination of airflow patterns, turbulence effects, and pressure distributions around the blades, offering valuable insights into the efficiency and energy-capturing potential of the turbine under various wind conditions.

DOI: 10.61137/ijsret.vol.10.issue6.378

Review on Performance Parameter of MOSFET and FinFET Transistor
Authors:- Assistant Professor Madhvi Singh Bhanwar, Associate Professor Dr.Nidhi Tiwari, Professor Dr. Mukesh K Yadav

Abstract- In modern world technologies are grooming very fast day by day along with the world semiconductor industry the world of IC is also grooming and enhancing the technologies day by day as we know according to Moore’s law the number of transistors will be double on a chip in every eighteen months that means the size of components will be reducing day by day in the same way types of transistors were introduced like MOSFET and FinFET. FinFET replaced MOSFET, FinFET resolved all the challenges of MOSFET and helped in compact designing of electronic devices, FinFET is widely used in various modern electronic devices because of its structure, fast switching speed, low power consumption and less leakage current.

DOI: 10.61137/ijsret.vol.10.issue6.381

Truck Chassis Frequency Analysis with Different Simulation Conditions
Authors:- Dr. Prashanth A .S, Amith Kumar S N, Dr. Vishwanth M, Dr. T N Raju

Abstract- The chassis of a truck is the backbone of the vehicle, incorporating the majority of component systems such as axles, suspension, gearing, cab and trailer, and is typically subjected to the load of the cabin, its contents, and inertia forces induced by rough road surfaces, among other things (i.e. static, dynamic and cyclic loading).In fatigue research and component life prediction, strain analysis is critical for determining the best stress point, also known as the juncture that leads to likely failure. One of the causes that contributes to fatigue loss is this juncture.

Optimizing Solar Energy: A Study on Dynamic Panel Systems
Authors:- Ranjeeta Susan Avinash

Abstract- The greatest challenge in the upcoming decades is to switch from using fossil fuels to a greener form of energy. Solar energy is of the highest priority. However, the frequent change in the sun’s position with respect to the Earth makes it nearly impossible to collect a hundred percent heat energy from the sun. Therefore, the need to improve the energy efficiency of photovoltaic solar panels by building a solar tracking system must be considered. To get maximum energy, PV panels must be perpendicular to the sun’s position. The methodology includes the implementation of an Arduino-based solar tracking system consisting of Light-dependent resistors (LDRs), a PV solar panel, and a servo motor to control the movement of the solar panel based on the position of the sun. The result of this work has clearly shown that the tracking solar panel produces more energy than a fixed panel.

Analytical Study of Grubler’s Criterion for Plane Mechanisms
Authors:- Professor N.Tamiloli, T.Gowtham, T.Gowshik

Abstract- Grublers criterion is a foundational concept in kinematics, offering a systematic approach to determining the degrees of freedom (DoF) of planar mechanisms. This study delves into its theoretical basis, exploring its application to various types of plane mechanisms. By analyzing case studies and real-world examples, this research aims to validate the criterion’s utility and highlight its limitations. The findings demonstrate that while Grublers criterion effectively predicts kinematic behavior, it requires adaptation for certain complex mechanisms. The study provides insights into enhancing the understanding and application of this criterion in mechanical design.

Multimodal Emotion Recognition Using BERT and ANN: A Hybrid Deep Learning Approach
Authors:- Research Scholar Avasheen Shishir Temurkar, Professor Anuradha Purohit

Abstract- Emotion recognition plays a vital role in enhancing human-computer interaction systems by enabling empathetic and context-aware AI solutions. This study introduces a hybrid deep learning architecture that integrates BERT for extracting contextual text features and an Artificial Neural Network (ANN) for processing MFCC-based acoustic features. By combining textual and audio modalities, the proposed model effectively addresses the limitations of single-modality approaches. The model is evaluated on the USC-IEMOCAP dataset, encompassing six emotion categories: ‘Happy’, ‘Sad’, ‘Angry’, ‘Neutral’, ‘Frus- trated’, and ‘Excited’. It achieves competitive performance with a weighted F1-score of 0.91 and an accuracy of 86%, outperforming several state-of-the-art methods. The fusion of text and audio features enhances the model’s ability to capture subtle emotional nuances, demonstrating the potential of multimodal learning for robust emotion classification. This research underscores the value of hybrid architectures in advancing emotion recognition for real- world applications.

DOI: 10.61137/ijsret.vol.10.issue6.382

Educational Data Mining on University Management Information System for Measuring Performance of Students
Authors:- Pankaj Shrimali, Dr. Tarun Shrimali

Abstract- Data mining techniques are used in the numerous industries alongwith the IT sector, Agriculture and education system. Massive technical advancements and opportunities from past decades change the approach and lifestyle of the people. Although data mining techniques are used in the several industries but it is new approach in the Academics. The education system has not greatly profit from the potential of data mining techniuqes. A substantial amount of information are required for the better performance of the students in the academics. There is a vast amount of data are available which can help to find the performance of the students. The role of the data mining technology is to find out the performance of students in academics, the factors also find out which affects the academic performance and also other issues like financial, family background etc. how it effects the performance, how semester wise results so that students aware about the performance and also gender wise how it affects.

DOI: 10.61137/ijsret.vol.10.issue6.383

Validation Testing of Digital Blood Pressure Monitoring Devices for the Upper Arm According to the ISO 81060-2:2018/ AMD 1:2020 Protocol
Authors:- Saheb Singh, Deepak Sinha

Abstract- The purpose of the study was to ascertain the accuracy of blood pressure monitors commonly available in the market. Six devices were chosen including one professional BP monitor for home, clinical and hospital use, manufactured by Mann Electronics India Private Limited, Kota from the market. These devices did not have accessible validation testing results. The subjects for assessment were adults from the general population with varied age groups and sex. The objective was to establish whether the devices conform to the requirements of ISO 81060-2:2018/AMD1: 2020 protocol

DOI: 10.61137/ijsret.vol.10.issue6.384

Comparative Study of Dda Algarthem, Bresenham’s Line-Drawing Algorithm, Midpoint Circle Algorithm Using Python
Authors:- Professor N.Tamiloli, T.Gowtham

Abstract- Efficient algorithms for rendering geometric shapes are fundamental in computer graphics. This study presents a comparative analysis of the Digital Differential Analyzer (DDA), Bresenham’s line-drawing, and Midpoint circle algorithms. We evaluate their performance in terms of computational efficiency, accuracy, and ease of implementation. Python is used as the platform to implement and test the algorithms. Experimental results demonstrate that while DDA offers simplicity in implementation, Bresenham’s algorithm is computationally more efficient for line drawing. The Midpoint circle algorithm proves robust for circular shapes but is relatively complex. This paper provides insights into the algorithms’ suitability for various real-world applications, backed by runtime performance and output quality metrics.

Diagnosis of Acute Diseases in Villages and Smaller Towns Using AI
Authors:- Shreya Ravi Kumar, Neha R., Sneha R.

Abstract- Healthcare has changed as an effect of artificial intelligence’s remarkable accuracy and efficiency in medical diagnostics. A technology named artificial intelligence (AI) lets computers along with additional machines to mimic human abilities such as understanding, problem-solving, innovative thinking, autonomy, and the decision-making process Applications and devices with AI capabilities possess the ability to recognize and understand objects. They are able to decode and give response to human speech. AI is transforming the way illnesses are recognized, evaluated, and treated, especially in the field of medical diagnostics. Using machine learning and deep learning algorithms, AI can swiftly and effectively understand enormous quantities of data, offering healthcare professionals insightful information. These developments not only increase the accuracy of diagnoses but also make it possible for early diagnosis and customized treatment plans. In the early days, AI was primarily employed for administrative duties, but its use has risen significantly. Massive quantities of data can now be accurately and quickly evaluated by AI and machine learning systems, which helps healthcare professionals make better decisions. Medical practice can be revolutionised by these technologies, which can interpret medical pictures, discover trends, and even predict the course of diseases. Access to effective healthcare is usually limited in neglected and rural areas, leading to mediocre health outcomes and delayed diagnosis. Existing ways of resolving this issue, such as telemedicine, have struggled to grow in parallel with growing demands for healthcare. According to this method, a system driven by artificial intelligence would be able to comprehend a large volume of medical data, identify symptoms, and converse with patients in order to find out about their medical concerns. The advent of advanced AI- powered technology and the growing popularity of smart assistants like Google and Alexa signal the beginning of an era of change in healthcare innovation.

Development of Lightweight High-Entropy Nanocomposite Materials for Enhanced Protective Hat
Authors:- Abdulaziz S. Alaboodi, S. Sivasankaran, R. Karunanithi, Khalid Algadah

Abstract- The research project focuses on the design and development of lightweight, high-entropy nanocomposite materials for hard hats and helmets, aimed at enhancing safety across various industrial sectors, including construction and manufacturing. By blending five thermoplastic polymers—high-density polyethylene (HDPE), polycarbonate (PC), polypropylene (PPE), polyethylene terephthalate (PET), and polybutylene terephthalate (PBT) with glass fibers and nanographene, the study produced novel composite materials. Mechanical testing demonstrated improved strength and impact resistance, with a notable 13% weight reduction in the final prototype compared to traditional materials. The project utilized advanced characterization techniques, including FTIR and XRD, to validate the material properties. These innovative materials not only meet industry safety standards but also align with environmental considerations by utilizing readily available raw materials.

Examining the Acceptance of Mobile Marketing by Customer of Small and Medium Scale Enterprises
Authors:-Sopheap Suon

Abstract- In this study we try to explore the concept of mobile marketing in a holistic context. The main focus of the research is on consumer’s behaviour towards mobile marketing. The research is conducted through a primary methodology. Both quantitative and qualitative methodology were used. Surveys were conducted from customers of SMEs and interviews were conducted from the managers of those SMEs. The result shows various consumer attitudes towards mobile marketing, which organisations can understand and attract customers.

DOI: 10.61137/ijsret.vol.10.issue6.385

Smart Classroom Management Software for Enhanced Learning Environment
Authors:-Assistant Professor Ranjana Thakuria, Prajwal k, Sindhu H, Soumya A Bavagi

Abstract- Modern education needs real-time engagement and attendance tracking in order to ensure an effective learning environment. This paper introduces a Smart Classroom Management System, developing together with state-of-art tools like OpenCV for facial recognition and the Mailgun API for effective notifications. The automation of attendance would include sending absence notifications along with the topics missed to students and their parents at login. Furthermore, a camera is turned on during the login session to monitor the activity and engagement levels of the user. The system facilitates instant alerting about inactivity to mentors or parents, thus strengthening accountability. By integrating these technologies, the proposed system is the intelligent, responsive solution for classroom automation, allowing the creation of a more connected and interactive educational ecosystem.

Employing Swarm Intelligence for Optimizing Latency and Energy Consumption for Routing in WSNs
Authors:-Khushboo Parmar, Professor Ruchika Pachori

Abstract- Efficient routing is crucial for many practical applications in wireless sensor networks. Nevertheless, they encounter the unavoidable obstacle of restricted energy resources, which underscores the need of developing data transmission mechanisms that optimize the allocated energy to enhance the longevity of the networks and minimize the system’s latency. Implementing efficient clustering and energy management strategies can enhance the longevity of the network while concurrently decreasing the observed delay. The present study introduces a two-tier methodology for reducing unnecessary transmissions in conjunction with particle swarm optimization (PSO). The objective is to minimize the distances inside clusters in order to reduce both latency and energy usage. The evaluation parameters for the proposed method include the delay in the first hop, the latency in the network, and the energy usage. This empirical method has been employed to determine the optimal fitness function so as to optimize latency and energy consumption in WSNs.

DOI: 10.61137/ijsret.vol.10.issue6.386

AI in Healthcare and Medicine
Authors:-Assistant Professor Santhosh T, Khushi N S, Likhitha K M, Mamatha V

Abstract- AI is the science and engineering of creating intelligent machines, particularly clever computer programs. In fact, AI is already being used in healthcare in a number of ways that are pertinent to nurses in both nursing practice and nursing education. It consists of numerous healthcare technologies that improve patient care and change the duties of nurses. The workload of nurses is lessened as a result of it. AI ethics are crucial since the technology can effect not just the outcome for a single patient but also the way it is used in healthcare during the research, design, testing, integration, and continuous usage phases. Mobile health, clinical decision support, and sensor-based technology like voice assistants and robotics are examples of AI tools for nurses.

DOI: 10.61137/ijsret.vol.10.issue6.387

Over the top Platform
Authors:-Vipashyna Arun Sable, Namrata Yeola, Sanchee Sable, Kanishka Sable

Abstract- Hotstar, (now Disney+ Hotstar), is the most subscribed–to OTT platform in India, owned by Star India.The major cause of the issue might be an unreliable internet connection or connection that is not operating correctly in hotstar. OTT has boosted experimentation to another level. exchange4media Group held the second edition of its one-day event on OTT titled e4m Play Streaming Media Conference & Metal Announcements on May 12, 2021, at 2 pm. The awards honoured excellence in the on-demand video and audio content. OTT platforms deliver content via the Internet, circumventing the need to pay subscriptions to traditional cable broadcast and satellite TV service providers. Therefore, we are building an OTT platform. We are adding subscription model. The web system is developed with PHP, MySQL and Xampp

DOI: 10.61137/ijsret.vol.10.issue6.388

Optimizing Business Outcomes through Data-Driven Decision-Making: Techniques for Complex Dataset Analysis
Authors:-Vinaychand Muppala

Abstract- This study investigates how big data, artificial intelligence (AI), and predictive analytics can work together to transform marketing strategies within the context of Industry 4.0. By utilizing advanced analytical techniques, businesses can enhance their marketing efforts, predict consumer behavior, and optimize resource allocation to improve return on investment (ROI). The research examines the capabilities of AI algorithms and predictive analytics, demonstrating their ability to process large datasets and uncover actionable insights. Through a series of case studies and examples, we highlight how companies across various industries are leveraging these technologies to stay competitive in today’s fast-paced market. Furthermore, the study explores the challenges and ethical concerns related to integrating AI and predictive analytics into marketing strategies. In conclusion, this research underscores the significance of data-driven decision-making in maximizing marketing ROI in the age of Industry 4.0.

A Study on Factors Affecting to Loan Defaults of Micro Credit (Special Reference to People’s Bank Branches in Anuradhapura Region, Sri Lanka)
Authors:-Samansiri Sooriyagama

Abstract- This research investigatesthe factors affecting to loan defaults of micro credit (special reference to people’s bank branches in Anuradhapura region, Sri Lanka).The study addresses the critical need to understand the factors contributing to loan defaults, arrears, and loan restructuring, providing valuable insights for microfinance institutions to enhance their risk management strategies. The primary objectives of this study are to identify, analyse, and comprehend the factors influencing loan repayment behaviour among microfinance clients at People’s Bank branches in the Anuradhapura region. The research aims to contribute to the existing body of knowledge in microfinance and provide practical recommendations for enhancing the loan repayment process. A quantitative research approach was employed, utilizing Likert scale questionnaires to gather data on socioeconomic factors, loan characteristics, institutional practices, and borrower financial behaviours. The survey was distributed to a representative sample of microfinance clients in the Anuradhapura region. Data analysis was conducted using SPSS and Microsoft Excel, employing statistical methods to draw meaningful insights. The research revealed significant correlations between certain socioeconomic factors and loan repayment behaviour. Income levels, educational background, and employment status demonstrated notable associations with loan default rates. Additionally, institutional factors, such as the loan approval process and collection procedures, played a crucial role in shaping repayment behaviour. This research contributes valuable insights into the multifaceted aspects of loan repayment behaviour in microfinance. By understanding the key determinants, microfinance institutions can tailor their practices to mitigate risks and foster a more sustainable and inclusive financial environment. While efforts were made to ensure the reliability and validity of the research, certain limitations, such as sample size constraints and potential biases, should be acknowledged. Future research endeavours could delve deeper into the cultural and social dimensions influencing loan repayment behaviour. Longitudinal studies may also provide a dynamic perspective on the evolving nature of microfinance clients’ financial behaviours.

DOI: 10.61137/ijsret.vol.10.issue6.389

Optimizing the Influence of Temporal Dynamics, Network Topologies, and Semantics on Unsupervised NLP Algorithms
Authors:-Mayank Konduri

Abstract- The purpose of this study was to generate an algorithm able to decipher bots in social media. Prior research shows that variables/parameters affect the detection of AI; however, none attempt to compile an algorithm accurate enough to be deployed into a real-world scenario. Data was collected through mixed methods, in which data was collected online and through questionnaires. Participants included individuals from all demographics, only restricted to demonstrate no bias. Initial results show a strong correlation with variables on the usage of AI. This means that a model which can effectively deduce the usage of AI is plausible. Therefore, the conclusion can be made that it is possible to find bots in social media; however, this is limited to 70% accuracy, given the available resources. Future research should be targeted towards making sure text can be deciphered with more accuracy.

DOI: 10.61137/ijsret.vol.10.issue6.390

A Survey on Machine Learning Handling Imbalanced Dataset in Credit Card Fraud
Authors:-Pawan Panchole, Rajesh Dhakad

Abstract- In the era of digital transaction people prefer to make online payments and purchases due to the convenience of time, transportation, etc. Credit card fraud has also increased significantly due to the growing trend of e-commerce. Fraudsters try to take advantage of card and internet payment information. Credit card and online payment information is often used by fraudsters for fraudulent purpose. Imbalanced dataset and high dimensionality of data are the key issues observed in credit card fraud detection. The use of various machine learning algorithm has been utilized for identifying anomalies in credit card transaction, focusing on the problem of imbalanced dataset and reduction of dimension which were carefully reviewed and studied. The study investigates the impact of imbalanced datasets on PCA-based fraud detection and provided detailed techniques such as Random Oversampling, SMOTE & Random Undersam- pling to handle imbalanced datasets and various classification as well as anomaly detection methods. Additionally, given the labelled nature of the dataset, various methods are reviewed like Logistic Regression, Random Forests, and Decision Trees. This study analyses and compares the performance of these methods before and after applying PCA and addressing data imbalance to assess their effectiveness in detecting credit card fraud.

DOI: 10.61137/ijsret.vol.10.issue6.391

Optimizing Information Management, Security, and Analysis with Database Technologies
Authors:-Greeshma Muraly

Abstract- Database technology has been a central focus for organizations and businesses involved in managing information. As the amount and complexity of data continue to increase, efficient data management has become more critical. This paper examines the wide-ranging uses of database systems across different sectors. It starts with an overview of both relational and non-relational databases, then explores their applications in areas such as enterprise management, retail, education, and government/public services. In enterprise management, databases ensure data is timely, accurate, and reliable, forming the foundation for effective information handling. In retail, they support inventory management, sales analysis, and improve customer interactions. In education, databases help manage student records, support teaching insights, and contribute to online learning platforms. For government and public services, databases enhance information sharing, promote transparency, and are essential for crisis management and emergency response. This paper highlights the diverse and crucial roles of database systems while also addressing current research trends and future advancements in the field.

DOI: 10.61137/ijsret.vol.10.issue6.392

Development of an AI-Powered Chess Engine Using Minimax Algorithm and Genetic Algorithm for Evaluation Function
Authors:-Rishi Kiran Karnatakam, Kalyani Gullaeni, Sai Tarun Siri Vadlakonda

Abstract- This project demonstrates a high level processing chess engine employing the Minimax algorithm along alpha-beta pruning, one more added feature used is a genetic algorithm which proves useful to make decisions and performance higher. While the Minimax algorithm is a cornerstone of game theory, which helps one to discover best moves and counter-moves in order not to lose in games like chess, with Alpha-beta pruning you can limit the number of nodes that are evaluated and hence restrict computational power needed without loosing optimality. Our evaluation function rates board states, based on which we use a genetic algorithm to fine-tune it. The optimal criteria are formed by the selection and combination of those evaluation functions over generations, while the genetic algorithm evolves a population of candidate solutions. This continuous refinement allows the evaluation function to improve as it gives a better result. While playing, the engine uses the so-called Minimax algorithm with alpha-beta pruning to look ahead and move sequences up to a certain depth for better decision-making. We tackle both tactical and strategic parts of chess in our implementation, showing strong play against humans. The project has had an analysis, which shows that the move selection and game outcomes are superior to conventional Minimax-based engines. This breakthrough in the class of Minimax algorithms achieves higher intelligence levels in computer chess, drastically changing gameplay for both fun and competitive purposes.

DOI: 10.61137/ijsret.vol.10.issue6.393

Shaping the Social Commerce Landscape: Trends, Challenges, and Opportunities for Brands and Creators
Authors:-Jason Zeng

Abstract- Social Commerce (S-Commerce) is transforming the retail landscape by combining social media platforms with e-commerce to create a more engaging and personalized shopping experience. This paper looks into the challenges and future opportunities that come with S-Commerce. Some of the main challenges include concerns about data privacy and security, trust issues in online transactions, difficulties in integrating social platforms with e-commerce systems, and managing user-generated content. On the other hand, the future of S-Commerce presents exciting opportunities, such as the use of artificial intelligence (AI) to create customized shopping experiences, the rise of social commerce marketplaces, and the growing significance of video and live-streaming content. These trends provide substantial potential for businesses to improve customer engagement, boost sales, and innovate their digital commerce strategies. The paper delves into these dynamics and discusses how businesses can tackle the challenges while seizing the emerging opportunities in S-Commerce.

Developing a Web Application for Financial Statement Analysis: A User-Centric Approach
Authors:-Assistant Professor Md. Alim Khan, Mimansha Pranjal, Md. Ahbab Khan, Sudhakar Singh, Achint Raghuwanshi

Abstract- This application is designed to streamline the analysis of financial statements by allowing users to easily upload company data for comprehensive evaluation. By leveraging advanced algorithms, the application conducts thorough ratio analysis and trend analysis, converting raw financial data into meaningful visual insights, including graphs, pie charts, and heatmaps. These visual representations enhance the understanding of a company’s financial health, revealing trends and performance metrics over time. In addition to historical analysis, the application incorporates sophisticated predictive analytics to forecast the company’s financial performance over the next five years. This feature enables stakeholders to make informed strategic decisions based on projected outcomes. By integrating historical data analysis with predictive modeling, this tool empowers investors, financial analysts, and business managers to identify potential risks and uncover growth opportunities. Ultimately, the application enhances financial decision-making capabilities, providing users with a robust framework for evaluating company performance and making strategic investments. With its user-friendly interface and powerful analytical features, this application is poised to revolutionize how financial data is interpreted and utilized.

DOI: 10.61137/ijsret.vol.10.issue6.394

Design and Development of Exam Kit for Children with Dysgraphia Disorder
Authors:-Pavana A, Rakshitha G A, Sahana Shirishail Patil, Nagesh P, Dr. Jenitta J

Abstract- Children with Dysgraphia, a learning disorder that affects handwriting and fine motor skills, face significant barriers to academic progress and confidence building. This project introduces a novel. By integrating a Raspberry Pi with Optical Character Recognition (OCR) and advanced machine learning algorithms, the system provides precise, real-time feedback on let- ter formation, spacing, and stroke direction. The kit incorporates an intuitive interface, supported by a TFT display, QPC 1010 camera, and peripheral devices, ensuring accessibility and ease of use.To enhance engagement, gamified learning elements are in- tegrated, fostering an enjoyable and motivational environment for skill development. The system seeks to increase self-confidence, enhance motor coordination, and improve handwriting accuracy. By establishing a connection between technology and education. This project provides a portable and scalable solution for schooling that enables kids with dysgraphia to overcome obstacles and succeed academically.

DOI: 10.61137/ijsret.vol.10.issue6.395

A Bugs of C Programming
Authors:-Tapasya Mandar Mate

Abstract- A bug is an error in a computer program that causes it to behave unexpectedly or produce incorrect results. The focus of this study is on detecting, analyzing, and fixing of c programming bugs. The process of finding bugs — before users do — is called debugging. Debugging starts after the code is written and continues in stages as code is combined with other units of programming to form a software product, such as an operating system or an application. This research paper is about details explanation about the bug which mostly occurs while doing c programming.

Energy Storage Systems
Authors:-Ahmed R. Alharbi

Abstract- This review paper provides an in-depth analysis of diverse energy storage systems, emphasizing their significance, operating principles, and practical applications in tackling contemporary energy issues. As the global shift towards sustainable energy gains momentum, effective Energy Storage Systems (ESS) play a pivotal role in maintaining the balance between supply and demand, especially in the integration of renewable energy sources. The paper explores an extensive array of energy storage solutions, such as Thermal Energy Storage (TES), Chemical Energy Storage (CES), Electrochemical Energy Storage (EcES), Electrical Energy Storage (EES), Hybrid Energy Storage Systems (HES), and Mechanical Energy Storage (MES). By conducting a comparative assessment, it highlights the strengths and weaknesses of each approach and provides insights into emerging trends and challenges within the sector. Furthermore, the study focuses on optimizing Gravity Energy Storage (GES) systems using the Taguchi method to improve energy efficiency and system reliability, showcasing the potential of GES as a viable and adaptable solution for sustainable energy storage.

DOI: 10.61137/ijsret.vol.10.issue6.397

Fruits and Herbs Online Shopping
Authors:-Subaranjani BS, Deepavarthini S, Karpagam P

Abstract- This project brings the entire manual process of Fruits and Herbs Online Shopping which is built using Asp.NET as a front end and SQL Server as a backend. An online Fruits and Herbs shop that allows users to check for various Fruits and Herbs products available at the online store and purchase online. This project helps the users in curing its disease by giving the list of fruits and herbs that the user should consume in order to get rid of its disease. The main purpose of this project is to help the user to easily search for herbs and fruits that will be good for the health of the user depending on any health issue or disease that he/she is suffering from. This system helps the user to reduce its searching time to a great extent by allowing the user to enter its health problem and search accordingly. The admin can add fruits and herbs to the system and its

Predictive Maintenance with AI for Smart Homes
Authors:-Revathi Renjini, Associate Professor S R Raja

Abstract- As homes are increasingly adopt smart technologies, their reliability as well as longevity have become paramount to avoid unnecessary downtime and ensure continuous, efficient operations. By incorporating Artificial Intelligence (AI) and Internet of Things (IoT) technologies this research enhances predictive maintenance and thereby contributing sustainability goals. Sensors are utilized to monitor real-time data like temperature, pressure, and vibrations from connected devices and systems. Using the machine learning models – linear regression and decision trees, this research demonstrates how AI can extract actionable insights from sensor data. This research showcases the potential to create more reliable, sustainable, and efficient predictive maintenance solutions that are not only low-cost and accessible but can be adapted for both small-scale and large industrial applications. These advancements will further enhance the predictive capabilities of the system and support long-term environmental sustainability by continuously optimizing resource consumption and reducing waste generation.

DOI: 10.61137/ijsret.vol.10.issue6.398

Automating Complex Workflows in Cloud-Based Applications: Software Quality Assurance Process Driven Practices
Authors:-Raghavender Reddy

Abstract- Modern software systems are becoming increasingly complicated due to which the demand for a reliable, scalable system is on the rise. Cloud-based software-intensive systems (C-SIS) are emerging as the most significant means of meeting these challenges: flexibility, scaling, and increased reliability through distributed computing. This paper looks at the design and implementation of cloud-based systems as they are capable of leveraging the advantages offered by the cloud infrastructure for high availability, fault tolerance, and performance at scale. Cloud-based software-intensive systems are supposed to be a framework for developing systems that are reliable and scalable. The framework brings together the best practices related to cloud architecture, towards automated scaling, load balancing, and fault-tolerance mechanisms to adjust dynamically to varying workloads for always-on service availability. It also discusses the need for microservices and containerization as powerful components for modular and scalable solutions. The results of our experiments demonstrate that this proposed system is able to handle large-scale applications, leading to an understanding of its different performance, fault tolerance, and scalability under certain conditions. This study throws light on how the cloud-based software-intensive systems have a bright perspective to transform the industrial concept, robustly providing high performance and scalable solutions to meet today’s ever-increasing demands of computing environments.

Smart Surveillance Robotic Rover Using ESP32-CAM and Node MCU
Authors:-N Praveen, Professor S Swarnalatha

Abstract- Robotics is a field that combines engineering, technology, and science to design, build, and operate robots. Robots are machines that can perform tasks that are repetitive, complex, or dangerous for humans. They can be controlled by humans or operate autonomously. Robotics deals with the design, construction, operation, and use of robots and computer systems for their control, sensory feedback, and information processing. This project presents the design and implementation of a Smart Robotic Rover that integrates an ESP8266 microcontroller with various sensors and modules to achieve autonomous navigation and real-time data transmission. The rover is equipped with ultrasonic sensors for obstacle detection and Previous studies have demonstrated their effectiveness in providing real-time distance measurements, A GPS module for location tracking Such As outdoor navigation and autonomous vehicles Systems, GPS provides accurate location data, which is essential for tasks that require precise positioning. A BMP180 sensor for environmental monitoring, systems for measuring temperature, pressure, and altitude and a servo motor for directional control. The system is controlled remotely via the Blynk platform, and combining it with an ESP32 module for camera control and additional motor functionalities, Research on camera integration in robotics illustrates the benefits of using high- resolution cameras and efficient streaming protocols for real-time visual feedback. The project aims to deliver a comprehensive robotic system that is controllable via a web interface and Blynk application. Blynk’s native IOS and Android mobile apps are most often used as client-facing UI to remotely control the connected devices and visualize data from them in the dashboard The vehicle is designed for autonomous navigation, real-time environmental monitoring, and user-friendly remote control. Allowing for real-time data visualization and interaction. This paper discusses the system architecture, sensor integration, software development, and testing results of the Smart Robotic Rover.

T- Purity and T_C- Purity in Modules
Authors:-Professor Ashok Kumar Pandey

Abstract- An exact sequence E:0⟶A ⟶B ⟶C ⟶0….(1) is called T-pure if any torsion R- module is projective and relative to it and F- copure if any torsion free R- module is injective relative to it. . Since Tis closed under factors and F is closed under sub-modules. Here Walker’s [19] criterion of Co-purity is also applicable in this situation. We also know that 〖Pext〗_T (M,A)=0 if and only if an R- module M is T-pure projective and〖 Pext〗_F (A,M)=0 if it is F – copure injective for all A⊆M. In particular 〖Pext〗_T (T,A)=0 for all T∈T. We write the torsion sub-module of A⊆M by σ(A). Walker proved that the class of I- pure (J- copure) sequences form a proper class whenever I(J) is closed under homomorphic images (sub-modules) of an R- module M and if I(J) is closed under factors (sub-modules) then for any I- pure (J- copure) sequence E:0⟶A ⟶B ⟶C ⟶0 if E ∈π^(-1) (I) (E ∈i^(-1) (I)) and hence in this case the earlier notion of purity coincides with Walker’s I- purity (J- copurity ) . A sequence E:0⟶A ⟶B ⟶C ⟶0 is I- pure (J- copure) if and only if given C^’≤C∈ I, then there existsB’≤B such that B^’≅C’ and A∩B^’=0. We consider an another stronger notion of purity than the Cohn’s purity[11]. If FG denotes the class of all finitely generated R-modules, which is closed under factors. We shall try to develope some characterizations of FG-purity and to determine its relationship with the T- purity and T_C- purity in cyclic torsion modules We also derive some relations of absolutely ϑ- pure modules with it . We try to relate it the with conditions for T- pure projectivity Teply and Golan [18].. We relativize the above concept and also relate it with finite projectivity of Azumaya [8] with respect to a torsion theory and to study the inter-relationship between these concepts. Finite σ-projectivity, (FG,σ)- pure flatness, cyclically σ- pure projectivity and cyclically σ- pure flatness, the concept of locally σ- projectivity and locally σ- splitness are also considered here and we study its inter-relationship with (FG,σ)- purity and semi-simple module.

Optimizing Business Outcomes through Data-Driven Decision-Making: Techniques for Complex Dataset Analysis
Authors:-Assistant Professor D. Priyanka, Assistant Professor P. Anjaneyulu, Assistant Professor Y. Manaswini

Abstract- The widespread adoption of Cloud Computing technology in industry, education, and government sectors has made it a standard for IT implementation. Data leakage is one of them, particularly, the unauthorized transfer of information from one environment to any other domain. Data leakage has been a problem much before data was maintained digitally. It is therefore vital to prevent and detect this leakage so that the cloud service provider’s reputation is not jeopardized. Furthermore it is integral that users’ data confidentiality, integrity, and availability is not compromised. Inmost cases, data are handled by a third-party software whose security procedures are unknown to the user. This software serves as a bridge between the user and the cloud service provider. To resolve the issue of data leakage, several methods have already been proposed such as watermarking, cryptographic and probabilistic techniques. This paper, however, aims to use a revised version of the probabilistic approach by encrypting the user data even before it is uploaded through a portal. During the encryption process, a user ID is embedded into the encrypted file. When this file is accessed by another consumer, their user ID is also embedded into the file. Hence it makes it easier for the algorithm to detect the guilty agent by comparing the leaked file against the user file. A list of users who have accessed the file is thus maintained.

Power Consumption Analytics Using Cloud Platforms
Authors:-Muthuraja M, Krishnan T, Prakash Dass R, Deepak kumaran RMG, Bharath G

Abstract- The increasing demand for electricity and environmental concerns have created a critical need for advanced energy management solutions. This study presents an IoT and cloud-based analytics system that provides real-time insights into power consumption, enabling efficient energy utilization. Leveraging ThingSpeak as the cloud platform, the system integrates smart meters to monitor voltage, power factor, and energy trends. Key contributions include real-time anomaly detection, dynamic visualization, and customizable alert systems. The proposed methodology enhances user engagement and supports scalability for diverse energy applications.

DOI: 10.61137/ijsret.vol.10.issue6.399

Full Stack Web Application for Prediction and Diagnosis of Heart Disease
Authors:-Assistant Professor Ms. Dornadhula Danya, Suraj A U, Moju Kumar B L, Deepak Kumar Singh D, Shubhan GC

Abstract- In the modern era, Cardio-vascular disease has high prevalence and rate of mortality which proves how critical, identification and intervention strategies are, further highlighting the importance of incorporating this in developing heart disease prediction systems. The heart prediction system research revolves around using AI-driven techniques techniques to strengthen and make heart disease risk prediction robust and effective. The paper explains methodology, dataset characteristics, experimental setup, results and the design of the models in a AI-driven techniques heart prediction system. Additionally, the practical implications of the research output are discussed regarding the use of the system in real life for alleviating heart disease predictions and strategies.

DOI: 10.61137/ijsret.vol.10.issue6.400

Securing the Digital Age: A Look at Cryptography and Network Security
Authors:-Professor Mugdha Dharmadhikari, Mr. Vaishnav Sabale

Abstract- The digital world thrives on the secure exchange of information across vast networks. This paper explores cryptography as a fundamental pillar of network security, ensuring data confidentiality, integrity, and authenticity. We delve into the core objectives of network security and how cryptography achieves them through encryption techniques. We explore both symmetric-key and asymmetric-key cryptography, along with their strengths and limitations. The paper further examines cryptography’s role in guaranteeing data integrity and sender authentication. We acknowledge the limitations of cryptography, including computational demands and the looming threat of quantum computers, which necessitates the development of post-quantum cryptography. Finally, the paper emphasizes the crucial role of ongoing research and development in cryptography to safeguard the ever-expanding digital landscape.

DOI: 10.61137/ijsret.vol.10.issue6.401

A Comparative Analysis of Lab View and PyTorch for Machine Learning: The gap between Experimentation and Production
Authors:-Archana Narayanan, Vishrut Jha, Joanne Anto

Abstract- This paper presents a comparative analysis of handwritten digit recognition performance between LabVIEW and PyTorch frameworks, utilizing a Convolutional Neural Network (CNN). The model is designed to classify digits from the MNIST dataset, which consists of 28×28 grayscale images of handwritten digits (0–9). The dataset includes 60,000 training images and 10,000 test images, providing a standardized benchmark for evaluating model performance. Metrics such as accuracy, training time, memory usage, and inference speed are evaluated. The results provide insight into the strengths and weaknesses of these frameworks in terms of efficiency, scalability, and usability. Results indicate that while both frameworks are effective, PyTorch offers faster training and inference, whereas LabVIEW demonstrates marginally better training accuracy.

DOI: 10.61137/ijsret.vol.10.issue6.402

A Review on Effects of Water Proofing Admixture on Concrete
Authors:-M.Tech Scholar Viplove Lahori, Professor Afzal Khan

Abstract- This review explores the effects of water-proofing admixtures on concrete properties, focusing on their impact on durability, strength, and performance. Water-proofing admixtures are additives designed to reduce water permeability and enhance the resistance of concrete to moisture ingress, which is critical for ensuring the long-term durability of structures, especially in environments with high humidity, rainfall, or exposure to aggressive chemicals. The study systematically examines the various types of water-proofing admixtures, including crystalline, hydrophobic, and integral admixtures, and evaluates their performance characteristics such as compressive strength, permeability, durability, and resistance to chemical attacks. The influence of these admixtures on concrete microstructure, hydration process, and pore structure is discussed in detail. Additionally, the review highlights the factors that affect the effectiveness of water-proofing admixtures, such as admixture type, dosage, water-cement ratio, and curing conditions.

AI Based Smart Energy Meter for Data Analytics
Authors:-Assistant Professor Mrs.B. Christyjuliet, Dinesh Kumar.B, Divya.G, Kaviraj.S, Monisha.R

Abstract- The proliferation of smart meter technology offers vast opportunities for harnessing real-time data to optimize energy consumption, predict demand, and support sustainable energy grids. This paper explores the integration of artificial intelligence (AI) techniques, such as machine learning and deep learning, into smart meter data analytics, enhancing the accuracy of predictions and anomaly detection. With the rise of big data from millions of connected devices, AI-based analytics are vital to efficient energy management. We present a comparative analysis of various AI models used for smart meter data analytics and propose improvements for their real-time applications.

DOI: 10.61137/ijsret.vol.10.issue6.404

A Review of Herbal Technology
Authors:-Averineni Ravi Kumar N, Deepa Ramani

Abstract- Herbal Drug Development Plant Selection and Identification The first step is identifying a plant with potential medicinal properties. Ethnobotanical surveys, historical use, and scientific literature guide this process. Extraction and Isolation of Active Constituents Different extraction methods (e.g., solvent extraction, steam distillation, supercritical fluid extraction) are employed to isolate the active ingredients from plant material. Techniques like chromatography and spectroscopy are used to identify and purify these compounds. Standardization Standardization ensures that a herbal product contains a consistent amount of active compounds in each batch. This is crucial for reproducibility and efficacy. Preclinical Studies Laboratory testing on animals and in vitro models to assess the biological activity, toxicity, and pharmacokinetics of the herbal product. Clinical Trials Human trials are conducted to evaluate the safety, efficacy, and dosage of the herbal drug. Technological Approaches in Herbal Drug Development Extraction Techniques Solvent Extraction The most common method, where solvents like ethanol or water are used to extract bioactive compounds. Supercritical Fluid Extraction (SFE) Uses supercritical CO2 as a solvent, offering a cleaner and more efficient extraction method. Microwave-Assisted Extraction (MAE) Uses microwave energy to enhance the efficiency of the extraction process. Ultrasonic Extraction Utilizes high-frequency sound waves to enhance solvent penetration and compound release. Formulation Development Herbal products may be formulated into various forms

DOI: 10.61137/ijsret.vol.10.issue6.405

Automated Greenhouse Agricultural System (AGAS): Enhanced Efficiency and Sustainability in Agricultural Practices
Authors:-Justine P. Fuertes, Mary Jean R. Arevalo, Glyza Nicole M. Ewag, Michael P. Tumilap

Abstract- This research aimed to develop a prototype of an automated Greenhouse Agricultural System (AGAS) for efficient and sustainable cultivation of plants in tropical regions. The AGAS prototype was built using an Arduino Uno microcontroller, which monitors and regulates temperature, humidity, and soil moisture, utilizing sensors and a servo motor for water distribution. Data is transmitted to a website for remote monitoring and control. Data were analyzed mainly using percentages, mean and t-test of independent means. Results showed that, the system achieved a 100% success rate in six trials, demonstrating accurate soil moisture detection, effective servo motor operation, and reliable pump functionality; the website is 100% success rate in four trials in recording analog values, it successfully maintained optimal growing conditions for lettuce, showcasing its potential to improve crop yields and resource efficiency; and the AGAS is efficient compared to the traditional greenhouse system in terms of temperature, humidity and soil moisture. This highlights the significance of AGAS in addressing the challenges of unpredictable weather patterns and resource scarcity in tropical regions. Further development, including a user-friendly application, HVAC system, and error detection mechanisms, is recommended. The AGAS holds the potential to revolutionize greenhouse agriculture, promoting sustainable practices and enhancing food security.

DOI: 10.61137/ijsret.vol.10.issue6.406

Computer Network Secure Communication and Encryption Algorithm
Authors:-Janani J, Associate Professor Dr S R Raja

Abstract-Due to the continuous progress of Internet technology, computer network communication service has replaced the traditional short message service and multimedia message service. In order to ensure the security of the instant messaging system, some advanced security encryption algorithms are used in the communication system to prevent attacks and information leakage. By using encryption algorithms, the network security research based. Our system operates on a network of nodes, where each node plays a crucial role in ensuring the security and integrity of transmitted data. The SHA-256 algorithm is employed for generating hash values, providing a secure and efficient means of verifying data integrity. Furthermore, we implement AES (Advanced Encryption Standard) for file encryption, enhancing the confidentiality and privacy of sensitive information. AES is a symmetric key encryption algorithm renowned for its strength and efficiency, by combining SHA-256 for integrity checking and AES for encryption, we Include Face Change Attaining methods to prevent from attackers In Face Change that can support both anonymizing real IDs among neighbor nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Our system offers a robust defense against various cyber threats, including data breaches and unauthorized access. Prevent malicious actors from intercepting or tampering with encrypted data, our system employs advanced encryption techniques and secure communication protocols.

DOI: 10.61137/ijsret.vol.10.issue6.407

AI Enabled Digital Media Versus Print media
Authors:-Research Scholar Seethal George, Dr. Prachi Chathurvedhi

Abstract-The introduction of artificial intelligence ultimately changing the media landscape, this lead to digital divide between traditional media and modern media. This research paper emphasize on the challenges opportunities strength and weakness faced by traditional media in this artificial intelligence era. Modern technology can replace the older one see but in the case of print media that is News Papers and magazines are not replaceable. Digital technological advancements are a part of our life but usage of traditional print medias became a habit of our generation. Through comparative analysis and expert interview this paper prose how artificial intelligence influence traditional media.

DOI: 10.61137/ijsret.vol.10.issue6.408

Data Narratives Using AI: A Framework for Automated Insight Storytelling
Authors:-Soundhar B, Associate Professor Dr S R Raja

Abstract-In today’s data-driven world, organizations are faced with an ever-growing volume of raw data that often requires sophisticated analysis to extract meaningful insights. However, the complexity of these insights can make it difficult for decision-makers, especially non-experts, to understand and act on the information. This paper proposes a novel framework that leverages Artificial Intelligence (AI) to automatically generate data narratives, transforming raw data into human-readable insights. The framework integrates data preprocessing, advanced AI techniques, and natural language processing (NLP) models to construct compelling and insightful narratives. We present a detailed methodology, including the use of clustering, trend analysis, and regression models to extract key insights from diverse data sources. The generated narratives are tested on multiple datasets, demonstrating their effectiveness in conveying actionable insights in an easily understandable format. Our results show that AI-generated data stories not only provide clarity and context but also enhance decision-making processes across various industries. Future work will focus on enhancing the framework’s adaptability to real-time data and improving narrative customization for different stakeholders.

DOI: 10.61137/ijsret.vol.10.issue6.409

A Robust and Secure Image Watermarking Technique for Digital Data: State-of-the-Art
Authors:-Bhupendra Kumar Bhardwaj, Professor Dr. Satya Singh

Abstract-With the fast development of computer technology, research in the fields of multimedia (text, image, audio and video clip) security, image processing and robot vision have recently become popular. Digital image watermarking techniques is one of the best techniques for image authentication. Watermarking algorithms are designed to embed and extract digital watermarks within digital content, such as images, audio, or video. The basic objective of the watermarking technique is to enhance imperceptibility, capacity and robustness. When developing an effective watermark method, it’s necessary to have a highly balanced trade-off between imperceptibility, capacity, and robustness. In this paper we presence about watermarking system, requirements for digital image watermarking, challenging issue of watermarking, application of watermarking, importance of watermarking, image watermarking classification, various watermarking techniques, attacks on watermarking process, performance measure for evaluating the image quality using metrics and a short view of watermarking tools. The work gives a view on various watermarking schemes in digital images that give new ideas to improve the already existing techniques.

DOI: 10.61137/ijsret.vol.10.issue6.410

Revolutionizing Neonatal Care: The Role of Embrace Innovations in Addressing Infant Mortality in Resource-Constrained Settings
Authors:-Ashish Pattnaik, Rishika Patwari, Rishi Kumar Karnani, Aayushman Joshi

Abstract-This paper explores the innovative business model of Embrace Innovations, a social enterprise committed to tackling the critical issue of infant mortality in resource-constrained settings, especially in India. Founded with the mission of offering affordable and effective infant care solutions, Embrace has developed the Embrace Infant Warmer as a cost-effective alternative to traditional incubators. In analysing the operational strategies, market dynamics, and impact of Embrace’s products on neonatal health outcomes, the study uses a mixed-method approach through applying qualitative and quantitative research methods. Conducting in-depth interviews and surveys with relevant stakeholders which lead to important discoveries about how Embrace was able to effectively penetrate these markets through its unique value proposition: affordability, portability, and user-friendliness. The paper discusses the challenges of the organization, such as high maintenance costs and regulatory compliance issues. Ultimately, this research would highlight the potential for Embrace Innovations to transform infant healthcare through continuous innovation and strategic partnerships, thereby contributing significantly to reducing infant mortality rates globally.

DOI: 10.61137/ijsret.vol.10.issue6.411

AI-Based Framework for Predicting Quantum State Transitions in Topologically Protected Material
Authors:-Soundhariya Ravi, Associate Professor Dr S R Raja

Abstract-Quantum state transitions in topologically protected materials have garnered significant attention for their potential applications in quantum computing, spintronics, and material science. Predicting these transitions under varying external conditions remains a challenge due to the intricate interplay of quantum effects and topological invariants. This study proposes an AI-based framework that leverages deep learning techniques to predict quantum state transitions in such materials with high precision. The framework utilizes a custom neural network architecture trained on data derived from simulations and experimental results. By incorporating topological invariants and environmental variables as features, the model accurately predicts phase transitions and provides insights into the factors driving them. The results demonstrate over 95% prediction accuracy, outperforming traditional simulation methods in terms of computational efficiency and scalability. This work lays the foundation for integrating AI into quantum materials research, offering tools for designing next- generation quantum devices.

DOI: 10.61137/ijsret.vol.10.issue6.412

Role of Data Mining and AI on Human Health
Authors:-Dharmendra Kumar Nagrani, MR.B.L.Pal

Abstract-Data Mining and AI are revolutionize the medical field by providing enhanced understanding of disease trends, increasing accuracy in diagnosis, and driving the development of tailored healthcare solutions. This document investigate into how data mining and Artificial intelligence methodologies influence various dimensions of human health, with an emphasis on predictive analytics, diagnostic imaging, real time health tracking, and customized treatment options. Techniques in data analytics, including categorization, grouping, and rule based mining, are utilized on extensive data sets, assisting healthcare professionals in making informed data centric choices for disease prevention and management. AI techniques, featuring ML and deep learning frameworks , significantly improves diagnoses, particularly within medical Imaging, where these models showcase remarkable accuracy in detecting diseases at early stages. In addition, wearable technology and mobile health platforms offer continuous data for ongoing health assessment, facilitating timely medical interventions. Nonetheless, applying data mining and AI in healthcare, introduces challenges, especially concerning data privacy, interpretability of models and ethical issues. This research addresses these hurdles and proposes strategies to bolster data protection, enhance model clarity, Forster patient confidence. With ongoing progress and mindful applications, data mining and Artificial intelligence present considerable potential for enhancing health outcomes, supporting preventive measures and leading to individualized and precision medicines.

DOI: 10.61137/ijsret.vol.10.issue6.413

Intelligent Baby Monitoring System Using Raspberry Pi and Sensors
Authors:-Sankalp shant, Shreelekha K, Siri Vennela KS, Tanya Raj, Dr .Nirmala S

Abstract-With the increased demand for advanced childcare solutions, the development of an intelligent and reliable baby monitoring system using the versatile Raspberry Pi has been encouraged. This project is focused on creating a comprehensive monitoring solution that prioritizes the safety and well-being of infants through the integration of sophisticated audio monitoring and environmental sensing capabilities using various sensors. This new system utilizes the central processing unit Raspberry Pi 4 Model B and interfaces nicely with high-quality microphone capability to pick up sound; it comes equipped with environmental sensors capable of monitoring essential conditions in temperature and humidity. The functionalities are advanced and include motion detection, which notifies caregivers upon any baby movement, while cry detection informs caregivers of a crying baby within seconds of its cry. A two-way audio system that connects caregivers with their children can converse and communicate with the baby real-time, providing yet another level of interaction and comfort. The application will be designed so that parents or guardians, through a mobile application, can have instant alerts when their baby’s condition arises from virtually any location. This system was designed to be cost-effective and easy to set up; it can be highly scaled to meet the needs of the users. There is always an integration for push notifications via mobile devices. By incorporating these advanced features and focusing on user-friendly design, this baby monitoring system represents a significant advancement in the realm of smart parenting tools, addressing the critical need for reliable and intelligent childcare solutions in contemporary households.

DOI: 10.61137/ijsret.vol.10.issue6.414

Leveraging Predictive Analytics and Cybersecurity Measures for Enhancing Risk Management and Resilience in Global Supply Chains
Authors:-Erumusele Francis Onotole

Abstract-In today’s interconnected global supply chains, the integration of predictive analytics and advanced cybersecurity measures has become a pivotal strategy for fortifying risk management and enhancing resilience. The COVID-19 pandemic underscored the vulnerabilities of supply chains, prompting organizations to adopt cutting-edge technologies to mitigate disruptions and ensure continuity. This paper explores the critical interrelationship between predictive analytics, cybersecurity, and supply chain resilience, highlighting their combined potential to create robust and adaptable systems. The study delves into predictive analytics for risk identification and mitigation, the role of cybersecurity in addressing digital threats, and the need for a holistic risk management approach. Empirical evidence and theoretical insights are discussed to present actionable strategies for organizations aiming to enhance their supply chain resilience in an increasingly uncertain global environment.

DOI: 10.61137/ijsret.vol.10.issue6.628

End-to-End Encryption, Role-Based Access Controls, and Audit Logs in Safeguarding Electronic Health Records – A closer look at the features housing EHR
Authors:-Erumusele Francis Onotole

Abstract-The rise of Electronic Health Records (EHRs) has revolutionized the way health care is practiced globally, particularly in providing patients with effective and precise care. Nevertheless, given the types of information EHRs contain, they are vulnerable to malicious attacks and access by unauthorized persons. The paper focuses on the importance of end-to-end encryption, role-based access control, and audit logs in maintaining optimal security of EHR data. These aspects are discussed in such a way that their combined effect is presented along with the individual functionality of circumstances and how each of them contributes to security, the legal requirements, and the stakeholders.

DOI: 10.61137/ijsret.vol.10.issue6.629

Analyzing the Loss of Sound Transmission for a Rectangular Cross Section Muffler with a Different Aspect Ratio in Same Gas Volume
Authors:-Associate Professor Amit Kumar Gupta

Abstract-The measurement of the acoustical transmission loss of an expansion chamber muffler with a rectangular cross section and different cross section aspect ratios is presented in the study. An essential component of noise management for reducing noise from gas flow sources, such as machinery exhaust, is a muffler, also known as a silencer. As a component of an internal combustion engine’s exhaust system, mufflers are usually placed along the exhaust pipe to lessen noise. One-dimensional waves are utilized as simulation tools.

DOI: 10.61137/ijsret.vol.10.issue6.415

Enhancement of Security in Wireless Network
Authors:-Mrs.C.Radha, Mr.R.Midunkumar, Mr.S.Muralibabu, Mr.V.Partheeban, Mr.C.Mani

Abstract-Wireless networks have become ubiquitous in our modern digital landscape, facilitating connectivity and enabling seamless access to information. However, the inherent vulnerabilities of wireless communication pose significant security challenges. This paper provides a comprehensive overview of wireless network security, examining various aspects such as encryption, authentication mechanisms, access control, intrusion detection, and physical security measures. The discussion begins by highlighting the importance of encryption protocols, such as WPA2 and WPA3, in safeguarding data transmitted over wireless networks. Strong encryption mechanisms are essential for ensuring the confidentiality and integrity of sensitive information, protecting against eavesdropping and data tampering. The aim of this study was to review some literatures on wireless security in the areas of attacks, threats, vulnerabilities and some solutions to deal with those problems. It was found that attackers (hackers) have different mechanisms to attack the networks through bypassing the security trap developed by organizations and they may use one weak pint to attack the whole network of an organization. Overall, this paper provides valuable insights into the various techniques and strategies for enhancing security in wireless networks.

The Role of Heavy Metals in Disrupting Intercellular Communication via Exosomes
Authors:-Talha, Usama Zahoor, Faseeh Ur Rehman, Muhammad Usama, Muhammad Shafique, Atif Ali, Ahmad Abid, Muhammad Faisal Ramzan, Muhammad Abdullah Sohail, Muhammad Zubair

Abstract-Small extracellular vesicles secreted by most cell types have been crucial for intercellular communication in transferring biologically active molecules, such as proteins, lipids, and RNA. The vesicles regulate the physiological processes that contribute to pathological conditions such as cancer. Exposure to heavy metals, including arsenic, cadmium, and lead, disrupts communication by interfering with the biogenesis of exosomes, the cargo that is transferred within them, and their release. This review discusses the molecular mechanisms through which heavy metals affect exosomes, their downstream effects on recipient cells, and the potential of exosome-based biomarkers for detecting and mitigating heavy metal toxicity. The discussion also brings out therapeutic opportunities and future research directions.

DOI: 10.61137/ijsret.vol.10.issue6.416

Enhancing Software Quality through Automation Testing
Authors:-Associate Professor Dr.S.R. Raja, Research Scholar B. Karthigeyan

Abstract-Web automation testing has become an essential component of modern software development, enabling developers to ensure the quality, functionality, and performance of web applications. It leverages automated tools and frameworks to perform repetitive and complex testing tasks, thereby reducing human error and speeding up the development lifecycle. This paper explores the methodologies, tools, and advancements in web automation testing, presenting a proposed system designed to enhance efficiency and reliability. Through an experimental prototype, we demonstrate the effectiveness of the proposed architecture in streamlining testing processes. The paper also addresses the challenges faced in script maintenance, scalability, and adaptability of automated tests in dynamic web environments. Finally, we outline future directions for research in this domain, emphasizing the role of AI and real-time analytics in shaping the next generation of automation testing tools.This paper explores the methodologies, tools, and advancements in web automation testing, focusing on overcoming challenges like script maintenance, handling dynamic elements, and frequent application updates. Through an experimental prototype, the proposed system demonstrates improved efficiency by integrating modular test designs and advanced reporting mechanisms.

DOI: 10.61137/ijsret.vol.10.issue6.417

Uplifting a Farmer through Connected Ecosystem
Authors:-Professor Rohini, G Ravi Teja, C Vinay Kumar Reddy, A Vidhyadhari, P Monish

Abstract-This project focuses on developing a comprehensive platform that bridges the gap between farmers and consumers, allowing users to purchase agricultural products directly from farmers. The application provides seamless online payments, user and farmer profile management, and real-time inventory updates. Administrators play a key role in fostering trust by onboarding verified farmers and uploading schemes that are beneficial to them. Future expansions include vehicle and land renting functionalities as well as fertilizer management to support farmers further. This app allows farmers to effortlessly rent agricultural machinery, such as tractors and harvesters, at nominal costs, empowering them with technology that was previously out of reach. Through user-friendly interfaces and robust backend support, farmers can connect with rental providers, manage bookings, and access real-time updates. Administrators oversee the system, ensuring transparent transactions and efficient dispute resolution, while users can explore and contribute to the ecosystem. Our goal is to uplift the agricultural community by reducing operational costs, enhancing productivity, and fostering collaboration. By leveraging digital tools, this app bridges the gap between modern technology and traditional farming practices, paving the way for a sustainable and prosperous agricultural future.

Novel Prediction of Diabetes Disease by Comparing K-Means with Logistic Regression with Improved Accuracy
Authors:-R.Vinoth, Associate Professor Dr.S.R.Raja

Abstract-Aim: This study aims to evaluate the effectiveness of the K-Means algorithm in comparison to Logistic Regression (LR) for analyzing a diabetes dataset. Diabetes is a critical and potentially fatal condition, and as it remains incurable, its prevention and management are vital public health concerns. Materials and Methods: For this research, a substantial dataset was sourced from the Kaggle Dataset – Diabetes Disease Analysis and Prediction, encompassing 13 clinical features pertinent to diabetes. The sample consisted of 10 instances, with additional control variables incorporated to account for possible confounding factors and enhance the accuracy of the findings. Both K-Means and Logistic Regression algorithms were employed for predictive analysis. Discussions: Two distinct analyses were conducted to assess the performance of the K-Means algorithm against the proposed LR algorithm. The outcomes indicated that the enhanced LR method yielded superior results. Result: The mean accuracy for the LR algorithm was recorded at 76.8%, while K-Means clustering achieved a mean accuracy of 46.2%, demonstrating that LR outperformed K-Means. The results suggest that machine learning techniques can effectively predict diabetes. The p-value obtained in this study was 0.001, which is less than the threshold of p=0.05, underscoring the importance of utilizing LR for diabetes prediction. Conclusion: The findings reveal that the extended LR algorithm achieved greater accuracy compared to the K-Means algorithm. Nonetheless, it is noted that Logistic Regression would benefit from a larger sample size to enhance the precision of the results.

AI with a Human Touch: Innovating E-Commerce through Emotion-Sensitive Technologies
Authors:-Umamageswari.GS, Associate Professor Dr S R Raja

Abstract-The swift advancement of artificial intelligence (AI) has dramatically altered the e-commerce landscape, allowing companies to improve customer experiences through increasingly customized and emotionally responsive methods. E-commerce platforms can now offer tailored interactions that connect with customers on an emotional plane by utilizing AI to identify and react to their emotional states, whether through written, spoken, or behavioral indicators. Emotion-cognizant AI systems can comprehend sentiments expressed across various contact points, including chatbots, customer support exchanges, product suggestions, and individualized marketing efforts. These AI systems employ sentiment analysis, natural language processing, and emotional intelligence algorithms to modify response promotions, and product recommendations based on weather a customer is content, irritated, perplexed, or enthusiastic. Consequently, customers receive highly personalized and empathetic interactions that boost satisfaction, build trust, and increase conversion rates. This study examines the newest innovations in AI-powered emotional intelligence for e-commerce, its capacity to enhance customer engagement, and its ramifications for businesses aiming to improve customer loyalty through a more profound understanding of emotional dynamics.

DOI: 10.61137/ijsret.vol.10.issue6.419

The Impact of Oil Sector Deregulation on the Nigerian Economy: Evaluating the Socioeconomic and Financial Implications across Key Economic Segments
Authors:-Dr. Sabina Ego Ekechukwu

Abstract-This study examines the impact of oil sector deregulation on the Nigerian economy, focusing on key economic segments such as households, finance, firms, public sector, and international trade. Utilizing a mixed-methods approach, this research combines quantitative survey data with qualitative observations to capture a comprehensive view of deregulation effects. A sample of 400 respondents, selected through stratified random sampling from relevant stakeholders in the oil industry, was surveyed to ensure representative insights across affected sectors. Anchored in the Circular Flow Model and the General Equilibrium Theory, the study explores how deregulation policies influence macroeconomic stability, cost structures, and resource allocation within Nigeria. Key findings indicate both positive and negative consequences: while deregulation contributes to fiscal savings and potential investment in infrastructure, it has also led to inflationary pressures and increased operational costs for firms. Limitations of this study include its restricted focus on short-term impacts and challenges in capturing the broader social implications of policy shifts. These insights offer policymakers a nuanced understanding to refine future economic strategies.

AcademEase: Revolutionizing Online Assignment Management for Enhanced Academic Efficiency
Authors:-Chethan M S, Associate Professor Dr S R Raja

Abstract-The traditional methods of managing assignments are steadily becoming outdated due to their numerous drawbacks, including inconvenience, inefficiency, and a lack of accuracy. These limitations have prompted a growing need for more effective solutions in the educational domain. With the rapid advancement of web technologies, web-based management systems have gained significant traction and are being widely adopted across various sectors. This paper presents a novel AcademEase: Revolutionizing Online Assignment Management for Enhanced Academic Efficiency that not only integrates the most effective features of existing commercial systems but also introduces innovative functionalities tailored specifically for modern assignment management needs. The proposed system addresses critical gaps in traditional practices by offering a comprehensive platform designed to streamline assignment handling processes for both administrators and students. Key features of the AMS include a user-friendly interface that simplifies the user experience, ensuring that assignments are managed in a convenient, efficient, and systematic manner. Furthermore, the system is designed with a high degree of portability and extensibility, making it adaptable to various educational environments and capable of evolving with future technological advancements. To safeguard sensitive data and ensure secure operations, the system incorporates robust, multi-layered security strategies that enhance its overall reliability. By leveraging the power of web technologies, this innovative system not only improves assignment management workflows but also sets a new benchmark for efficiency, usability, and security in academic institutions. This paper delves into the design, functionality, and benefits of the AMS, showcasing how it effectively meets the demands of modern educational practices.

DOI: 10.61137/ijsret.vol.10.issue6.420

An Overview of Textual Sentiment Analysis and Emotion Recognition
Authors:-Pallavi Suryavanshi, Dr Sunil Patil

Abstract-Opinion mining, another name for sentiment analysis, is a crucial task in natural language processing (NLP) that enables the extraction of subjective information from text. Sentiment analysis can use machine learning algorithms to classify opinions in text into three categories: neutral, negative, and positive. In the Internet age, social networking sites have grown rapidly, making them an essential tool for communicating emotions to individuals all over the world. Many people use music, video, photos, and text to express their ideas or perspectives. Sentiment analysis is inadequate in certain applications; therefore, emotion detection is necessary to accurately ascertain a person’s emotional and mental condition. The degrees of sentiment analysis, different models, and the steps involved in sentiment analysis and emotion detection, challenges faced are all explained in this review study.

DOI: 10.61137/ijsret.vol.10.issue6.421

User-Centered Design in Digital Marketing
Authors:-Abhijit Mojumder, Susmita Biswas

Abstract-Purpose: This thesis investigates how user-centered design (UCD). , user experience (UX) principles can have a remarkable impact on digital marketing campaigns, focusing on consumer engagement. , conversion rates. With the rising complexity of online consumer behavior. , ever-increasing competition in digital marketplaces, leveraging strategic UX design has emerged as a powerful tool for marketers. Methodology: The study adopts a mixed-methods approach, incorporating both quantitative data (such as user analytics, A/B testing results)., qualitative insights (such as interviews, focus groups). A framework is established to evaluate campaign performance metrics, user satisfaction scores, . , conversion funnels within diverse digital platforms—social media, e- commerce websites, mobile applications. Findings: The findings suggest that user-centered design elements—such as intuitive navigation, responsive interfaces, consistent br. ,ing, . , personalization—lead to higher levels of user satisfaction, br. , trust, , customer retention. In addition, campaigns designed around UX principles witnessed a measurable uptick in conversion rates compared to those that lacked deliberate UX planning. Implications: This thesis contributes to the existing literature on digital marketing by incorporating comprehensive UX design strategies. By applying user-centered methodologies, marketers can cultivate more engaging. , persuasive digital experiences, thus boosting key performance indicators (KPIs) such as click-through rates, time on site, average order value., customer lifetime value.

DOI: 10.61137/ijsret.vol.10.issue6.422

A 12 Switch Operated 19-Level Inverter to Reduce Distortion
Authors:-Mtech Scholar Umang Soni, Assistant Professor Shyam Kumar Barode, Assistant Professor Hari Mohan Soni, Assistant Professor Sachin Jain

Abstract-Purpose: The idea of a multilayer inverter originated from the development of inverters to more than two layers in order to lessen distortion from the basic sinusoidal waveform. One drawback of employing multiple level inverters is the installation of more switches, which raises system bulk and cost and reduces system dependability due to the increased component count. In order to address the issue of the system becoming bigger, more expensive, and less dependable with less distortion, this work provides a nineteen-level inverter (19-LI) with fewer switches than a symmetrical H-bridged nineteen-level inverter. The idea is developed using the MATLAB platform, then analysis is done to determine how valuable the final product is.

DOI: 10.61137/ijsret.vol.10.issue6.423

LIXXI-FSRD, A Fuel Efficiency Material “Z” Capsule
Authors:-Reghunath Ramakrishnan

Abstract-New technology to reduce pollution in motor vehicles and increase mileage.

DOI: 10.61137/ijsret.vol.10.issue6.424

Detection of DDOS Attacks and Classification
Authors:-Gopi A G, Professor Dr. M Anand Kumar

Abstract-Distributed Denial of Service (DDoS) attacks are a significant threat to the stability and availability of network services, often resulting in financial and reputational damage to organizations. Detecting and mitigating these attacks is a complex task due to their large scale, diverse attack vectors, and evolving nature. This paper explores various methods for DDoS attack detection and classification, with a focus on leveraging machine learning and statistical techniques. The primary objective is to identify attack patterns in network traffic data and classify them in real-time to distinguish between legitimate and malicious activities. We review traditional methods such as signature-based detection and anomaly detection, alongside modern machine learning-based approaches, including supervised and unsupervised classification techniques. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are evaluated for their effectiveness in detecting various types of DDoS attacks, including volumetric, protocol, and application-layer attacks. Additionally, we discuss the challenges posed by high traffic volumes, the need for low-latency detection, and the impact of adversarial tactics on detection systems. Finally, the paper highlights the importance of developing robust, scalable, and adaptive classification models that can efficiently handle the evolving nature of DDoS attacks in dynamic network environments.

DOI: 10.61137/ijsret.vol.10.issue6.425

Development of an Automated Penetration Testing Tool for Enhanced Cybersecurity
Authors:-Sanskriti Grover

Abstract-The continuous evolution of digitalization and the rapid growth of tools and technologies have led to a parallel rise in sophisticated cyberattacks. Attackers deploy advanced techniques to compromise critical systems, steal sensitive data, and disrupt operations. Traditional vulnerability detection and penetration testing methods, which rely heavily on manual processes and frameworks like Metasploit, are labour-intensive, time-consuming, and prone to human error. To address these challenges, this research presents the development of an Automated Penetration Testing Tool (APTT) to streamline cybersecurity assessments. Integrated with the Metasploit framework, APTT automates reconnaissance, vulnerability scanning, and exploitation, reducing time complexity and human error. Initial testing in diverse environments showed a 50% reduction in testing time and improved reliability of results, making it scalable and adaptable to various security needs.

DOI: 10.61137/ijsret.vol.10.issue6.427

Real-Time Malware Detection for Documents: A Cyber Security Browser Extension for File Protection
Authors:-Aniket Jha, Aaditya Chaudhari, Malay Khant, Anuj Kumar

Abstract-The increasing frequency of malware attacks through document files poses a significant risk to personal and organizational data security. This project focuses on developing a real-time malware detection system as a browser extension to protect users from malicious documents. By leveraging machine learning techniques and heuristic analysis, the extension scans documents uploaded or downloaded through the browser, identifying potential threats in real time. The solution ensures high accuracy in detecting various malware types while maintaining lightweight operation for seamless user experience. The system incorporates a user- friendly interface, automated scanning, and secure cloud-based updates for the detection engine. The proposed extension bridges the gap between cybersecurity and accessibility, providing a practical tool for users to protect themselves from file-based threats. Testing and evaluation demonstrate its reliability and effectiveness, making it a valuable addition to modern cybersecurity solutions.

Ethnomycological Investigation and Domestication of Wild Edible Mushrooms from the Department of Bamboutos (West Cameroon)
Authors:-Kamgoue Ngamaleu Yves Bertin, Sumer Singh Rathore, Sudhanshu Mishra, Donkeng Voumo Sylvain meinrad, Prashakha Jyotiprakash Shula, Nanda Djomou Giresse Ledoux, Ladoh Yemeda Christelle Flora, Essouman Ebouel Pyrus Flavien, Wamba Fotso Oscar, Asseng Charles Carnot

Abstract-Food security remains one of the major problems in the world. Wild edible mushrooms constitute an important source of food due to their nutritional and medical values, as well as a source of income for populations. This study aims to domesticate wild edible mushrooms that grow in the Bamboutos department. An ethnomycological survey was conducted among 154 people through direct and semi-structured interviews in the 04 Districts and in 15 villages of the Department. The macroscopic identification of the different species was carried out in situ using identification keys. The domestication test was carried out in the laboratory, the species inoculated on PDA medium and transplanted onto cereal seeds then onto corn cobs in order to obtain seeds. The seeds obtained were tested on corncob and sawdust substrates with the use of two additives, wheat bran and corn bran.The different substrates composed of slaked lime, urea, fungicide and water. This work reveals that the largest percentage of respondents is made up of men (65%). Knowledge related to the edibility of mushrooms is mainly transmitted by family members (68%). The wild edible mushrooms collected (04 species) belong to the Lyophyllaceae family and the Termitomyces genus: Termitomyces letestui, T. striatus, T. aurantiacus and T. brunneopileatus. The seed production process was a complete success. The substrate made up of corn stalks and wheat bran presented the best weights at harvest (221,66±3,36 g , 89,24±3,74 g and 93,58±7,13g). However, the carpophores obtained from the harvested and cultivated species were undifferentiated.

DOI: 10.61137/ijsret.vol.10.issue6.428

AI-Driven Vehicle Assistance Platform with Geolocation Services
Authors:-Rakesh Jaiswal, Kuldeep Yadav, Deepak Singh Purviya

Abstract-It often has brought inconveniences of unsafe situations and discomfort to its customers owing to vehicular breakdown. Typical roadside assistant applications that come out face problems such as high response times, small cover-up areas, and lack of real-time diagnostic capabilities among other problems. This research proposal intends to establish an innovative, web-based platform called Repair that has AI and LBS technologies integrated to provide real-time assistance for vehicles. The core feature of Repair is an AI-powered chatbot that can troubleshoot the most common vehicle issues independently. Advanced NLP techniques are applied to guide users through the diagnostic steps and provide solutions to problems such as flat tires, dead batteries, or other engine issues. When the problem exceeds the capabilities of the chatbot, the system uses Geolocation API technology to pinpoint the user’s exact location and dispatch the nearest available towing service. This seamless integration of AI and geospatial technology ensures faster response times, reducing user waiting periods and improving service efficiency.

DOI: 10.61137/ijsret.vol.10.issue6.429

Comprehensive Study of Mobile and Web Applications for on-Demand Services
Authors:-Aditi Pradeep, Akshara Vijay, Jerom Jo Manthara, K S Abhishek, Jithy John

Abstract-With the rapid growth of digital solutions, on- demand service applications have emerged as valuable tools for addressing daily needs, such as home maintenance and freelancing tasks. This survey paper provides a comprehensive review of ten existing mobile and web-based applications designed to connect customers with service providers across a range of sectors. By examining each system’s features, user experience, and limitations, this study highlights the commonalities and distinct approaches used to facilitate service matching. Key findings reveal that, while these applications effectively streamline access to services, they often face challenges such as limited service categories, regional restrictions, and issues with pricing transparency and real-time availability. Through a comparative analysis, this paper identifies trends, limitations, and potential improvements for future on-demand service platforms.

DOI: 10.61137/ijsret.vol.10.issue6.430

Assessing Model Misspecification in Stochastic Linear Regression Analysis
Authors:-Research Scholar Siddamsetty Upendra, Research Scholar R. Abbaiah

Abstract-This paper studies misspecification tests for stochastic linear regression models, including the Durbin-Watson test, Ramsey’s regression specification error test, Lagrange’s multiplier test, and UTTS’ rainbow test. Specification errors arise when there are deviations from the underlying assumptions of a stochastic linear regression model, impacting associated inferences. Specifically, errors may occur in specifying the error vector ( ) and the data matrix ( X ). Common causes of specification errors involve including irrelevant independent variables or excluding relevant ones in the stochastic linear regression model. Previous research by Ivan Krivy et al. (2000) presented two stochastic algorithms for estimating parameters in nonlinear regression models. In a 1984 paper, Russell Davidson et al. developed a computational procedure for a variety of model specification tests. Ludger Ruschendorf et al. (1993) constructed nonlinear regression representations of general stochastic processes, focusing on specific representations for Markov chains and certain m-dependent sequences. This study contributes to the understanding of misspecification in stochastic linear regression models, utilizing a range of tests to identify errors in model assumptions and parameter estimation. The insights gained from these tests can enhance the accuracy and reliability of regression model inferences.

DOI: 10.61137/ijsret.vol.10.issue6.432

The Role of Data Science in Business Intelligence: Use Cases and Implementation Challenges
Authors:-Priyanshu Tripathi

Abstract-Data Science has become a pivotal element in the evolution of modern Business Intelligence (BI), transforming the way organizations process and analyze vast amounts of data to uncover actionable insights. By leveraging advanced techniques such as machine learning, statistical modeling, and data visualization, businesses can enhance decision-making processes and gain a competitive edge. This report delves into the synergistic integration of Data Science within BI frameworks, illustrating its practical applications through diverse use cases including predictive analytics for forecasting trends, customer segmentation for personalized marketing strategies, and fraud detection to safeguard organizational integrity.While the potential benefits are immense, the implementation of Data Science in BI is not without its challenges. Key hurdles include ensuring data quality and consistency across sources, overcoming integration complexities with legacy systems, and addressing skill gaps in data literacy among employees. These challenges require strategic planning, investment in technology, and workforce training to be effectively mitigated.The report also explores emerging trends shaping the future of BI, such as the increasing adoption of artificial intelligence, real-time analytics, and the use of natural language processing for intuitive data interactions. Finally, it provides actionable recommendations for organizations to build robust and scalable BI strategies, emphasizing the importance of fostering a data-driven culture, prioritizing ethical data practices, and continuously evolving with technological advancements.

DOI: 10.61137/ijsret.vol.10.issue6.433

Software Evaluation Tools and Testing Methodologies
Authors:-Anil Kumar Behera, Associate Professor Dr S R Raja

Abstract-Testing is a task, which is performed to check the quality of the software and also this process is done for the improvement in software at the same time. Software testing is a critical component of the software development lifecycle, ensuring that applications meet specified requirements and function as intended. Over the years, a wide range of tools and methodologies have been developed to enhance the effectiveness, efficiency, and scalability of testing processes. This paper provides an overview of the most widely used tools and methodologies for software testing, focusing on both manual and automated approaches. It explores popular testing tools for different testing types such as unit testing, functional testing, performance testing, and security testing, with a detailed examination of frameworks like Selenium, JUnit, and TestNG. Additionally, the paper highlights key methodologies, including Agile testing, Behaviour-Driven Development (BDD), and Continuous Integration/Continuous Delivery (CI/CD) integration, emphasizing how these approaches align with modern development practices. The research also addresses the strengths and weaknesses of different tools and methodologies, offering insights into their suitability for various types of projects and testing environments. Challenges related to test maintenance, scalability, and the integration of testing within DevOps pipelines are also discussed. By analysing the current landscape of software testing tools and methodologies, this paper aims to provide valuable guidance for teams looking to improve their testing strategies, optimize workflows, and ensure higher- quality software releases.

DOI: 10.61137/ijsret.vol.10.issue6.434

Automated Malware and Phishing Website Detection Using Cluster Ensemble Techniques for Cybercrime Prevention
Authors:-Nega. B, Rithika. K, Rithika. V, D. Suganthi, J. Mythili, Dr. N. Prabhu

Abstract-Cybercrime is a specialised field that use internet communication networks to enhance the identification of cyber offenders via cyber laws. Extensive study is being undertaken to provide appropriate legal methodologies for preventing and regulating cybercriminal activity. Malware and phishing detection have become as prominent subjects in the last decade because to the harm they inflict on internet users. The identification of phishing websites is a novel area in the discipline. Phishing websites are regarded as a significant threat for the exploitation of personal information for the benefit of cybercriminals. This research presents an automated classification system designed to identify malware and phishing websites by integrating several clustering techniques using a cluster ensemble approach.

DOI: 10.61137/ijsret.vol.10.issue6.652

Development of Forensic Analysis Model for Investigating the Cybercrime Over TOR Network
Authors:-Atchaya. S, Bavana. D, Dharshini. V, Suganthi. D, Mythili. J, Greeshma K

Abstract-The proliferation of crimes using anonymised networks such as The Onion Router (TOR) has posed considerable hurdles for law enforcement and cybersecurity experts. Conventional forensic methods often have difficulties in tracking illegal activity carried out on TOR because of its encryption and anonymity attributes. This study aims to provide a forensic analysis model tailored for the investigation of criminality inside the TOR network. The model utilises sophisticated data analysis methods, using machine learning classifiers like as Naïve Bayes, Support Vector Machines (SVM), Random Forest, and K-Nearest Neighbours (KNN), to identify anomalous activity and discern attack patterns. Furthermore, it incorporates feature selection techniques to improve classification precision and minimise false positives. The proposed methodology utilises publicly accessible information and network traffic analysis to enhance the detection and investigation of criminality inside the TOR network, providing significant insights for security experts and law enforcement authorities.

DOI: 10.61137/ijsret.vol.10.issue6.653

Fraud Detection in Financial Institutions: AI VS. Traditional Methods
Authors:-Chintamani Bagwe

Abstract-This research paper provides a comparative analysis of traditional rule-based fraud detection methods and emerging AI-based approaches in financial institutions. The study examines effectiveness, adaptability, operational efficiency, regulatory compliance, and implementation considerations of both methodologies. Through detailed evaluation supported by visual representations, the paper demonstrates that while AI-based methods offer superior detection accuracy, adaptability, and reduced false positives, traditional approaches provide greater transparency and established regulatory compliance frameworks. The findings suggest that hybrid approaches combining the strengths of both methodologies represent the optimal strategy for most financial institutions. The paper concludes with an examination of future trends and recommendations for financial institutions seeking to enhance their fraud detection capabilities.

DOI: 10.61137/ijsret.vol.10.issue6.654

Professor Group Search Optimization and Leicht-Holme-Newman Trust based Wireless Sensor Network Optimization
Authors:-Poonam Tiwari, Professor Rani Kushwaha

Abstract-Wireless sensor networks (WSNs) are vulnerable to many backdoor attacks counting malicious nodes. Malicious nodes can inject false data, drop packets, or even launch denial-of-service attacks. One way to detect malicious nodes in WSNs is to use trust-based routing protocols. Trust-based routing protocols calculate a trust value for all network nodes. This paper has developed a model that estimates trust of each node based on social behavior function Leicht-Holme-Newman. Based on the trustful nodes paths of the packet were found by the Group Search Optimization algorithm. This paper has proposed a model that reduces the energy losses of WSN network. Experiment was done on different set of network environment under varying nodes attacks. Result shows that proposed model has increased the network spectrum utilization and network life as well.

Published by: vikaspatanker

Authors: Shravan Kumar Reddy Padur

Abstract: The evolution of enterprise software delivery has entered a transformative era where artificial intelligence (AI) and platform engineering unite to revolutionize the developer experience (DX). Traditional DevOps pipelines, though effective at accelerating releases, often introduced cognitive overload, toolchain sprawl, and inconsistent governance. The advent of internal developer platforms (IDPs) exemplified by Spotify’s Backstage, Humanitec, and CNCF’s platform engineering models has redefined developer productivity through unified, self-service abstractions that reduce operational friction while preserving control and compliance. Concurrently, AI’s influence has permeated every layer of the development lifecycle: AI-assisted coding enhances ideation and reduces context switching, AI-driven operations (AIOps) enable proactive detection and self-healing, and predictive analytics frameworks like DORA and SPACE translate delivery data into actionable performance insights. Together, these advances are ushering in an era of adaptive, intelligence-augmented platforms where automation, observability, and developer empathy converge—elevating enterprise software delivery from procedural execution to a continuously learning, self-optimizing ecosystem.

DOI: http://doi.org/10.5281/zenodo.17679655

Published by: vikaspatanker

Authors: Olamide Ayeni, Opeyemi Alamutu

Abstract: The rapid urbanization of the 21st century has created unprecedented challenges for waste management systems, necessitating innovative approaches that integrate resilience and sustainability. This article examines the design and implementation of resilient waste management systems through a circular economy lens, addressing the critical need for sustainable urban development. By analyzing contemporary research and best practices, this study explores how cities can transform linear waste management models into circular systems that promote resource recovery, environmental protection, and economic viability. The article synthesizes evidence from global case studies and technological innovations to provide a comprehensive framework for designing resilient waste management systems that can withstand environmental, economic, and social pressures while contributing to urban sustainability goals

DOI: http://doi.org/10.5281/zenodo.17213748

Published by: vikaspatanker

 

Vivek Agrawal
Affilation:	SwiftyMinds Houston, Texas, United States
Email-Id:	jadu.vivek@gmail.com
ACADEMIC QUALIFICATION Master of Science, University of Houston, United StatesBachelor of Engineering, Savitribai Phule Pune University, India

 

Published by: vikaspatanker

Authors: Olajide Adebayo, Tolulope Awobeku

Abstract: Business Email Compromise (BEC) attacks represent one of the most financially devastating cybersecurity threats facing modern enterprises, with losses exceeding $43 billion globally since 2016 according to FBI Internet Crime Complaint Center data. This study presents a comprehensive detection and mitigation strategy specifically designed for hybrid cloud environments utilizing Microsoft 365 and Google Workspace platforms. The research focuses on developing an integrated framework that combines advanced identity and access management protocols, robust encryption mechanisms, and automated compliance enforcement to effectively counter BEC threats. Through analysis of enterprise security architectures and implementation of policy-aware automation systems, this study demonstrates how organizations can significantly enhance their resilience against sophisticated social engineering attacks while maintaining operational efficiency in distributed work environments.

Published by: vikaspatanker

An Unmanned Level Crossing Controller with Real Time Monitoring Based on Microcontroller Elements
Authors:-Angel Dixon, Muhammed Ashiq k, Sreenika V Nair, Assistant Professor MS. Sayana M

Abstract-The Automatic Railway Gate Control (ARGC)system is designed to overcome the limitations and inefficiencies associated with traditional manually operated railway crossing gates. This innovative system employs sensors and microcontroller technologies to manage and control the operation of railway gates automatically, thereby enhancing the safety and efficiency of rail and road traffic.

IoT Enabled Solutions for Women Safety and Health Monitring
Authors:-Sudeshna P, Vivekanandan K

Abstract-Women and children today deal with a number of problems, including sexual attacks. The victims’ life will undoubtedly be greatly impacted by such atrocities. It also has an impact on their psychological equilibrium and general wellbeing. The frequency of these acts of violence keeps rising daily. Even schoolchildren are victims of sexual abuse and abduction. In our society, a nine-month-old girl child is not protected; she was abducted, sexually assaulted, and ultimately killed. Seeing the abuses of women makes us want to take action to ensure the protection of women and children. Therefore, we intend to present a device in this project that will serve as a tool for security and guarantee the safety of women and children. GSM microcontroller.

DOI: 10.61137/ijsret.vol.10.issue5.224

Impact of Subsidies on Indian Agriculture
Authors:-Manish Kumar, Assistant Professor Dr Gurshaminder Singh

Abstract-Agriculture plays a crucial role in India’s economy, supporting approximately 55% of rural households and contributing about 18% to the nation’s GDP. At the time of independence, the agricultural sector was underdeveloped, with limited land dedicated to key crops. In response, the Indian government implemented programs to modernize farming by introducing high-yielding seed varieties, fertilizers, mechanization, and irrigation. However, higher the costs of these modern techniques presented challenges for many farmers. To make agricultural inputs more affordable to the farmer, the government introduced subsidies based on recommendations from the Food Grain Price Committee. While subsidies are essential for addressing market inefficiencies and promoting societal benefits like poverty alleviation and food security, they are often criticized for issues like poor targeting and governance challenges. In spite of this fact, subsidies have significantly influenced agricultural production, particularly during the Green Revolution. As India moves toward sustainable agricultural development, subsidies remain a vital tool for balancing economic, environmental, and social objectives.

DOI: 10.61137/ijsret.vol.10.issue5.225

A Review on Direct Seeded Rice: A Sustainable Approach to Paddy Cultivation
Authors:-Jagdeep Singh, Assistant Professor Dr. Gurshaminder Singh

Abstract-Agriculture is crucial for the Indian economy. Rice is a staple crop to more than half of world population. Conventional transplanted rice production faces issues like lowering water tables, lower productivity, methane emissions, soil health deterioration, and labour scarcity. Puddling, a crucial step in wetland rice production, can improve transplanting and weed management but can also cause soil conditions that are unfavourable for post-rice crops. Puddled transplanted rice is energy-intensive and contributes to climate change by emitting methane and nitrous oxide. Direct seeded rice (DSR) technologies can minimize environmental impact and increase productivity. DSR was introduced in 2009-10 to address labour constraints, rising labour prices, and a diminishing groundwater table in Punjab. With agricultural water requirements expected to increase by 20% by 2050, DSR requires about 50% less water under Indian conditions. This review article studies the condition of current rice production practices, the major constrains and DSR, its advantages along with agronomy as substitute of current TPR method.

DOI: 10.61137/ijsret.vol.10.issue5.226

Food for thought: Image-Based Recipe Generation using Deep Learning
Authors:-Aftab Shakil Shaikh

Abstract-The recognition of food on social media has spawned an growing interest in automated food recognition and recipe era. We gift a system that combines both neighborhood and global functions to create spatiotemporal convnet, this paper outlines the venture of creating particular but special recipes from snap shots of food. on this paper, we use convolutional neural networks and a generative antagonistic community to robotically convert meals photographs into textual content based totally recipes. To generate coherent and contextually relevant recipe instructions, our approach combines image popularity techniques based totally on Convolutional Neural Networks (CNN) [17] for the identity and category of food with herbal Language Processing (NLP)—fashions utilized in conjunction to analyze textual data. extra records: The authors present a large-scale dataset with various meals categories and corresponding recipe (i.e., cooking method) for schooling their proposed framework. at the photograph- to-recipe mission, our experiments set up that it is able to certainly generate a recipe carefully matching with food objects in snap shots. Quantitative assessment benchmarks on preferred datasets display superiority as compared to baseline models and qualitative evaluation verifies that our architecture can produce human-like recipe commands. these consequences assist our approach as a benchmark for more state-of-the-art packages closer to automated culinary content creation by way of offering users with more food-related experience in digital interfaces.

DOI: 10.61137/ijsret.vol.10.issue5.227

Accredited Philhealth Konsult Providers Service Quality and Diagnostic Examination Availability in Baguio City
Authors:-Aileen D. Ambros, Cristine Rose A. Angiwot, Kenje L. Coytop, Melvin A. Danao, Phemy Amor C. Galingan, Diana Febone L. Macalo, Merriam S. Pay-an, Marilou Dela Peña, Jolly B. Mariacos

Abstract-The PhilHealth Konsulta program aims to improve healthcare access and affordability in the Philippines by offering full primary care services such as consultations, diagnostic testing, and prescriptions. This study looks at the quality and availability of diagnostic examinations provided by accredited PhilHealth Konsulta providers in Baguio City. A quantitative research approach was used, with a survey disseminated to staff and outpatients from various PhilHealth Konsulta facilities in Baguio City. The study used a Likert scale to assess satisfaction levels in 10 areas of service quality and diagnostic availability, ranging from 1 (least satisfied) to 5 (very highly satisfied). A total of 117 people responded, including 48 personnel and 69 outpatients. Findings indicate generally high satisfaction levels among both healthcare providers and patients regarding consultation services, queue management, and patient instructions within the PhilHealth Konsulta framework. However, moderate satisfaction was noted regarding the availability of medications and diagnostic tests, highlighting potential areas for enhancement in inventory management and diagnostic infrastructure. Disparities between staff and patient perceptions suggest a need for improved communication and alignment in service delivery expectations. While the PhilHealth Konsulta program in Baguio City typically satisfies the demands of patients with moderate to high satisfaction, there are several crucial areas that need to be addressed to increase service quality and diagnostic availability. The study emphasizes the importance of increasing diagnostic test availability through enhanced equipment procurement and supply chain management. Strengthening healthcare manpower by recruiting additional staff and implementing training programs to optimize service delivery efficiency is also advised. Furthermore, leveraging technology to streamline administrative processes and improve patient management systems can enhance overall patient experience and operational effectiveness. This research contributes valuable insights to policymakers, healthcare managers, and practitioners involved in optimizing primary healthcare delivery under the PhilHealth Konsulta program. By addressing identified gaps and leveraging strengths, this study aims to support efforts towards achieving equitable healthcare access and improving health outcomes for residents of Baguio City and similar settings across the Philippines.

DOI: 10.61137/ijsret.vol.10.issue5.228

Mitigating Cyber Threats in Digital Payments: Key Measures and Implementation Strategies
Authors:-Praveen Tripathi

Abstract-This paper examines the increasing importance of robust cybersecurity measures in the digital payments industry. As the volume and value of online financial transactions continue to grow exponentially, the sector faces a corresponding surge in cyber-attacks, necessitating advanced cybersecurity protocols. This study explores key cybersecurity measures and implementation strategies, including encryption, multi-factor authentication (MFA), tokenization, artificial intelligence (AI)-based fraud detection, and regulatory compliance, to safeguard digital payments against various cyber threats. Through a detailed review of existing literature, case studies, and statistical analysis, the article provides strategic insights into how organizations can enhance security in digital payment ecosystems, maintain compliance, and achieve resilience in the face of evolving cyber threats.

DOI: 10.61137/ijsret.vol.10.issue5.229

Scalar and Vector Controlled Inverter Topology FED Three Phase Induction Motor
Authors:-Megavath Shankar

Abstract-This paper presents a comprehensive study of scalar and vector control techniques for three-phase induction motors fed by inverter topologies. Scalar control, commonly known as Voltage/Frequency (V/f) control, offers a simple, cost-effective method for motor control but is limited in its precision, torque regulation, and dynamic response. In contrast, vector control (or field-oriented control) decouples the motor’s torque and flux components, providing enhanced performance, including faster response times, improved speed and torque accuracy, and reduced harmonic distortion. MATLAB/Simulink simulations are used to evaluate both methods under various load and speed conditions, demonstrating the superior dynamic performance, accuracy, and reduced harmonic content of vector control, making it ideal for high-performance industrial applications.

DOI: 10.61137/ijsret.vol.10.issue5.230

Exploring Bioinformatics for Early Detection and Management of Lifestyle Disorders
Authors:-Dr. V. K. Singh

Abstract-Lifestyle diseases, such as cardiovascular diseases, diabetes, obesity, and hypertension, are significantly influenced by environmental factors and individual habits, including diet, physical activity, and stress. Advances in bioinformatics have allowed researchers to leverage genomics, proteomics, metabolomics, and transcriptomics data for early detection and effective management of these diseases. By analyzing gene expression, protein interactions, and metabolic pathways, bioinformatics helps identify biomarkers and therapeutic targets. This paper explores how bioinformatics-driven approaches can aid in understanding the molecular mechanisms behind lifestyle diseases, facilitating early diagnosis, personalized treatments, and improved health outcomes.

DOI: 10.61137/ijsret.vol.10.issue5.231

Effective System Design for Scalable Mobile Applications: A Practical Guide
Authors:-Vivek Agrawal

Abstract-Designing scalable mobile applications requires more than just robust code; it involves architectural foresight, optimized data models, and efficient network communication strategies. In this article, we present a comprehensive guide to effective system design for scalable mobile apps. Using real-world examples, we explore advanced data modeling techniques, API architecture (REST vs. GraphQL), and real-time data handling using Server-Sent Events (SSE) and WebSockets. Additionally, we examine design patterns such as Model-View-Presenter (MVP) and the use of Dependency Injection for managing complex dependencies. This paper explores a technical roadmap for developers looking to build scalable, maintainable mobile applications capable of handling growing user bases and evolving requirements.

Heart Disease and COVID-19 Prediction Using AI/ML
Authors:-Ms.Sristi Sharma, Mr.Sumeet Singh, Dr. Jasbir Kaur, Assistant Professor Ms.Sandhya Thakkar, Assistant Professor Mr.Suraj Kanal

Abstract-The current pandemic of COVID-19 for global medical care has high demand on rapid and correct diagnosis, especially in a cardiac population with prior heart disease being a large proportion among these patients. In this work, a predictive machine learning (ML) model based on convolutional neural networks (CNNs) is proposed to recognize COVID-19 and heart disease from chest X-ray images. The COVID-19 positive and normal X-ray images were used to train the CNN model. The objective behind was to automate the diagnosis so that it helps in early detection of diseases which can save lives and improve patient management. The model was accurate and demonstrated promising results in clinical scenarios.

DOI: 10.61137/ijsret.vol.10.issue5.232

Exploring the Adoption of Digital Payments: Key Drivers & Challenges
Authors:-Praveen Tripathi

Abstract-This paper investigates the factors influencing the adoption of digital payments globally. It discusses the drivers, challenges, and potential future research areas required to enhance the digital payment ecosystem. Emphasis is placed on technology advancements, consumer preferences, and regulatory frameworks, with a data-driven approach. Tables, graphs, and statistical analyses provide insights into the current adoption trends across regions. Future research directions focus on improving the security, user experience, and accessibility of digital payments.

DOI: 10.61137/ijsret.vol.10.issue5.233

Role of AI in Developing Countries
Authors:-Azhan Aslam

Abstract-This paper explores the role and impact of Generative Artificial Intelligence (AI) in developing countries, emphasizing its potential to address significant socio-economic challenges. Unlike traditional AI, which primarily focuses on decision-making based on existing data, Generative AI can create new content, making it a powerful tool for innovation. This technology offers unique opportunities for sectors such as healthcare, education, agriculture, and infrastructure development, particularly in nations with limited resources and technological infrastructure. Generative AI can revolutionize healthcare by enhancing diagnostic tools, supporting drug discovery, and enabling remote medical services. In agriculture, it assists in optimizing crop yields and improving food security through advanced monitoring techniques. Additionally, the technology can personalize educational experiences and democratize access to learning materials. Despite these advantages, the adoption of Generative AI faces challenges, including ethical concerns, data privacy issues, and the risk of job displacement. The paper concludes that Generative AI holds immense potential to drive sustainable development in developing countries. However, careful implementation and strategic investments in infrastructure and education are required to overcome existing barriers and ensure equitable access to these technologies.

DOI: 10.61137/ijsret.vol.10.issue5.234

A Performances Evaluation and Modelling of Solar and Wind Hybrid Power Generation Source
Authors:-Dharmendra Malviya, Neha Singh

Abstract-The recent upsurge in the demand of PV and wind systems is due to the fact that they produce electric power without hampering the environment by directly converting the solar radiation into electric power. However the solar radiation, wind never remains constant. It keeps on varying throughout the day. The need of the hour is to deliver a constant voltage to the grid irrespective of the variation in temperatures, wind pressure and solar isolation. We have designed a circuit such that it delivers constant and stepped up dc voltage to the load. We have studied the open loop characteristics of the PV array and wind system with variation in temperature and irradiation levels. Then we coupled the PV array and wind system with the boost converter in such a way that with variation in load, the varying input current and voltage to the converter follows the open circuit characteristic of the PV array and wind system closely. At various isolation levels, the load is varied and the corresponding variation in the input voltage and current to the boost converter is noted. It is noted that the changing input voltage and current follows the open circuit characteristics of the PV array and wind system closely.

Microgrid Modelling and its Performance Identification Using Matlab Simulink
Authors:-Bharat Lal Yadav, Neha Singh

Abstract-In this work, a Microgrid (MG) test model based on the 14-busbar IEEE distribution system is proposed. This model can constitute an important research tool for the analysis of electrical grids in its transition to Smart Grids (SG). The benchmark is used as a base case for power flow analysis and quality variables related with SG and holds distributed resources. The proposed MG consists of DC and AC buses with different types of loads and distributed generation at two voltage levels. A complete model of this MG has been simulated using the MATLAB/Simulink environmental simulation platform. The proposed electrical system will provide a base case for other studies such as: reactive power compensation, stability and inertia analysis, reliability, demand response studies, hierarchical control, fault tolerant control, optimization and energy storage strategies.

Emerging Network Security Threats
Authors:-Shashant Srivastava, Dr. Usha J

Abstract-Over the past few decades, the rapid expansion of the Internet in India has brought significant challenges in ensuring network security. Network security encompasses the strategies and policies implemented by users to protect and oversee the network infrastructure from unauthorized access. This concept is crucial for both private and public networks in India, safeguarding communications and transactions. In recent years, India’s networks have experienced substantial attacks from unauthorized entities. This paper examines the current network infrastructure and security policies in India, identifies the prevalent types of attacks, and proposes advanced technologies to enhance the robustness of India’s network security framework.

Exploring the Diagnostic Capabilities of Machine Learning in Glaucoma Detection
Authors:-Research Scholar Ramesh Chouhan, Assistant Professor Vikas Kalme

Abstract-This is a review of various image processing methods used in diagnosing glaucoma, an irreversible eye disorder of optic nerve results nerve cell damage. Glaucoma causes slow vision loss and is largely prevalent in rural and semi-urban populations, but people suffering from the disease can be found just about anywhere. The current method to diagnose retinal diseases mainly relies on the analysis of fundus images obtained from a retina through advanced image processing techniques. Image registration, fusion, segmentation, feature extraction, enhancement, morphological operations, medical image understanding are few of the standard methods used for detecting Glaucoma and different eye diseases along with GLCM based analysis and its pattern matching classification statistical techniques used. These methods play a critical role in increasing accurateness with early diagnosis and treatments results required for eye practices.

Smart Automation Systems for Home Appliances Using Arduino Techniques
Authors:-Mohin Dhiman

Abstract-In the order of the World massive quantities of power are inspired in residential buildings leading to a unenthusiastic impact on the surroundings. Also, the number of wireless connected strategy in use around the World is constantly and rapidly increasing, leading to potential health risks due to over exposer to electromagnetic emission. An opportunity appears to decrease the energy consumption in residential buildings by introducing smart home automation systems. Multiple such solutions are available in the market with most of them being wireless, so the challenge is to design such systems that would limit the quantity of newly generated electromagnetic radiation. For this we look at a number of wired, serial communication methods and we successfully test such a method using a simple protocol to switch over data between an Arduino microcontroller board and a Visual C#.Net app running on a Windows computer. We aspire to show that if desired, smart home automation systems can still be built using simple viable alternatives to wireless communication.

Tokenization Strategy Implementation with PCI Compliance for Digital Payment in the Banking
Authors:-Praveen Tripathi

Abstract-The banking sector is under increasing pressure to ensure secure and seamless digital payment processes. Tokenization, a method of securing sensitive payment data, has emerged as an effective strategy for mitigating security risks and ensuring compliance with Payment Card Industry Data Security Standards (PCI DSS). This paper explores the implementation of tokenization strategies within the banking sector, emphasizing its role in achieving PCI compliance. Through case studies, statistics, and the presentation of real-world examples, the paper highlights both the challenges and benefits of adopting tokenization strategies.

Tokenization Strategy Implementation with PCI Compliance for Digital Payment in the Banking
Authors:-Praveen Tripathi

Abstract-The banking sector is under increasing pressure to ensure secure and seamless digital payment processes. Tokenization, a method of securing sensitive payment data, has emerged as an effective strategy for mitigating security risks and ensuring compliance with Payment Card Industry Data Security Standards (PCI DSS). This paper explores the implementation of tokenization strategies within the banking sector, emphasizing its role in achieving PCI compliance. Through case studies, statistics, and the presentation of real-world examples, the paper highlights both the challenges and benefits of adopting tokenization strategies.

DOI: 10.61137/ijsret.vol.10.issue5.235

Review on Enhancement of Power System Demand Side Management and Forecasting of Grid Performance Using Machine Learning Approach
Authors:-Deepkant Ujjaini, Assistant Professor Raghunandan Singh Baghel

Abstract-Renewable energies are being introduced in countries around the world to move away from the environmental impacts from fossil fuels. In the residential sector, smart buildings that utilize smart appliances, integrate information and communication technology and utilize a renewable energy source for in-house power generation are becoming popular. Accordingly, there is a need to understand what factors influence the accuracy of managing such smart buildings. Thus, this study reviews the application of machine learning prediction algorithms in Home Energy Management Systems. Various aspects are covered, such as load forecasting, household consumption prediction, rooftop solar energy generation, and price prediction. Also, a proposed Home Energy Management System framework is included based on the most accurate machine learning prediction algorithms of previous studies. This review supports research into the selection of an appropriate model for predicting energy consumption of smart buildings.

Review on PV-Wind-Battery-Based Grid-Connected Bidirectional DC-DC Coupled Multi- Distribution Transformer
Authors:-Ravi Kumar Malviya, Assistant Professor Raghunandan Singh Baghel

Abstract-The objective of this synopsis is to provide a control scheme of a power flow management of a grid connected hybrid PV-wind-battery. The hybrid PV wind-battery system is connected to a multi-input transformer coupled bidirectional dc-dc converter and using a fuzzy controller. The power from the PV along with battery charging/discharging is controlled by a bidirectional buck-boost converter. The power from wind is controlled by a transformer coupled boost half-bridge converter. A single-phase full bridge bidirectional converter is used for feeding ac loads and interaction with grid. The proposed converter design has lessened number of power transformation stages with less segmentally, and diminished misfortunes contrasted with existing grid connected hybrid frameworks. In this proposed work analyzing the multi response of a grid connected hybrid PV-wind-battery in different cases. In the proposed system has two renewable power sources, load, grid and battery.

Review on Damped-Sogi Based Control Algorithm for Solar PV Power Generating System
Authors:-Vijay Jhaniya, Assistant Professor Raghunandan Singh Baghel

Abstract-This Review deals with two stage solar PV power generating system with improved power quality in three-phase distribution system. This system not only feeds the power to the grid but it also provides the load compensation, power factor correction and harmonics elimination. For this, a double stage system is used where first stage is a DC-DC boost converter, which performs the MPPT (Maximum Power Point Tracking). For extracting maximum power from the PV string, an incremental conductance based MPPT algorithm is used. Moreover, in second stage a voltage source converter (VSC) is utilized. For control of VSC, a damped-SOGI (Second Order Generalized Integrator) algorithm is proposed. By using damped-SOGI based control algorithm, fundamental active and reactive power components of load currents are extracted for estimating the reference grid currents. After comparing these reference grid currents with sensed grid currents, these produce the switching pulses for the grid tied VSC. A prototype of the proposed system is developed in the laboratory. Test results are shown to validate the design and control algorithm under steady state and dynamic conditions at linear and nonlinear loads.

Intelligent ERP System: A Survey an Intelligent and modern approach to ERP Software
Authors:-Piyush Khandelia

Abstract-today’s business need is more complicated than before. That is why the existing ERP needs to be updated and need to be empowered by Artificial Intelligence techniques. In this regard, this manuscript has provided an overview of the Intelligent ERP System.

DOI: 10.61137/ijsret.vol.10.issue5.236

Human Intelligence in the Age of AI: Why Machines Won’t Take Over Jobs
Authors:-Dr. V. K. Singh

Abstract-Artificial Intelligence (AI) is rapidly advancing, transforming industries and reshaping the global job market. While there are concerns that AI will replace human workers, this paper argues that AI will complement rather than substitute the human workforce. The paper explores the irreplaceable human qualities such as emotional intelligence, creativity, and ethical decision-making, and emphasizes the importance of AI-human collaboration. The research draws on a wide range of literature and studies to analyze how AI is enhancing, not replacing, job roles, and contributing to the creation of new job opportunities. The conclusion emphasizes that AI and humans will co-evolve, leading to a more dynamic, adaptive, and skilled workforce in the future.

DOI: 10.61137/ijsret.vol.10.issue5.237

Fostering Inclusive Ecologies of Knowledge: A Pathway to Equitable and Sustainable Futures in Education
Authors:-Clement Yeboah, Andrews Acquah

Abstract-This meta-analysis investigates the impact of inclusive ecologies of knowledge on promoting equitable and sustainable futures in education, synthesizing findings from peer-reviewed journal articles published between 2010 and 2024. The primary objective was to evaluate the effectiveness of inclusive educational practices in fostering equity and sustainability. Studies were selected based on explicit inclusion criteria, focusing on empirical research that addressed inclusivity and sustainability within educational contexts. A comprehensive search of databases including PubMed, ERIC, Web of Science, and Scopus was conducted, and the risk of bias in the included studies was assessed using the Cochrane Collaboration’s tool. The synthesis of results, encompassing 35 studies with a total of 4,500 participants, revealed a moderate positive effect of inclusive practices on educational outcomes (Cohen’s d = 0.45, 95% CI: 0.30–0.60). Limitations include variability in study designs and potential bias in some studies. The findings underscore the importance of integrating diverse knowledge systems into education to achieve equitable and sustainable futures. The review was neither registered nor funded.

Review of Optimization Algorithms
Authors:-Er. Vivek Sya, Assistant Professor Er.Raman kumar sofat

Abstract-Over the years, several optimization techniques has been developed for the real life applications. The traditional methods do not solve the nonlinear objectives. This paper presents the review of four popular optimization techniques: genetic algorithm (GA), differential evolution (DE. They can be applied to the linear, non-linear, differential and non-differential problems. The related description for each procedure of optimization is presented.

Active and Reactive Power Dispatch using Differential Evolution
Authors:-Er. Vivek Sya, Assistant Professor Er.Raman kumar sofat

Abstract-The paper presents an approach for the optimal dispatch of active and reactive power with an aim to generate the optimal generation schedule satisfying the equality and inequality constraints and minimizing the cost of operation of generating units by using Genetic Algorithm and Differential Evolution. The approaches have been applied to IEEE 30 Bus system and the obtained results are compared.

Understanding and Mitigating Ransomware Threats: A Comprehensive Analysis
Authors:-Rohit Yadav, Vinit Warang, Dr. Jasbir Kaur, Assistant Professor Ms. Sandhya Thakker

Abstract-Ransomware has emerged as one of the most significant and widespread cyber threats in recent years. This form of malicious software locks or encrypts victims’ data, demanding a ransom in exchange for restoring access. The growing sophistication of ransomware attacks has made them increasingly difficult to detect and mitigate, causing severe economic and operational damage across industries. This paper presents a comprehensive analysis of ransomware, its evolution, types, attack mechanisms, and the defensive measures necessary to combat its spread. We also explore the economic implications of ransomware and present future trends in the fight against these cyberattacks. Finally, we propose best practices for organizations to reduce their vulnerability to ransomware attacks and present case studies on successful and failed mitigations.

DOI: 10.61137/ijsret.vol.10.issue5.238

Understanding and Mitigating Ransomware Threats: A Comprehensive Analysis
Authors:-Rohit Yadav, Vinit Warang, Dr. Jasbir Kaur, Assistant Professor Ms. Sandhya Thakker

Abstract-Ransomware has emerged as one of the most significant and widespread cyber threats in recent years. This form of malicious software locks or encrypts victims’ data, demanding a ransom in exchange for restoring access. The growing sophistication of ransomware attacks has made them increasingly difficult to detect and mitigate, causing severe economic and operational damage across industries. This paper presents a comprehensive analysis of ransomware, its evolution, types, attack mechanisms, and the defensive measures necessary to combat its spread. We also explore the economic implications of ransomware and present future trends in the fight against these cyberattacks. Finally, we propose best practices for organizations to reduce their vulnerability to ransomware attacks and present case studies on successful and failed mitigations.

DOI: 10.61137/ijsret.vol.10.issue5.238

Football Game Analysis and Tracking Position
Authors:-Assistant Professor Dr. Divya T.L, Tenzin Yignyen

Abstract-Tracking players and the ball in football games is crucial for accurately evaluating team strategies and individual performance. To derive meaningful metrics such as players’ positions, ball possession, and tactical movements throughout a match, continuous tracking of both players and the ball is required. Traditionally, these analyses are conducted manually by professional analysts. However, automated systems using advanced image processing and machine learning techniques have begun to enhance the efficiency and accuracy of such analyses. In this paper, we explore a method utilizing YOLOv8 for object detection and tracking, combined with K-means clustering and homography-based transformations, to provide a comprehensive real-time analysis of football games. We discuss the integration of these technologies into a user-friendly application for coaches and analysts to enhance tactical planning and performance evaluation in sports.

Tuberculosis Detection: A Deep Learning Approach
Authors:-Krishna Pratap Singh R, Dr. Gowthami

Abstract-A serious and pervasive lung disease with a poor diagnosis rate is tuberculosis. Following the vacuity of high-resolution coffin x-rays, deep literacy can now yield results for the successful discovery of this unpleasant complaint and other possible operations in the health sector. This study presents a new deep learning algorithm for tuberculosis identification using a coffin x-ray image bracket to acquire geographical data. It combines the ImageNet dataset with two popular, trained vgg16 and vgg19 models. The system that is being described is validated through trials using the chest x-ray dataset. After assessing the model on the test set, we receive a score of 0.9992 for each of the criteria (delicacy, perfection, recall, and f1-score).

DOI: 10.61137/ijsret.vol.10.issue5.239

Fraud Detection in Financial Transactions Using Machine Learning
Authors:-Professor Syeeda, Abhisek Mohanty

Abstract-Banking system vulnerabilities have made us vulnerable to fraudulent activities that seriously harm the bank’s reputation and financial standing in addition to harming clients. An estimated large sum of money is lost financially each year as a result of financial fraud in banks. Early discovery aids in the mitigation of the fraud by allowing for the development of a countermeasure and the recovery of such losses. This research proposes a machine learning-based method to effectively aid in fraud detection. In order to combat counterfeits and minimise damage, the artificial intelligence (AI) based model will expedite the check verification process. In order to determine the association between specific parameters and fraudulence, we examined a number of clever algorithms that were trained on a public dataset in this article.

DOI: 10.61137/ijsret.vol.10.issue5.240

Advancing Sustainability and Performance: A Review on Recycled Aggregates and Portland Slag Cement in Construction
Authors:-Lamiaa Ismail, M. Abdelrazik, Assistant Professor El Sayed Ateya, Assistant Professor Ahmed Said

Abstract-The construction industry faces increasing pressure to adopt sustainable practices due to resource depletion and waste management challenges. This review critically examines the use of Portland Slag Cement (PSC) in combination with Recycled Aggregate Concrete (RAC) to enhance sustainability and performance in construction. The analysis consolidates research on the mechanical properties, durability, and environmental impact of PSC-RAC composites. Findings show that PSC enhances compressive strength, tensile strength, and long-term durability while reducing the carbon footprint of concrete production. The review highlights the superior performance of PSC in comparison to traditional cementitious materials, particularly in harsh environments. However, challenges remain regarding the variability in the quality of recycled aggregates, workability issues, and economic feasibility. This review emphasizes the need for standardized quality controls for recycled materials and advocates for further research into long-term performance and the integration of PSC with advanced materials such as Nano-Silica. Comprehensive studies and cost-benefit analyses are recommended to fully explore the feasibility of PSC-RAC in both structural and non-structural applications.

DOI: 10.61137/ijsret.vol.10.issue5.241

Modified Dadda Multipliers and Compressors Designed Using Approximate Multiplier Algorithm
Authors:-Mtech Scholar Sidhharth Yadav, HOD & Professor Dr Bharti Chourasia

Abstract-The multiplier is a crucial component in digital signal processing. Many scientists have attempted—and continue to attempt—to construct multipliers that satisfy the two flowing pan criteria of fast speed, low power consumption, consistent design, and fewer zones. This is made possible by technological advancements. They are appropriate for a range of applications needing high speed, low power, and less VSI consumption because they can even combine these two objectives into a single multiplier. In This paper present modified dadda multipliers using approximate multiplier with high speed and energy economy is discussed. This way, speed and energy efficiency are increased at the cost of a slight inaccuracy, as the computationally intensive part of the multiplication is bypassed. Whereas Isim Simulator is used for simulation, Xilinx 14.7 is used for implementation. Data from test bench validation indicates that it provides a higher accuracy than the others. Based on the simulation results, the suggested multiplier design outperforms earlier designs in terms of space, latency, speed, and power. Unlike prior proposals which could only construct 16 or 32 bit multipliers, the proposed multipliers can be constructed with 64 bits.

Recoil Logger: A Logging Utility for Monitoring Recoil State Changes in React Applications
Authors:-Sait Yalcin

Abstract-This paper introduces the RecoilLogger component, a lightweight utility designed to track and log state changes within Recoil-based React applications. The component provides developers with the ability to monitor both current and previous state values, aiding in debugging and state management performance analysis. The paper outlines the implementation, use cases, and potential applications of the RecoilLogger, discussing its methodology in comparison to existing logging utilities in React. Results demonstrate its effectiveness in state tracking without causing performance overhead or altering the UI.

DOI: 10.61137/ijsret.vol.10.issue5.242

Vertical Farming: An Agricultural Revolution
Authors:-Arjit Vashishta, Assistant Professor Dr Gurshaminder Singh

Abstract-Vertical farming is becoming a valuable complement to traditional agriculture, enhancing sustainable food production as climate pressures increase. Initially, vertical farming focused on technological advancements like design innovation, automated hydroponic systems, and advanced LED lighting. However, recent studies emphasize improving resilience and sustainability, particularly through water quality and microbial life in hydroponic environments. Plant growth-promoting rhizobacteria (PGPR) have proven effective in boosting plant growth and resilience to both biotic and abiotic stress. Using PGPR in plant-growing media enhances microbial diversity, helping reduce reliance on chemical fertilizers and pesticides. This overview explores the history of vertical farming, its economic, environmental, social, and political opportunities and challenges, and the role of the rhizosphere microbiome in advancing hydroponic systems.

DOI: 10.61137/ijsret.vol.10.issue5.243

Design of Cross Level Automatic Railway Gate Control System Using Arduino UNO 328
Authors:-Ayodele J, Barakur C.A, Joel O.O

Abstract-This paper presents the design and construction of an obstacle detection system for railway level crossings. The focus of this research is on reducing accident rates attributed to obstructions between the gates of the level crossing. Research indicates that approximately 30% of railway accidents at level crossings are resulting from obstacles blocking the tracks. To address this issue, we developed a system utilizing an Arduino Uno microcontroller, along with ultrasonic and reed switch sensors, and a GSM module for real-time alerts. While the ultrasonic sensors are deployed to monitor the gate crossing arena, the reed switches are positioned 3km away from each gate to detect the arrival/departure of the train. Such that when there is any obstacle detected the GSM triggers sms alert to the train operators for a possible halt to create room for evacuation of the obstacle. By facilitating timely responses, this system aims to decrease the likelihood of accidents, thereby enhancing safety for both rail and road users. This innovative solution highlights the potential for improved safety measures within railway infrastructure.

DOI: 10.61137/ijsret.vol.10.issue5.254

Kidney Stone Detection Using Machine Learning With CT_Images
Authors:-Ms.Priya Bhagat, Mr.Taabish Shaikh, Dr. Jasbir Kaur, Assistant Professor Ms.Ifrah Kampoo, Assistant Professor Mr.Suraj Kanal

Abstract-Effective management and treatment of these stones depend on early and precise detection. Ultrasound and X-ray are two conventional methods for kidney stone detection, but their resolution and accuracy are limited. Because of its increased resolution and capacity to produce precise anatomical information, computed tomography (CT) imaging has grown in reliability. However, it takes a lot of experience and time to interpret CT scans for kidney stone detection. Recent developments in Convolutional Neural Networks (CNNs) provide a promising solution to these problems. CNNs, a class of deep learning algorithms, have demonstrated remarkable performance in image analysis tasks by automatically learning hierarchical features from large datasets.

DOI: 10.61137/ijsret.vol.10.issue5.244

Value Chain of the Water Sector in India
Authors:-Balaji A

Abstract-India’s water sector is crucial for economic growth, public health, and environmental sustainability. With a population exceeding 1.4 billion, the water demand has risen sharply due to urbanisation, agriculture, and industrialization. However, the sector faces significant challenges, including water scarcity, pollution, and inadequate infrastructure. With 18% of the world’s population but only 4% of the world’s water sources, India grapples with water scarcity in many regions. India is the world’s largest user of groundwater that extracts more than any other country in the world and accounts for nearly 25 percent of the world’s extracted groundwater. With an estimated $250 billion investment requirement over the next 20 years, the Indian water sector offers immense opportunities for both domestic and international investors. This report highlights the structure of the water value chain in India, identifies investment opportunities, and names the key players and beneficiaries in the ecosystem.

DOI: 10.61137/ijsret.vol.10.issue5.245

Bioethanol Production from Potato Peel Waste
Authors:-Renuka Yadav, Shubham Shubhashish, Dr. Gurshaminder Singh

Abstract-Bioethanol is generated by fermenting sugars obtained from biomass such as crops, agricultural waste, and organic refuse, and is a sustainable and eco-friendly energy option. It provides a long-term solution to fossil fuels, which has the capacity to decrease greenhouse gas emissions and combat climate change. Potato peel waste (PPW) is one of the many feedstocks that shows potential for bioethanol production because of its high starch content. PPW is a waste product from the potato processing sector, commonly thrown away or utilized for less valuable purposes. This study investigates the possibility of using PPW as a productive raw material for bioethanol manufacturing, specifically examining its preparation, breakdown, conversion, and purification stages. Even though bioethanol from PPW shows potential, economic and technical limitations arise due to high moisture levels, composition variability, and the requirement for substantial pre-treatment processes. However, the use of PPW for bioethanol production is in line with worldwide initiatives for sustainable energy, waste reduction, and the circular economy.

DOI: 10.61137/ijsret.vol.10.issue5.246

Sustainable Potato Production through MAS and Late Blight Resistance
Authors:-Kartikay Sharma, Sahil Kumar, Dr. Gurshaminder Singh

Abstract-Late blight, caused by Phytophthora infestans, continues to pose a significant threat to potato production globally. While traditional breeding methods have been used to create resistant cultivars, these methods can be slow and often face limitations due to the availability of genetic resources. Marker-assisted selection (MAS) provides a more efficient and accurate approach by using molecular markers to identify plants that possess resistance genes. This review offers a thorough overview of MAS for late blight resistance in potatoes, discussing its historical development, genetic foundations, molecular markers, and the steps involved in its application. Key topics include the identification of resistance genes and their corresponding markers, the establishment of PCR conditions for marker amplification, and the combination of MAS with traditional breeding techniques. The review also addresses the challenges and future directions of MAS, emphasizing the importance of ongoing marker development, maintaining genetic diversity, and adapting to changing pathogens. In summary, MAS is a valuable tool for improving late blight resistance in potatoes. By integrating MAS with traditional breeding methods and tackling its challenges, breeders can create cultivars that are more resilient to this destructive disease, thereby supporting sustainable potato production.

DOI: 10.61137/ijsret.vol.10.issue5.247

Detection and Classification of Cotton Plant Disease Using Deep Learning Network
Authors:-Associate Professor G.Vasanthi, Professor Dr.S.Artheeswari, Assistant Professor M.Nithya

Abstract-This research aims to address critical challenges in agricultural sustainability by proposing a multifaceted approach to the detection and prediction of diseases affecting cotton plants. The objectives of this study are threefold. Firstly, the research focuses on the classification of cotton plant leaves, essential for accurate disease diagnosis. Through dataset analysis, normalization techniques, and feature extraction using Local Binary Patterns (LBP), cotton plant leaves are effectively differentiated from other foliage. Classification is accomplished utilizing Lightweight Convolutional Neural Networks (CNN), with performance parameters rigorously evaluated to ensure efficacy. Secondly, the study extends its scope to the classification of diseases affecting tomato plant leaves, offering insights into disease identification methodologies applicable to cotton plants. Leveraging the Coral Reef Optimization approach for feature extraction and a hybrid classifier comprising ResNet50 and VGG16 architectures, the system achieves precise disease classification. Lastly, the research addresses the critical need for predictive analytics in disease management by forecasting the occurrence of diseases in cotton plants. Utilizing historical time series weather data, machine learning and deep learning models, specifically Quantile Regression Forests coupled with Long Short-Term Memory (LSTM) algorithms, predict temperature and relative humidity parameters crucial for disease occurrence. By integrating these objectives, this study endeavors to provide a comprehensive framework for proactive disease management in cotton cultivation, thereby contributing to sustainable agricultural practices and food security.

DOI: 10.61137/ijsret.vol.10.issue5.248

CRISPR-Cas Technologies for Nutrition Enhancement: Current Progress and Future Directions
Authors:- Abhishek

Abstract-CRISPR-Cas technology has revolutionized the field of crop biotechnology, offering precise and efficient tools for enhancing the nutritional value of plants. This review highlights the current applications of CRISPR-Cas in biofortifying staple crops to combat global malnutrition. By editing specific genes, researchers have been able to increase essential nutrients such as vitamins, minerals, and proteins. However, challenges remain, including off-target effects, regulatory and biosafety concerns, and ethical considerations. Future directions point toward innovations in precision editing, multiplex gene editing for complex traits, and integration with synthetic biology and traditional breeding. Additionally, harmonizing global regulatory frameworks and ensuring equitable access to CRISPR technologies will be essential for realizing its potential to improve food security. This review underscores the transformative potential of CRISPR-Cas to address global nutritional deficiencies and enhance crop resilience in the face of climate change, ultimately contributing to a sustainable and food-secure future.

DOI: 10.61137/ijsret.vol.10.issue5.249

Sentiment Analysis of Customer Reviews Using Natural Language Processing
Authors:-Ms. Jyoshna Butty, Ms. Ankita Gupta, Dr. Jasbir Kaur, Assistant Professor Ms. Ifrah Kampoo, Assistant Professor Mr.Suraj Kanal

Abstract-The purpose of this research is to use Natural Language Processing (NLP) to categorize customer reviews into three groups: favorable, negative, and neutral. We employ machine learning models to categorize sentiment by preprocessing textual data. Matplotlib is then used to illustrate the results using area plots, pie charts, and keyword-based analysis. Our investigation shows how sentiment analysis, which provides actionable insights generated from consumer feedback, can advise firms on how to improve customer satisfaction and experience.

DOI: 10.61137/ijsret.vol.10.issue5.250

Comparative Assessment of Phytochemical Contents of Diet Combinations Made From Lima Beans and Cowpea
Authors:-Olife, Ifeyinwa Chidiogo, Ayatse, James O.I, Ega, RAI, Anajekwu, Benedette Azuka

Abstract-Legumes are important sources of nutrients and phytochemicals. Phytochemicals are plant derived chemicals known to possess many properties, including anti-oxidant, anti-microbial and physiological activities. Though phytochemicals are vital to both plants and animals, they are not established as essential nutrients and they can also have adverse effects by functioning as anti-nutrients. Processing affects the nutritional values of plant-based food and such food products may lose part of their functionality as these chemicals are sensitive to the impact of processing methods. Therefore, the objective of this study was to evaluate the phytochemical contents of legume-based lima beans/cowpea diet combinations so as to recommend the best combination to maximize their pharmacological potentials and reduce the anti-nutritional effects. Quantitative analysis of phytochemical constituents of the formulated diet combinations were carried out using standard procedures for oxalate, alkaloids, flavonoids, saponin, cardiac glycosides, tannin, phytate, cyanogenic glycoside while spectrophotometer method was used for the determination of steroids and phenols. Among whole legume-based diet combinations, 75:25 ratio lima beans/cowpea diet recorded the lowest alkaloid, flavonoid, cyanogenic glycosides and saponin levels of 5.20 %, 4.0 %, 4.8 % and 3.0 %, respectively. However, among the dehulled legume-based diet, the 50:50 ratio lima beans/cowpea combination had the lowest saponin, steroid, alkaloid, cyanogenic glycoside and flavonoid levels of 1.90 %, 5.38 mg/g, 3.0 %, 5.55 % and 4 %, respectively. Over all, the 50:50 ratio dehulled lima beans/cowpea diet combination, compared to other diet combinations, had the lowest contents of saponin, steroid, alkaloid and flavonoid out of the nine phytochemicals quantified. Pharmacological properties of phytochemicals are beneficial to human health. However, these phytochemicals could also be detrimental to human health when consumed in excess. Therefore, legume-based lima beans/cowpea diet combination ratios should be done with respect to the pharmacological properties of interest.

DOI: 10.61137/ijsret.vol.10.issue5.251

Designing of Nozzle for Unmanned Water Powered Aerial Vehicle
Authors:-Raj Sharma

Abstract-This project is used to develop a conceptual design for an UNMANNED WATER POWERED AERIAL VEHICLE (UWAV) that utilizes a waterjet propulsion system instead of traditional propulsion methods such as propellers or jet engines. The project idea is based on the flyboard system where the drone flies with the force generated by water jet from the nozzles and directing the force in required directions. The purpose of the project is to optimize the efficiency of the waterjet propulsion system to achieve maximum thrust while minimizing energy consumption by improving the design of nozzle. This propulsion systems reduces noise generated in conventional UAV’s. These types of drones are used for Aquatic ecosystem surveillance, agriculture, cleaning of building without human interface.

DOI: 10.61137/ijsret.vol.10.issue5.252

Innovative Antenna Coupling Approaches for Low SAR in Smartphone Communication Modes/strong>
Authors:-Associate Professor Dr TVS Divakar, Vambaravelli Mohini, Rupanagudi Siva Reddy

Abstract-This paper provides a thorough investigation of coupling adjustment using two antennas in the speak position for voice conversations on recent smart phones. Using the optimal relative phase between components helps minimize SAR while maintaining efficiency through power splitting and appropriate interelement coupling. When not in talk position, antenna elements can remain used for MIMO without considerably lowering their fundamental limit of capacity, although this is of secondary significance. This approach is applicable to mobile communications frequencies ranging from 1.8 – 10. 8 GHz, given that the ground plane possesses the suitable form factor. This study shows that optimizing two PIFAs at 10.1 GHz may Reduce SAR by more than 50% over one element. SAR reduction remains consistent irrespective of the user’s head structure or manipulation of the device while speaking.

Risk Management Using VaR, CVaR and Baye’s Model/strong>
Authors:-Arshad Ahmad Khan, Kiran Kumari

Abstract-Managing risks effectively is essential in the world of trading and investing to reduce the chance of losing money and improve the quality of decisions. This document delves into how Value at Risk (VaR), Conditional Value at Risk (CVaR), and Bayes’ Theorem are used to evaluate and handle financial risks. VaR offers a numerical estimate of the possible decrease in the value of a portfolio over a certain period, providing a glimpse into the most severe outcome under typical market conditions. CVaR builds on this by looking at the expected loss when the VaR threshold is surpassed, tackling the rare but significant risks that VaR might miss. Bayes’ Theorem is used to refine risk evaluations with fresh data, boosting the reliability of risk prediction models. By examining these techniques and how they can be combined, the document seeks to introduce a detailed strategy for risk management, showing how the use of these methods can result in more thorough risk evaluations and better strategic choices in trading and investing. The research also points out real- world uses and its constraints, providing a guide for refining risk management strategies in ever-changing financial landscapes.

Problem in Reviewing Software Testing in the Current Decade/strong>
Authors:-Professor Dharmaraj S Kumbar

Abstract-Software testing is an inalienable part of the software development life cycle, which directly influences product quality, stability, and, finally, user satisfaction. While the complexity of software systems is growing, testing methodologies face growing challenges related to a lack of coverage, high costs, or time-to-market. In this respect, 56% of organizations currently report that one of the biggest pains is certainly a lack of test coverage, while 40% report high operational costs as a significant barrier to effective testing. This paper looks at these key challenges and some of the emerging trends—impelling AI-driven testing, integration with DevOps, automation, and low-code/no-code platforms—that are rewriting this landscape. With such modern solutions to solving difficulties, organizations will be able to optimize the testing processes, enhance productivity, and deliver quality software that aligns with customer expectations and market demand.

Semigroups in Automata Theory and Formal Languages/strong>
Authors:-Nikuanj Kumar, Dr. Bijendra Kumar

Abstract-Semigroups are essential to many areas of theoretical computer science, including formal languages and automata theory. A thorough mathematical investigation of semigroups and their use in computer models is presented in this study. We first give a thorough explanation of semigroups, covering their algebraic structure, attributes, and classifications. We then discuss their importance in automata theory, with particular attention to how finite automata are represented as semigroups and how this helps with language recognition. Key ideas like syntactic semigroups are emphasised as well as the relationship between semigroups and regular languages. The paper also covers real-world computational applications, such as algorithmic models for machine learning and language processing. through the integration of practical computer applications with rigorous mathematical content. The versatility of semigroups in the nexus of computer science and mathematics is illustrated by this work.

Malaysian Noodle Images Classification System Using CNN and Transfer Learning/strong>
Authors:-Ibrahim Abba, Ubaid Mohammed Dahir, Mohammed Shettima

Abstract-Image Recognition is a term used to describe a set of algorithms and technologies that attempt to analyze images and understand the hidden representations of features behind them and apply these learned representations for different tasks like classifying images into distinct categories automatically, understanding which objects are present and where in an image, etc. These technologies leverage various traditional computer vision methods as well as machine learning and deep learning algorithms to achieve the required results for solving such problems. This paper shows a recognition model for classifying Malaysian Noodle images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition were used for this task. The model uses a deep learning process that was trained on natural images (AlexNet and SqueezeNet dataset) and was fine-tuned to generate the predictive Noodle model, which comprised approximately 4308 images. The dataset was divided into ten groups/categories of Noodles images which include the following: Mee Bee Hoon Goreng, Mee Bee Hoon Sup, Mee Goreng, Mee Koay Teow Goreng, Mee Koay Teow Sup, Mee Laksa Goreng, Mee Laksa Sup, Mee Maggi Goreng, Mee Maggi Sup, Mee Sup. The trained model achieved high accuracy on the test set, demonstrating the feasibility of this approach.

DOI: 10.61137/ijsret.vol.10.issue5.253

Review on: Methodology of Nanoemulsion Formulation/strong>
Authors:-Zadmuttha Bhavana P., Tandale Prashant S., Garje S.Y., Sayyed G. A.

Abstract-Nanoemulsions are thermodynamically stable systems consisting of two immiscible liquids combined with emulsifying agents, such as co- surfactants and surfactants, to create a single phase. Nanoemulsion represents an innovative drug delivery system that facilitates controlled or sustained release of medications. It is characterized as a dispersion comprising a surfactant, oil, and a clear aqueous phase, exhibiting kinetic or thermodynamic stability with droplet sizes ranging from 10 to 100 nanometers. The application of nanoemulsions enhances the solubility and bioavailability of lipophilic drugs, offering numerous advantages for drug delivery. Various methods exist for the preparation of nanoemulsions, including high-energy emulsification, spontaneous nanoemulsion formation, and phase inversion temperature (PIT) techniques. This system is applicable across multiple delivery routes, thereby demonstrating significant potential in diverse fields such as cosmetics, therapeutics, and biotechnology.

A Review on Machine Learning Assisted Handover Mechanisms for Future Generation Wireless Networks/strong>
Authors:-Priyanka Vishwakarma, Dr. Kamlesh Ahuja

Abstract-Machine Learning and Deep Learning Algorithms have been explored widely to identifyy potential avenues to optimize wireless networks. One such area happens to be a data driven model for initiating handover among multiple access techniques such as OFDM and NOMA. With increasing number of users and multimedia applications, bandwidth efficiency in cellular networks has become a critical aspect for system design. Bandwidth is a vital resource shared by wireless networks. Hence its in critical to enhance bandwidth efficiency. Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple access (NOMA) have been the leading contenders for modern wireless networks. NOMA is a technique in which multiple users data is separated in the power domain. A typical wireless system generally has the capability of automatic fall back or handover. In such cases, there can be a switching from one of the technologies to another parallel or co-existing technology in case of changes in system parameters such as Bit Error Rate (BER) etc. This paper presents a review on existing machine learning based approaches for handover prediction in future generation wireless networks. The salient features of each of the approaches has been highlighted along with identifying potential research gaps.

DOI: 10.61137/ijsret.vol.10.issue5.255

Social Media Analysis in Criminal Investigation/strong>
Authors:-Anish Chauhan, Aman Kumar, Anushka Thakur, Assistant Professor Manish Goyal,

Abstract-Social media platforms have become an integral part of modern society, offering a wealth of data that can be instrumental in criminal investigations. This research paper examines the evolving role of social media analysis in the realm of criminal investigation. Focused on understanding the impact, challenges, and ethical considerations, this study delves into the multifaceted ways law enforcement agencies leverage social media data to solve crimes. The paper begins by exploring the transformative effect of social media on the investigative landscape, highlighting its potential as both a valuable tool and a source of complexity. It investigates the ethical and legal dimensions surrounding the use of social media data as evidence in criminal cases, addressing concerns of privacy, authenticity, and admissibility. Furthermore, this research sheds light on how social media platforms are utilized for crime detection, prevention, and profiling. It scrutinizes the methodologies, tools, and techniques employed in social media analysis to extract actionable intelligence for law enforcement purposes. Amidst the benefits, the paper examines the challenges and limitations inherent in social media analysis for criminal investigations, encompassing issues related to data validity, biases, and the rapid evolution of online platforms. Ultimately, this study aims to provide a comprehensive overview of the intersection between social media analysis and criminal investigations, presenting insights into its efficacy, limitations, and the evolving landscape of digital evidence in modern law enforcement. this abstract encapsulates the key areas of focus within the scope of social media analysis in criminal investigation, giving a glimpse of the research paper will explore.

DOI: 10.61137/ijsret.vol.10.issue5.256

Opex Home Solutions/strong>
Authors:-Yash Hulle, Abhishek Jadhav, Sangram Chougule, Sham Patil, Professor Girish Awadhwal

Abstract-The integration of modern technology into home design and architecture has transformed how homeowners and contractors engage with construction data and design options. This paper introduces Opex Home Solutions, a comprehensive platform that leverages artificial intelligence (AI) and machine learning (ML) to enhance the process of home design, selection, and customization. By utilizing a recommendation system and natural language processing (NLP)-driven search capabilities, the platform provides personalized home design suggestions based on user preferences and advanced query understanding. The system architecture is built on scalable.

DOI: 10.61137/ijsret.vol.10.issue5.257

Cybersecurity in Digital Therapeutics: Navigating the Risks Associated with Sensitive Health Data/strong>
Authors:-Sooraj Sudhakaran

Abstract-Imagine reaching for your smartphone to access a prescribed app that helps manage your chronic condition, only to wonder: “Is my personal health data truly safe?” As digital therapeutics revolutionize healthcare by bringing treatment directly to our fingertips, they also open new doors for potential security breaches. From busy doctors accessing patient records on tablets to individuals tracking their mental health through apps, the digital therapeutic revolution touches countless lives daily. But with this incredible progress comes a critical challenge: keeping sensitive health information secure in an increasingly connected world. Our paper delves into the real-world cyber threats that digital therapeutic platforms face, from data breaches that could expose personal health information to potential tampering with treatment protocols. We explore practical strategies for protecting sensitive health data and outline user-friendly approaches to enhance cybersecurity as these digital treatments evolve. By sharing actual cases and relatable scenarios, we highlight why it’s crucial to build security measures into these applications from the ground up, ensure they meet necessary regulations, and foster teamwork among everyone involved – from app developers to healthcare providers. Ultimately, our goal is to help create a digital therapeutic environment where patients can focus on their health journey without worrying about the safety of their personal information.

DOI: 10.61137/ijsret.vol.10.issue5.258

Application of Drone Technology in Evacuation Guidance and Emergency Support/strong>
Authors:-Madhav Venkatachalam

Abstract-Currently, drone technology is not widely applied in the emergency sector due to the high cost of implementation, and limited capabilities in terms of first response, where the drone is mainly used to collect data and provide a live feed. Drones are mostly seen as reconnaissance tools, unable to perform any vital “boots on the ground work”. However a possible scope for drones in certain evacuation and emergency situations exists, which is explored in this paper. To support and analyze the use of such drones, using a novel prototype drone, combining both a bluetooth module and flight controller in separate systems, was built and deployed for a relatively low cost to demonstrate the applications of the technology in real-world scenarios.

DOI: 10.61137/ijsret.vol.10.issue5.259

Self Balancing Robot with Autonomous Navigation and Obstacle Detection/strong>
Authors:- Professor Disha Nagpure, Bhakti.B.Bagal, Vaishnavi.B.Kute, Aakanksha.D.Pednekar, Akanksha.S.Shinde

Abstract-This paper details the design and implementation of a two-wheeled self-balancing robot capable of following a predefined path while detecting and avoiding obstacles. The robot utilizes an Infrared (IR) sensor array to track the path and an ultrasonic sensor to identify and measure the distance to obstacles in real-time. The self- balancing mechanism is achieved through a feedback control system that stabilizes the robot on its two wheels using a combination of gyroscopic and accelerometer data. A proportional-integral-derivative (PID) controller is employed to maintain stability and ensure smooth navigation along the path. The system’s effectiveness was evaluated through a series of experiments, demonstrating the robot’s ability to maintain stability, follow complex paths, and avoid collisions with obstacles.

DOI: 10.61137/ijsret.vol.10.issue5.261

Experimental Study on Effect of Heat Transfer Characteristics in a Corrugated Tube Pipe Having Different Pitch Length/strong>
Authors:-Assistant Professor Shailesh M Patel

Abstract-Heat transfer augmentation is a technique needed to increase the thermal performance of heat exchangers effecting energy, material & cost savings. This heat transfer augmentation technique lead to increase in heat transfer coefficient but at the cost of increase in pressure drop. So, analysis of heat transfer rate and pressure drop are the major parameter which are to be taken care of during design of heat exchanger using any of this techniques. One such technique is the use of corrugated tube instead of smooth tube. Corrugated tubes can enhance heat transfer coefficient on both the outer and inner heat transfer surface area without a significant increase in pressure drop. Experimental study is carried out on corrugated double pipe heat exchanger, in which comparison of heat transfer is carried out on smooth pipe and corrugated pipe having different pitch length. Pitch length of 0.045, 0.055 and 0.065 meter were taken and comparison of results were done with smooth pipe.

Design of 15th Order Length 32 Digital Differentiator Using Genetic Algorithm/strong>
Authors:-Anantnag V Kulkarni

Abstract-An essential tool for signal processing is the digital differentiator. It is used in a wide range of devices, including high frequency radars and low frequency biomedical equipment. Digital differentiators are an essential building piece of emerging areas like online signature verification and touch screen tablets. Although a variety of techniques have been established to build differentiators of all kinds, parameter optimization still has room for improvement. The challenge of designing differentiators is difficult. This work presents the design of a fifteenth order digital differentiator using the Genetic Algorithm, one of the optimization approaches.

A Review of Renewable Energy Based Distributed Generation in Electrical Power System/strong>
Authors:-Ravindra Sharma, Associate Professor Dr.Chandrakant Sharma

Abstract-It is possible to describe distributed generation as power generation by small scale generating units installed in distribution systems. There is a steady growth in the penetration of distributed generation (DG) units into electric distribution systems. DG allocation is the process of finding the optimal type, location and size of DG units. The allocation of DGs is a hot research field and poses a difficult problem in electrical power engineering. This paper discusses the recent research work on the issue of DG allocation from the point of view of their optimization algorithms, targets, and decision variables, type of DG, implemented limitations and type of modeling of uncertainty used. In this research an overview of DG types and various DG technologies are highlighted. Some DGs challenges ahead with current drive towards smart grid networks is also discussed. The research gaps are defined on the basis of their views on current research work and some helpful suggestions will be made for future research on DG allocation. The author strongly believes that this paper could be beneficial in the related field for researchers and engineers.

DOI: 10.61137/ijsret.vol.10.issue5.262

Credit Shield Solutions: Credit Card Fraud Detection System Using Machine Learning Approach/strong>
Authors:-Assistant Professor Mr. Rakesh Jaiswal, Aditya Krishna, Lucky Singh Rajput, Divyansh Rathore, Kishore Bole

Abstract-In recent times, the exponential growth in the usage of credit cards has increased fraudulent activities, which impacts financial institutions significantly. A large number of machine learning (ML) techniques are used to detect fraudulent transactions in order to thwart such threats. This paper represents a review of state-of-the-art ML algorithms used for credit card fraud detection and further analyzes their performance with regard to accuracy and privacy. Besides, a hybrid approach combining ANN with federated learning is proposed. This approach has the potential to not only increase the detection accuracy but also mitigate data privacy issues. The given model has had promising results for real-time application in credit card fraud detection while keeping users’ data private. Keywords— Artificial Neural Networks, Credit Card Fraud Detection, Federated Learning, Machine Learning, Privacy-Preserving, Blockchain. Credit card fraud has been an exploding problem with the large-scale growth of digital transactions, posing significant risk exposure to financial institutions. In this paper, we conducted a comprehensive review of various ML techniques applied to credit card fraud detection, touching on both aspects of accuracy and concerns over data privacy. We herein present a novel hybrid model based on the paradigm combination of ANN and FL for overcoming challenges arising from accuracy and privacy protection in detection. The advantages of the model are the usage of pattern recognition ability on ANN and its preservation of data privacy through decentralized learning. It has promising uses and outcomes since high detection accuracy and user privacy persistence were noted in achieving this characteristic. This makes this type of model suit fraud detection applications applied real-time. Keywords: Credit card fraud detection Machine learning Artificial neural networks Federated learning Privacy.

DOI: 10.61137/ijsret.vol.10.issue5.263

Exploring the Evolution, Impact and Growth of Investment and Trading Applications/strong>
Authors:-Shivang Gurjar, Umesh Bashyal, Khushi Vishwakarma, Priyanshi Shah, (Dr.) Monika Bhatnagar

Abstract-This paper explores the evolution, impact, and growth of investment and trading applications in the financial ecosystem, emphasizing how these platforms have revolutionized access to the market for retail and institutional investors alike. With the rise of fintech innovations, applications such as robo-advisors, micro-investing apps, and algorithmic trading platforms have democratized investing, lowering barriers to entry and automating portfolio management. These apps leverage advanced technologies like artificial intelligence (AI), machine learning (ML), and big data analytics to offer personalized investment strategies, real-time trading, and portfolio optimization. The paper examines the technological underpinnings of these applications, highlighting the role of AI and algorithmic systems in transforming traditional trading approaches. Case studies of platforms like Groww, Zerodha, and Upstox illustrates how investment apps have expanded market participation, particularly among younger, tech-savvy investors in India. However, the widespread adoption of these platforms has also raised concerns about overtrading, market manipulation, and speculative behaviour. Through a comprehensive review of the benefits, risks, and regulatory challenges, this research also addresses ethical concerns surrounding the gamification of trading and the protection of inexperienced investors. As investment apps continue to evolve, the paper explores future trends, including the integration of blockchain in decentralized finance (DeFi), increased regulatory scrutiny, and the growing focus on sustainability and environmental, social, and governance (ESG) investments. This study provides valuable insights into the ongoing transformation of the financial landscape through technology-driven investment solutions.

DOI: 10.61137/ijsret.vol.10.issue5.264

Review on Design of Bridge Structures/strong>
Authors:-Research Scholar Sombrat Arjariya, Assistant Professor Rahul Sharma

Abstract-This review synthesizes findings from a collection of papers investigating the design, analysis, and optimization of bridge structures. The reviewed studies cover a wide spectrum of topics, including T-beam and Box Girder designs, dynamic behavior under heavy loads, parametric studies for optimal design, and innovative optimization techniques. The papers collectively highlight the importance of accounting for factors such as material choices, loading conditions, and dynamic effects to achieve economically viable and structurally robust bridge designs. The insights gained from these studies contribute to the current knowledge base in bridge engineering and offer guidance for researchers, engineers, and practitioners seeking to enhance the efficiency and resilience of bridge structures.

A Review of Job Recommendation Systems Using Machine Learning/strong>
Authors:-M. Tech Scholer Reena Tiwari, Assistant Professor Mrs.Vaishali Upadhyay

Abstract-This research aims to assess recent literature on job recommender systems (JRS), placing particular emphasis on studies that consider temporal and reciprocal aspects in job recommendations. Unlike previous reviews, we highlight how incorporating these perspectives can improve model performance and lead to a more balanced distribution of applicants across similar jobs. Additionally, we examine the literature on algorithmic fairness, finding that it is rarely addressed, and when it is, authors often mistakenly assume that simply removing discriminatory features is sufficient. Many studies label their models as “hybrid” but fail to clarify what these methods entail, so we used existing recommender taxonomies to categorize these hybrids into more specific subclasses. We also found that data availability, particularly click data, significantly influences the choice of validation techniques. Finally, the research shows that generalizability across different datasets is rarely considered, though error scores can vary between datasets.

Autonomous Braking System for Automobile Powered by Artificial Intelligence and Reinforcement Learning/strong>
Authors:-Sukhwinder Sharma, P Hrithika kundar, Saksha K Bangera, Sandesh R Bhat, Shrinit R Poojary

Abstract-The rising number of accidents and injuries on the roads has created a pressing need for systems that can provide safety and protection to passengers while ensuring high performance in adverse conditions. Traditional braking systems may not always respond in time to prevent collisions, particularly in adverse conditions or emergencies. These systems rely on the driver to apply the brakes manually, which can result in delayed response times or even complete failure to apply the brakes in time. Additionally, these systems do not take into account factors such as road conditions, vehicle speed, and driver reaction time. To overcome these limitations and meet the needs, the Autonomous Braking System has been introduced in commercial vehicles, providing rapid brake response according to the driver’s need and safety. This system employs an intelligent control strategy that uses image processing technology based on object detection with the help of haarcascading object detection technique. Computer vision, a crucial component of this system, allows for the detection of path which is being followed by vehicle using Canny’s lane detection technique, obstacles and objects in the vehicle’s path. This information is then used to make decisions about when and how to apply the brakes, ensuring quick and safe stops. Reinforcement learning is also a key element of the system, allowing it to learn from its experiences and make better decisions over time. This involves providing feedback on the system’s performance and using it to adjust its behavior and improve its performance over a period of time. The haarcascading technique here recognizes captured objects as potential obstacles, feeding this information into the algorithm to take appropriate decisions. Overall, the Intelligent Braking System promises to significantly improve safety and performance in commercial vehicles.

DOI: 10.61137/ijsret.vol.10.issue5.266

Optimal Energy Management System Control of Permanent Magnet Direct Drive Linear Generator for Grid-Connected FC-Battery-Wave Energy Conversion/strong>
Authors:-Professor Adel Elgammal, Assistant Professor Curtis Boodoo

Abstract-The Wave Energy Conversion System (WECS) control strategy is presented in this study to make sure the system operates at its best under fluctuating wave resource situations. The suggested system consists of a MOPSO based MPC approach, a point absorber WEC oscillating in heave, back-to-back power converter for grid connections, and a linear permanent magnet generator. Despite the benefits of model predictive control, problems including switching frequency variations, steady-state errors, high processing costs, and constrained prediction horizons continue to exist. The article presents a method that incorporates the switching control action into the cost function while maintaining the finite nature of a model predictive control to handle the switching frequency issue. In order to minimise switching frequency variations while also addressing other control goals, such as regulating the direct current linked voltage and controlling the flow of active and reactive power, the switching control weight factors are optimised. In order to increase power quality, a fuel.

DOI: 10.61137/ijsret.vol.10.issue5.267

Use of Aeroponics Technique for Potato (Solanum Tuberosum) Mini Tubers Production in India: A Review/strong>
Authors:-Tamanna Sharma, Dr.Shilpa Kaushal, Shubham

Abstract-Potato, also known as Solanum tuberosum L., ranks as the third most vital food crop worldwide and is essential for food security, especially in developing countries. Potatoes grow from tubers instead of seeds like cereals, making them susceptible to seed-borne diseases that lower seed quality and decrease yields in the long run. India, a leading potato-producing nation, is facing a major challenge due to a significant lack of high-quality seed tubers, as only 20-25% of the required amount is being met by state and central agencies. Identified as promising solutions to address this problem are advanced methods of multiplication such as micropropagation, hydroponics, and aeroponics. These technologies make the production of disease-free Mini tubers faster and more efficient. Aeroponics, a method of growing plants without soil using mist, has demonstrated significant potential for producing seed potatoes on a large scale. Derived from research conducted in the early 1900s, aeroponics has advanced to increase crop yields, reduce disease risks, and improve production efficiency. Small tubers created using this method, varying from 5 to 25 mm in size, are grown in controlled settings such as greenhouses and growth chambers. Aeroponics provides several benefits, including enhanced water usage, quicker growth, increased harvest, and decreased reliance on pesticides and herbicides. Nevertheless, it also poses difficulties such as expensive initial costs, the requirement for specific expertise, and accurate management of nutrients. By making advances in temperature, nutrition, and light management, aeroponics presents a hopeful remedy for the lack of seed potatoes and a means to enhance worldwide potato yield.

DOI: 10.61137/ijsret.vol.10.issue5.268

Analysis of Methods of Fabricating Perovskite Photovoltaic Cells/strong>
Authors:-Barakur Calvin Azo, Al Moustafa Saad

Abstract-Perovskite solar cells (PSCs) are a promising photovoltaic technology utilizing organometal halides for high-efficiency, low-cost solar energy conversion. They have the potential to revolutionize renewable energy as a result of their outstanding photovoltaic performance and a surge in their efficiency advancements. with unprecedented progress on certified power conversion efficiency (PCE) from 3.8% to over 25% within a decade. However, large-scale, cost-effective fabrication remains a hurdle for commercialization The Objective of the research is to investigate various Perovskite Solar Cells (PSC) fabrication methods with the goal of identifying scalable and efficient fabrication methods for commercially viable PSCs.

DOI: 10.61137/ijsret.vol.10.issue5.269

Fundamental of Tissue Culture and it’s Future Prospects in Crop Improvement/strong>
Authors:-Anjali, Kopal Singh, Dr. Gurshaminder Singh

Abstract-The science of growing plant cells, tissues, or organs separated from the mother plant on artificial media is known as plant tissue culture. It has various useful goals and comprises research methodologies and approaches from numerous botanical disciplines. It is essential to acquire a thorough understanding of the processes involved in growing and working with plant material in “test tubes” before starting to propagate plants using tissue culture techniques.In a relatively short period of time, during the height of the plant tissue culture era in the 1980s, numerous commercial laboratories were set up worldwide to take use of the potential of micropropagation for the large-scale production of clonal plants for the horticultural sector.The most widely used biotechnological techniques are those based on plant tissue culture. These include investigations into the processes involved in plant development, functional gene studies, the creation of transgenic plants with particular industrial and agronomical traits, healthy plant material, the preservation and conservation of the germplasm of vegetative propagated plant crops.Plant tissue culture has to lead to significant contributions in recent times and today they constitute an indispensable tool in the advancement of agricultural sciences and modern agriculture. This review would enable us to have an analysis of plant tissue culture development for agriculture, human health and well being in general.

DOI: 10.61137/ijsret.vol.10.issue5.270

Assessing HRIS Effectiveness in Compliance Management among IT Employees within Trichy District/strong>
Authors:-Mrs. A.Keerthana Devi

Abstract-The Information Technology (IT) sector necessitates strict compliance measures to maintain operational integrity and data security because of the quickly changing regulatory environment. This research aims to assess how well Trichy’s IT organizations manage compliance using Human Resource Information System (HRIS) solutions. The research, which involved 2347 individuals in a variety of jobs across several IT businesses, used a thorough questionnaire to explore how employees perceive HRIS performance in negotiating intricate compliance concerns unique to the IT industry. Employee familiarity with compliance rules, data security, privacy features, audit trail maintenance, efficiency of documentation, and adequate user support are among the factors that are being examined. Regression modelling, multivariate analysis, and statistical validation approaches are used in this work to find connections and underlying patterns that affect compliance efficiency. The results of research highlight how important it is for users to be conversant with regulations, since they show a favourable link with improved compliance procedures. Data security plays a critical role in IT firms and is identified as a fundamental factor effecting compliance efficiency. In Trichy’s IT industry, accessibility and the efficacy of HRIS characteristics emerge as critical factors in maximizing compliance procedures. The study’s conclusions provide specific advice on how to improve HRIS capabilities so that they smoothly mesh with the complex compliance requirements that are common in Trichy’s IT environment. Consequences and significance, this study adds to a better knowledge of how HRIS systems can be tailored to successfully navigate and manage compliance in the always changing regulatory landscape of the IT sector. The consequences encompass methods for technology adoption within organizations, guaranteeing strong compliance management procedures that are essential for maintaining the security and integrity of IT operations within Trichy’s IT industry.

DOI: 10.61137/ijsret.vol.10.issue5.271

Magic Hexagon of Order-4 with Star Configuration: A Study on Symmetry and Combinatorial Patterns/strong>
Authors:-Himadri Maity

Abstract-This paper presents a new magic hexagon of Order-4 with 24 cells, which exhibits a unique star configuration inside the hexagon. The hexagon follows distinct combinatorial patterns where all combinations of selected numbers result in equal sums. A total of 52 combinations are identified with a constant sum of 190, making this work significant in the study of mathematical patterns and symmetry.

DOI: 10.61137/ijsret.vol.10.issue5.272

Agri Shield: Identify Plant Disease Using Machine Learning/strong>
Authors:-Aditya Bathre, Aajinkya Ingalkar, Awanish Srivastava, Anurag Patel

Abstract-This paper introduces Agri Shield, an innovative approach using machine learning, particularly convolutional neural networks, for predicting plant diseases and recommending sustainable individualized remedies. Agri Shield embodies early-stage disease detection with ecologically friendly solutions, making it easier for farmers and plant enthusiasts to care for plants, as such information would be sourced from a multiplicity of sources. The proposed system is able to detect 20 different diseases of 5 common plants with 93% accuracy.

DOI: 10.61137/ijsret.vol.10.issue5.273

Transforming Libyan Organizations through AI: Assessing Readiness and Strategic Pathways/strong>
Authors:-Ali Bakeer

Abstract-In the context of Libya’s ongoing digital transformation efforts, many sectors are still grappling with the early stages of deploying advanced technologies, particularly artificial intelligence (AI) tools. This study aims to addresses the pressing issue of AI readiness among Libyan organizations, focusing on the critical success factors that facilitate or hinder the Deployment of AI technologies. The study employs a case study methodology, collecting qualitative data through structured surveys from eighteen participants across various sectors, including education, healthcare, and finance. The findings reveal critical barriers to AI deployment, such as inadequate digital infrastructure, limited internet access, insufficient government support, and a shortage of skilled professionals. In response, a structured framework is developed, outlining essential steps for organizations to successfully integrate AI applications. This framework emphasizes the need for assessing organizational readiness, setting strategic objectives, selecting appropriate AI solutions, conducting pilot projects, implementing training programs, and fostering a culture of continuous improvement. Ultimately, this research aims to bridge the gap between the theoretical benefits of AI and the practical realities faced by Libyan organizations, providing a pathway toward a future where AI drives productivity, innovation, and informed decision-making. The insights derived from this study underscore the importance of collaboration between public and private sectors to ensure sustainable and effective AI Deployment in Libya.

DOI: 10.61137/ijsret.vol.10.issue5.274

Review on Basics of Cold Weather Concrete/strong>
Authors:-Anand Korakoppu

Abstract-Cold weather conditions pose significant challenges to concrete construction, primarily due to their impact on the hydration process, strength development, and overall durability of concrete. When temperatures drop below 10°C (50°F), the rate of chemical reactions in concrete slows down considerably, which can lead to delayed strength gain and potentially incomplete hydration. This is particularly critical during the early curing phase, as concrete is most vulnerable to freezing at this stage. If concrete freezes before reaching a compressive strength of approximately 5 MPa (725 psi), the formation of ice crystals can cause internal damage, resulting in spalling, cracking, and reduced long-term durability. Additionally, cold temperatures can adversely affect the workability of the mix, making it stiffer and more difficult to place and finish. This review not only examines these detrimental effects but also explores various methods for mitigating them, such as using heated materials, employing insulating techniques, and incorporating accelerating admixtures. Furthermore, it highlights best practices for successful concrete placement and curing in cold weather, emphasizing the importance of careful planning and monitoring. By understanding and addressing these challenges, construction professionals can ensure the integrity and longevity of concrete structures, even in adverse conditions.

DOI: 10.61137/ijsret.vol.10.issue5.275

Review Paper on LC3 Concrete: Properties, Applications, and Future Directions/strong>
Authors:-Assistant Professor K Sagar

Abstract-LC3 (Lime-Cement-Limestone) concrete is an innovative material that incorporates limestone powder, reducing the environmental impact associated with traditional Portland cement. This paper reviews the properties, benefits, and challenges of LC3 concrete, alongside its applications in construction and potential for sustainable development. LC3 (Lime-Cement-Limestone) concrete is a cutting-edge material designed to mitigate the significant environmental challenges posed by traditional Portland cement, which is responsible for approximately 8% of global CO2 emissions due to its production process. By incorporating limestone powder into the concrete mix, LC3 not only reduces the volume of Portland cement required but also enhances the hydration process, leading to improved mechanical properties. This innovative blend allows for comparable or even superior compressive strength and durability compared to conventional concrete. Additionally, the finer particle size of limestone enhances workability, making the mixing and placement processes more efficient. This composite material embodies a shift toward more sustainable construction practices by utilizing abundant local resources and decreasing the reliance on energy-intensive cement production.

DOI: 10.61137/ijsret.vol.10.issue5.276

Cryptocurrency Arbitrage: Exploiting Twin Exchange Price Differences/strong>
Authors:-Srijan Jaiswal, Syed Afzal Ali, Shubham Sharma, Assistant Professor Mohammad Alim

Abstract-Arbitrage trading takes advantage of the price differences existing in various markets; it is one of the major methods applied in the cryptocurrency world. This paper compares twin exchanges on different cryptocurrency platforms for the effectiveness of arbitrage trading. We introduce a new method for the identification and exploitation of arbitrage opportunities between paired exchanges with equivalent cryptocurrency pairs, examining variations of price and liquidity. Utilizing the massive dataset comprising outputs from various platforms, this research uses quantitative methods for determining arbitrage profitability, efficiency, and risks. Transactional costs, speed of execution, and existing market conditions all face analyses to establish the feasibility of arbitrage opportunities. This paper will therefore identify the most feasible conditions and strategies for exploiting twin exchange arbitrage while making obvious some of the limitations involved in such activities. It is mainly focused on enriching the knowledge about cryptocurrency arbitrage and offering real time insights to traders interested in maximizing their strategies across various platforms.

Life Depending on Digital Media: An Analysis on Contemporary Society/strong>
Authors:-Kajal Nanda

Abstract-This research paper explores the expansion of digital media in human life. The very existence of human beings seems to be enjoying the interference of digital life. From personal to professional, everything depends upon it. All aspects including Educational, Medical, cultural, communicational, and entertainment sectors have one thing in common which is digitalisation. It comes with both positive and negative impacts. This study draws upon a combination of qualitative and quantitative data to understand the influence of digital media on human life and lifestyle and potential consequences of over-dependence.

DOI: 10.61137/ijsret.vol.10.issue5.277

Classification of Packet Length Spectral Analysis for IoT Network Traffic Using Random Forest Extra Tree Categorization/strong>
Authors:-N.Deena Nepolian, Dr.Abhisha Mano, B.P.Beno Ben

Abstract-The swift advancement of the Internet of Things (IoT) has ushered in a wealth of benefits, allowing countless interconnected devices to interact and exchange data effortlessly. Previously, network traffic including unusual patterns, was mainly produced by established, secure endpoints with strong security features, like smartphones. With the advent of the Internet of Things (IoT) no matter how small or intricate device, now has the capability to produce unusual levels of network activity. One of the biggest challenges facing the IoT industry is network traffic, which can have a negative impact on the overall performance of IoT devices and systems. To address this issue, a random forest classifier has been developed specifically for classifying IoT data. Extra Trees offer a significant benefit by minimizing bias. This is achieved by randomly sampling from the entire dataset when building the trees. Random Forest is a widely recognized machine learning technique which favours accuracy, reliability, flexibility and scalability. The process of data preprocessing involves transforming unrefined data into a refined dataset. Chi-square based feature extraction is utilized to extract relevant information and this technique enhances classification by selecting the most important features from the extraction regions. In the end, the chosen characteristics are inputted into both an extra tree and random forest classifier to ensure precise categorization and the implementation of this endeavor is carried out utilizing Python programming.

DOI: 10.61137/ijsret.vol.10.issue5.278

Blockchain Technology in Global Healthcare: A Paradigm Shift/strong>
Authors:-Dr.Rohith Jampani

Abstract-The global healthcare landscape is rapidly evolving, driven by technological advancements, demographic shifts, and rising expectations for personalized, secure, and efficient medical care. However, healthcare systems face a myriad of challenges, including fragmented data systems, cybersecurity threats, inefficiencies, and opaque supply chains. Blockchain technology, with its decentralized, immutable, and transparent nature, has emerged as a promising solution to these issues. It enables a new paradigm for secure, interoperable, and scalable healthcare systems, addressing not only technological inefficiencies but also policy, regulatory, and ethical challenges. This research explores the application of blockchain technology in healthcare, providing case studies and insights into its transformative potential for enhancing patient-centered care and data security.

DOI: 10.61137/ijsret.vol.10.issue5.279

A Comprehensive Web-Based Application for Digital Book-Keeping, Payment Process, and Secure Peer-to-Peer Transaction/strong>
Authors:-Assistant Professor Shivangi Sharma, Devansh Gautam, Sanjana Rajput, Ritik Ghosh, Purva Pardhi

Abstract-This paper presents the designing of a web-based financial application that implements digital bookkeeping and payment management features considering the needs of small and medium-sized businesses (SMBs). Identified financial management challenges for SMBs to track their expenses and debts as well as to make UPI-based payments are considered. The added feature of the app is the sound peer-to-peer payment with state-of-the art technologies including usage of biometric authentication, face recognition, and near-field communication. This will enable smooth financial transactions and, consequently, enhance security thereby reducing reliance on intermediaries and risk of frauds. It then delves into technical architecture, security features, and user experience, extending that with business implications of the application in a scenario of commission-based benefits. The thesis then discusses market potential and scalability of the app.

DOI: 10.61137/ijsret.vol.10.issue5.280

Impact of Short-Duration Rice Cultivation on Water Resource Management and Sustainability/strong>
Authors:-Nikam Jaiswal, Assistant Professor Dr. Gurshaminder Singh

Abstract-Water scarcity is rapidly becoming one of the most critical challenges facing global agriculture, particularly in regions that heavily depend on water-intensive crops such as rice. Traditional rice farming, which involves continuous flooding of paddy fields, consumes vast amounts of water, making rice cultivation unsustainable in many water-stressed regions. The need for innovative, water-efficient agricultural practices has led to the development and adoption of short-duration rice varieties, which offer a viable solution to reducing water use without compromising crop yield or food security. Short-duration rice varieties are characterized by their shorter growing periods, typically maturing within 90 to 110 days compared to conventional varieties that can take over 150 days. By requiring less time in the field, these varieties also demand significantly less water for irrigation, making them highly suitable for areas facing water scarcity, irregular rainfall, and unreliable irrigation infrastructure. In addition to reducing water consumption, short-duration rice contributes to the overall sustainability of farming systems by allowing for better synchronization with seasonal rains, enabling double or multiple cropping, and minimizing the need for groundwater extraction.

DOI: 10.61137/ijsret.vol.10.issue5.281

The Expanding Universe: Dark Matter Causing Moon to Drift Apart From Earth/strong>
Authors:-Yashwini Gaur

Abstract-In the late 1960s, through Lunar Laser Ranging Experiments, it was discovered that the Moon is drifting apart from the Earth at a constant rate. This new discovery has created a buzz among the scientists, with widely speculated reasons such as tidal forces, and Earth’s rotation rate. This rate of the Moon drifting apart has been relatively stable over years, with an average rate of 3.8 centimeters (1.5 inches) per year. This research paper explores the factors contributing to the Moon’s gradual drift away from Earth and introduces an additional potential reason for this phenomenon; where dark energy and matter comes into the picture and plays a role by expanding the distance between the 2 celestial objects. This paper will discuss in detail about the effect of dark energy on local systems like the solar system. To conclude, this paper will analyze the gradual drift of the Earth and the Moon because of dark energy and matter, discuss about its distribution in the universe, and predict its future impact on local systems and bodies in detail.

DOI: 10.61137/ijsret.vol.10.issue5.282
55

Radeon: An Innovative Malicious Discernment and Deterrance for Automaton Gadgets/strong>
Authors:-Venkatakrishnan Elangumaran

Abstract-Android clients are continually undermined by an expanding many malevolent (apps), conventionally called malicious. Malicious comprises a genuine danger to client security, cash, and gadget and record uprightness. In this work system note that, by concentrate their activities, system can arrange malicious into few social classes, every one of which plays out a constrained arrangement of mischievous activities that portray them. These mischievous activities can be characterized by checking highlights having a place with various Android levels. In this work, an innovative malicious location framework for Android gadgets whichever at the same time investigations the application by utilizing conduct models and keep an android application. This framework will be intended to take into records those practices attributes of pretty much every genuine malicious which can be found in nature. An epic host-based application which recognizes and adequately squares over 96% of noxious applications, which originate in distinction to three substantial data files with 3,000 applications, by abusing the collaboration of dual simultaneous classifiers conduct behavior-based locator. Broad investigations, likewise incorporates the examination of a tried of 9,800 authentic applications, have been led to demonstrate the not high negative caution rates, the insignificant execution overhead, restricted cordless utilization.

DOI: 10.61137/ijsret.vol.10.issue5.283
55

Efficient Ultra High Voltage Conversion Using Multistage Boost Technology/strong>
Authors:-Assistant Professor R. Alamelu, R. Sureshkumar, R. Prasanth, S. Sakthivel

Abstract-An innovative ultrahigh step-up dc-dc converter that integrates a dual-stage boost converter, a coupled inductor, and a multiplier cell. The dual-stage boost converter provides an initial voltage boost, while the coupled inductor enables efficient energy transfer and recycling of leakage energy. The multiplier cell further amplifies the output voltage. This configuration reduces voltage stress on power switches, decreases the size of passive components, and ensures continuous input current. With these features, the proposed converter offers enhanced performance, making it suitable for applications requiring high voltage conversion with minimal power losses. The simulation prototype steps up the input voltage using a 150-W prototype converter from 25 V to 550 V using MATLAB.

DOI: 10.61137/ijsret.vol.10.issue5.284
55

Classification of Online Toxic Comments Using Machine Learning Algorithms/strong>
Authors:-Professor Shubhangi Chatnale, Shivai P. Gore, Rutwik J. Shetty, Soham A. Mahajan

Abstract-The increasing prevalence of toxic comments on social media necessitates efficient automated systems for content moderation. This paper presents a machine learning-based approach to classifying toxic comments, aiming to detect harmful content such as hate speech, threats, and offensive language. We evaluate various supervised learning algorithms, including logistic regression, support vector machines (SVM), random forests, and advanced deep learning models such as recurrent neural networks (RNNs) and transformer-based models like BERT. Text preprocessing techniques like tokenization and feature extraction using TF-IDF and word embeddings are applied to optimize model performance. The models are trained on large labeled datasets and evaluated using accuracy, precision, recall, and F1-score. Our results show that deep learning models, particularly transformer-based architectures, achieve superior performance in identifying toxic comments, highlighting their effectiveness in supporting content moderation on social media platforms.

DOI: 10.61137/ijsret.vol.10.issue5.285
55

Impact of Advertisement on Consumer’s Buying Behaviour with References to FMCGs in Jabalpur City (M.P): Literature Review/strong>
Authors:-Research Scholar Arpan Kumar Samuel, Assistant Professor Dr. Sourabh Kumar Nougriaya

Abstract-The key objectives of advertisement are to raise awareness and promoting products. The objective of this Paper is to find out Impact of Advertisement on Consumer’s Buying Behaviour with References to FMCGs in Jabalpur City (M.P). By using 5 point Likert scale total of 430 persons agreed to participate and 400 responses were found satisfactory for further analysis. Questionnaires having 18 questions were distributed in Jabalpur (M.P.). Data was analyzed by using different statistical techniques such as Descriptive statistic, Factor Analysis, and Reliability analysis. Results of our study are robust because the evidence shows that advertisements have significant impact on consumers’ buying behavior and their choices. From the above discussion we have drawn the conclusion that advertisement can change the behavior of the consumer’s. Factors likewise Need of advertisement, Happiness of advertisement, Control of advertisement, Recall of Brand advertisement, and Feeling of advertisement. These are very helpful in creating and shifting the consumer’s buying behavior that is a very positive sign for the advertising and marketing companies.

55

Solar- Powered Water Purification System/strong>
Authors:-Prakalya E, Priyadharshini S M, Srilatha B

Abstract-The Solar-Powered Water Purification System provides a sustainable solution for remote areas lacking clean drinking water. Powered by solar energy, it uses advanced filtration technology to operate independently of traditional electricity sources. IoT sensors allow real-time monitoring and maintenance. Designed for portability and user-friendliness, the system is adaptable to various environments. Targeted at NGOs and rural communities, it offers a cost-effective way to improve water access, with significant health and quality of life benefits.

DOI: 10.61137/ijsret.vol.10.issue5.286
55

Advanced Skin Cancer Detection using Hybrid CNN Feature Extraction/strong>
Authors:-Mr. S. Sinimoxon Lee, Professor Arpita Das

Abstract-Skin cancer is one of the deadliest types of cancer, with a rapidly increasing incidence worldwide. Early detection is crucial to reducing the mortality rate. In this paper, we present an effective computer-aided diagnostic model for accurate skin cancer detection and classification. Our proposed system consists of three primary steps: a) Preprocessing, b) Feature extraction, and c) Classification. During preprocessing, image quality is enhanced through median filtering. In the feature extraction phase, features are extracted from three powerful pretrained CNN models—GoogleNet, AlexNet, and ResNet-101—using transfer learning and are then combined. In the classification stage, the hybrid features are classified using three successful Machine Learning (ML) classifiers: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). We validated our model on 3000 images from the MNIST dataset, achieving an accuracy of 96.66%, a precision of 96.5%, a recall of 96.66%, and an F1-score of 96.5%.

DOI: 10.61137/ijsret.vol.10.issue5.287
55

Risk Identification/strong>
Authors:-Sattam A Otaibi, Ahmed A AlSaleh, Mohammed H Aljaber, Dhawi A Alotaibi

Abstract-Identifying and managing risk processes are essential for achieving organizational success. This study investigates the significance of risk assessment, evidence differentiation, and control in safeguarding fundamental objectives and enhancing contemporary decision-making. Organizations can develop strategies to mitigate adverse effects on performance, financial stability, and resource allocation if they promptly recognize a potential risk. Utilizing global indicators with ISO 31000 and specialized frameworks such as RiskWatch and RiskLens, organizations can more effectively identify and monitor risks across several domains. Fundamental factors encourage the prompt detection of inadequacies, thereby preventing negative consequences, boosting efficiency, cutting down on resource wastage, and guaranteeing the prioritization of well-informed choices. Furthermore, the item provides insights from the Deloitte Global Impact Survey, which indicates that 61% of firms acknowledge that risk identification and management are significant factors in transformation success. The incorporation of opportunities into business strategies, as demonstrated by several studies, results in enhanced success rates and improved planning aligned with strategic objectives. The inquiry thoroughly examines essential brainstorming strategies, SWOT analysis metrics, requirements, and protocols while highlighting developmental areas that facilitate organizational change. In addition, research indicates that the integration of opportunities into change initiatives results in increased success rates and enhanced alignment with key objectives. The agency’s ability to efficiently organize and execute tasks at a large scale, utilizing appropriate tools and techniques, is essential to its effectiveness and long-term success.

55

Cloud kitchen Inventory System/strong>
Authors:-Assistant Professor Mr. Vishal Jaiswal, Ms. Bhakti Sarode, Mr. Dipesh Bobade, Ms. Nikita Chhapparghare, Ms. Shrutika Chauhan, Ms. Sneha Kolte

Abstract-Fast growth in cloud kitchens, driven by increased demand for food delivery services, is coupled with massive challenges to inventory management. Traditional inventory systems usually cannot meet dynamic requirements like those of cloud kitchens—fast-moving environments needing precise, real- time tracking of ingredients to ensure minimal wastage and resource optimization. This paper investigates how an IoT- enabled inventory management system can be implemented in a cloud kitchen setting. The system provides real-time observations of inventory levels, expiration dates, and storage conditions through the use of IoT technologies such as smart sensors, and other connected devices. It provides a holistic solution to inventory management problems within cloud kitchens since it allows for the automation of replenishment, demand prediction using data analytics, and compliance with food safety standards. The integrating technology will increase operational efficiency, generate cost savings, and sustain them by decreasing food wastage. This paper also discusses the possible challenges of IoT adoption related to data security and system integration, proposing strategies for successful implementation.

DOI: 10.61137/ijsret.vol.10.issue5.288
55

Digital Marketing Grow in India/strong>
Authors:-Assistant Professor Tanmoy Ghosh

Abstract-Digital Marketing grow in India has seen outstanding development as of late, determined by the quick expansion in web entrance, cell phone utilization, and the computerized change across different enterprises. With more than 700 million web clients, India is one of the biggest internet based showcases universally, making a fruitful ground for organizations to use computerized promoting techniques. The multiplication of virtual entertainment stages, web crawlers, web based business, and portable applications has reshaped purchaser conduct, making computerized channels fundamental for arriving at interest groups. Factors, for example, the reception of advanced installment frameworks, the ascent of neighborhood language content, and government drives like Computerized India have additionally energized this development. Little and medium endeavors (SMEs), as well as huge organizations, are progressively putting resources into Web optimization, virtual entertainment promoting, email showcasing, and powerhouse coordinated efforts to drive commitment and deals. Also, the accessibility of reasonable information plans and the ascent of video content, especially on stages like YouTube and Instagram, have opened new open doors for advertisers. As digital marketing keeps on developing with the coordination of man-made brainpower (artificial intelligence) and information examination, organizations in India are zeroing in on customized and information driven ways to deal with upgrade their showcasing endeavors. The fate of computerized showcasing in India guarantees development, development, and a critical effect on business achievement.

DOI: 10.61137/ijsret.vol.10.issue5.289
55

Application of Hybridized Model of Shunt and Series Facts Controllers for Improvement of Generator Oscillation Damping Stability of Electrical Power System/strong>
Authors:-Abass Balogun, Isaiah Gbadegeshin Adebayo

Abstract-One of the technical solutions for improving the stability of power system is incorporation of Static Synchronous Compensators (STATCOM) and Static Synchronous Series Compensator (SSSC) controllers. However, the impact of hybridized STATCOM and SSSC on the generator damping stability of the power system to improve the post disturbance recovery voltages of the generator is necessary. Thus, in this study, hybridized model of STATCOM and SSSC controllers were incorporated in the Nigerian 31-bus power system to improve the system generator damping stability during disturbance. Transient stability of electrical power system with contingency was performed using swing equations technique. Line-Voltage Stability Index (L-VSI) technique was employed to determine the critical load bus for the placement of the controllers. Hybridized model of the STATCOM and SSSC was developed and incorporated into the selected load buses and its impact on stability of the generator oscillation damping was examined. Simulation was done in MATLAB R2023a. The generator damping ratio, total active power losses and total cost of controllers were determined. Results verified the effectiveness of hybridized model of STATCOM and SSSC controllers in improving the stability of power generator oscillation damping.

DOI: 10.61137/ijsret.vol.10.issue5.290
55

Artificial Intelligence with Cloud Computing/strong>
Authors:-Mr. Ankit Pandey, Dr.Jasbir Kaur, Assistant Professor Mrs.Sandhya Thakkar

Abstract-Artificial Intelligence (AI) boasts the ability to perform tasks that typically require human intelligence. Ability to completely transform many sectors within the market, facilitating decision-making that is both more efficient and effective. Cloud Computing offers the infrastructure needed for the expansion of AI applications and work together without any problems. This offers a thorough examination of the methodologies and techniques, AI integration with Cloud Computing. It had been a long time since she had [1] last seen her childhood friend, but when they finally reunited, it felt as if no time had passed at all, explores different methods of artificial intelligence, different types of cloud computing structures, as well as techniques for combining different systems. Moreover, the paper explores instances of successful outcomes, research and practical applications of artificial intelligence in cloud computing along with the difficulties that come with it. The article ends by discussing upcoming plans, potential areas for future research in this field.

DOI: 10.61137/ijsret.vol.10.issue5.291
55

AI and the Arts: Can Machines Truly Create/strong>
Authors:-Aditya Dubey, Archana Raj, Manish Rai, MD Owais Alam, Raj Mandwal

Abstract-The following paper deals with the modern trend of AI regarding artworks that have so far been challenging for human creativity. It goes as far as finding an answer to the question of whether machines can be attributed to true creators from analyses on AI-generated works on art, music, and literature. It similarly raises questions about philosophical matters with regard to the authorship, originality, and the emotional level of works by machines. The paper seeks to describe a wide capability and limitation of a potential creative force that AI carries about by reviewing the processes that are technical behind AI-generated art as well as the response towards this creativity in the world of art.

DOI: 10.61137/ijsret.vol.10.issue5.292
55

Decentralized E-Voting System Using Blockchain Technology/strong>
Authors:-Professor Disha Nagpure, Jidnesh Shah, Abhay Sanap, Hanuman Keskar, Krushna Khairnar

Abstract-Elections are a cornerstone of modern democracies. However, concerns regarding trust and potential manipulation plague traditional voting systems. This paper explores the potential of decentralized e-voting systems powered by blockchain technology and Aadhaar OTP verification. By leveraging the immutability, transparency, and security of blockchain, combined with the robust authentication of Aadhaar OTP, this system aims to revolutionize the electoral process. It addresses key challenges of traditional methods, such as fraud and lack of trust, through the use of smart contracts, voter identity verification, and cryptographic techniques.

55

Reinforcement Learning in Autonomous Racing/strong>
Authors:-Mr. Mihir Pawaskar, Dr. Jasbir Kaur, Assistant Professor Ms. Sandhya Thakkar

Abstract-Reinforcement Learning (RL) is rapidly advancing as a key approach to training autonomous agents, particularly in complex, real-time environments such as autonomous racing. This review discusses the latest developments in RL applied to endurance and competitive racing, including telemetry data integration and the application of advanced deep reinforcement learning models. The paper explores the architecture and strategies behind “Formula RL,” a system designed to optimize vehicle performance on the racetrack through RL. We delve into how RL algorithms such as Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) are employed to enhance racing strategies, vehicle control, and decision-making, ultimately setting a course for the future of autonomous racing.

DOI: 10.61137/ijsret.vol.10.issue5.293
55

The Symbiotic Relationship: Ethernet and the Rise of 5G Networks/strong>
Authors:-Hrishikesh Bhatawadekar, Professor Dr. Shivani Budhkar

Abstract-The emergence of 5G promises a transformative era in wireless communication, boasting ultra-fast speeds, minimal delays, and the ability to connect a multitude of devices. However, this revolution rests upon a foundation often overlooked – Ethernet technology. This paper delves into the critical role Ethernet plays in the success of 5G networks. We explore how Ethernet’s established standards, exceptional reliability, and high bandwidth capabilities significantly contribute to the efficient functioning of 5G infrastructure. This analysis delves into the specific functionalities of Ethernet within the 5G Radio Access Network (RAN), particularly the potential of Ethernet Fronthaul for future deployments. Additionally, the paper examines the strengths and limitations of both technologies, highlighting the synergistic relationship that allows them to operate seamlessly together. Finally, we explore ongoing research regarding the convergence of Ethernet and 5G, emphasizing the potential for more efficient and secure future networks.

DOI: 10.61137/ijsret.vol.10.issue5.294
55

Work-life Balance Initiative and Employee Well-being/strong>
Authors:-Yamuna P, Raychal Phillips

Abstract-In today’s dynamic and demanding work environments, achieving a healthy work-life balance has become increasingly essential for employees’ well-being and organizational effectiveness. This paper investigates the impact of work-life balance initiatives on employee well-being and organizational outcomes, recognizing them as a strategic imperative for modern organizations. Drawing on a comprehensive review of existing literature, including theoretical frameworks and empirical studies, this research explores the relationship between work-life balance initiatives, employee well-being, and organizational performance. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather insights from employees across various industries. Preliminary findings suggest that effective work-life balance initiatives not only contribute to enhanced employee well-being, including reduced stress levels and increased job satisfaction, but also yield positive outcomes for organizations, such as improved productivity, retention, and overall employee engagement. The implications of these findings for HR practitioners and organizational leaders are discussed, emphasizing the importance of prioritizing work-life balance initiatives as a strategic investment in human capital. By fostering a culture that values work-life balance and supports employees’ well-being, organizations can create healthier, more productive work environments conducive to long-term success and sustainability.

DOI: 10.61137/ijsret.vol.10.issue5.295
55

Financely: Personal Finance Tracker Revolutionizing your Financial Journey/strong>
Authors:-Megha Suvarna, Shruti Rajak, Pranjali Gupta, Bhoomi Saini

Abstract-This project aims to develop a comprehensive personal finance tracker to help individuals manage their expenses and savings efficiently. The tracker was developed using [specific technologies], incorporating features such as budget categorization, expense logging, and financial goal setting. User feedback indicated a 20% improvement in their ability to stay within budgets. This project provides a valuable tool for personal finance management and suggests avenues for future enhancements, such as integration with banking APIs for automated transaction tracking. A personal finance tracker investigates how tools designed to manage personal finances—such as apps, software, and online platforms—affect users’ financial habits and literacy. The study typically examines the features of these trackers, such as budgeting and expense tracking, and assesses their effectiveness in improving users’ financial awareness and decision-making. It often involves analyzing user data and feedback to understand how these tools help people manage their money better, identify any challenges they face, and suggest improvements for enhancing their impact. The ultimate goal is to determine how personal finance trackers contribute to better financial management and overall financial health. This research paper examines the impact of personal finance trackers (PFTs) on financial literacy and management. Personal finance trackers, including mobile apps, desktop software, and web-based tools, are designed to help individuals monitor their spending, budget effectively, and improve their financial decision-making. Through a combination of quantitative and qualitative methods, this study evaluates user engagement, financial behavior changes, and the overall effectiveness of these tools. The quantitative analysis involves surveys and usage data from personal finance tracker users, revealing increased financial awareness, better budgeting practices, and improved savings rates. The qualitative analysis includes user interviews, highlighting experiences and challenges related to data integration, privacy concerns, and tool usability.

DOI: 10.61137/ijsret.vol.10.issue5.296
55

House Price Prediction Models with Noise-Injected Data Using Machine Learning/strong>
Authors:-S.Shanmathi, V.Rajeswari, V.Chaitanya, T.Navya, P.Vasudeva Rao

Abstract-Using machine learning techniques, notably linear regression, the project “House Price Prediction Models with Noise-Injected Data Using Machine Learning” aims to improve house price predictions. Data collection, preprocessing, and the incorporation of environmental elements like noise levels into the model are among the goals of the study. The study’s data base consists of publicly available datasets from real estate sources and websites like Kaggle. To create a reliable prediction model, the methodology uses an organized procedure that includes data collection, preprocessing, feature engineering, exploratory analysis, model selection, and comparison analysis. Accurately predicting house prices is achieved by the use of linear regression, and the model’s performance is assessed using metrics such as Mean Squared Error (MSE) and R-squared (R²). The findings show that important variables like housing size, location, and noise levels have a big impact on the forecasts. High R-squared values and a low Mean Squared Error confirm the model’s good predictive ability and validate that it is a reliable tool for projecting property prices.

55

Vision Parking Model/strong>
Authors:-A. Mugdha, Harsh Jaiswal

Abstract-Parking was one of the first issues that emerged after the invention of the vehicle. Technology has made progress in solving this issue throughout time, but parking is still a challenge. The primary cause is that parking issues are a collection of issues rather than a single one. By training a model to guide us on the gate entry where we have to park our vehicle according to the available space in parking and saving people’s time, we can use AI technology to provide you with a solution that will make the parking system more convenient and easy for people. One such, task is to determine the occupancy of parking spaces in a decentralized parking ecosystem. In a decentralized system, users find their preferred parking space, not random parking spaces. In this post, we offer a web application, as a solution for detecting parking spaces in various parking spaces. The solution is based on computer vision. As we know Python is an emerging it is that the only but this will language, so it becomes easy to write a script for Traffic in Python. The instructions for it is that the only but this will, analysis can be it is that the only but this will handled as per the requirement of the user. Data analysis is the, process of converting data into information. This is commonly used in removing barrier like advertisement, fetching files etc. In Python there is an API it is that the only but this will called traffic, which allows us to convert data into text. In the current scenario, advancement in technologies is such that they, can perform any task with same effectiveness or can say more it is that the only but this will effectively than us.

DOI: 10.61137/ijsret.vol.10.issue5.297
55

Molecular dynamics Simulation of the Turnbull Criterion for Predicting the Glass Forming Ability (GFA) in the Binary Fe100-XZrX Metallic Alloy/strong>
Authors:-Anik Shrivastava

Abstract-In order to better understand the glass forming ability, we have evaluated the reduced glass transition temperature (Trg) as one of the potential factors in molecular dynamics simulations of the binary Fe100-XZrX (X=10,12) system. Our investigation indicates that the calculated Trg values for Fe88Zr12 and Fe90Zr10 are 0.537 and 0.535, respectively, which are close to the minimum requisite T_rg≅0.4 the Turnbull criteria for glass formation in alloys.

DOI: 10.61137/ijsret.vol.10.issue5.298
55

Enhancing Cardiovascular Disease Prediction with XAI Technique Using Machine Learning/strong>
Authors:-Assistant Professor Dr.N.Chandrasekhar, P. Sravani, V.Charishma, N.Padmavathi, SK. Abdul Khadar, S.Rajeswari

Abstract-Globally, coronary diseases (CV) are several of the most significant causes of demise, improvements in predictive healthcare technologies are imperative. The goal of this study is to improve the predictability and interpretability of cardiovascular disease prediction models by combining machine learning methods with Explainable Artificial Intelligence (XAI). To create reliable predictive models, we investigate a range of machine learning algorithms, such as ensemble approaches, logistic regression, and XG-Boost. But while though precision is crucial, these predictions’ interpretability is just as crucial for therapeutic use. Our goal is to make model procedures for making decisions concise and intelligible for physicians by utilising XAI techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). Using a real-world CVD dataset, our tests demonstrate that XAI-enhanced models do not not only increase the accuracy of predictions but also identify important variables affecting heart function. By providing a workable framework for using interpretable machine learning models in healthcare, this study advances the discipline and may result in better clinical judgements and more individualised patient care.The accuracy of the Random forest-CARDIO system is assessed against the Framingham heart disease dataset using the Colab Simulator. In the experiment, Random forest demonstrated a significant accuracy score of 91.38%, which is appreciably better than alternative techniques including, XGBoost (90.01%), RNN (85.02%), GRU (85.02%) and RNN+GRU (as a combined model) (86%).

DOI: 10.61137/ijsret.vol.10.issue5.299
55

Build Your Own SOC Lab/strong>
Authors:-Monika Sahu, Kanakmedala Kashish, Assistant Professor Neelam Sharma, Dr. Siddhartha Choubey

Abstract-The “Build your SOC Lab” project is designed to address the pressing need for robust cybersecurity measures in today’s digital landscape. It provides a comprehensive guide tailored to organizations and individuals seeking practical resources in digital security. Emphasizing cost-effectiveness, adaptability, and scalability, it offers detailed instructions for setting up a functional SOC lab. Covering essential components like hardware, software tools, and network infrastructure, the project ensures thorough preparation for cybersecurity challenges. It delves into various use cases, including threat detection, incident response, and security monitoring, facilitating hands-on learning in SOC operations. By enhancing stakeholders’ capabilities in safeguarding digital assets and mitigating cyber threats, the project contributes to the resilience and security of modern digital ecosystems. Through practical insights and methodologies, it empowers individuals and organizations to navigate the evolving cybersecurity landscape effectively.

DOI: 10.61137/ijsret.vol.10.issue5.300
55

Organic Farming and Climate Change Mitigation/strong>
Authors:-Rohan Raju Thomas, Dr Gurshaminder Singh

Abstract-Organic farming has gained significant attention as a sustainable agricultural practice with potential benefits for climate change mitigation. This paper presents a comprehensive review of the literature on the role of organic farming in mitigating climate change. The review examines various aspects such as carbon sequestration, reduced greenhouse gas emissions, soil health improvement, biodiversity conservation, and resilience to climate variability. The findings highlight the potential of organic farming practices to contribute positively to climate change mitigation efforts. Key challenges and future research directions in this field are also discussed. The analysis draws upon a range of studies and scholarly articles to support the assertions made regarding the positive role of organic farming in climate change mitigation. Additionally, challenges and future prospects in this field are explored, emphasizing the need for further research and policy support to harness the full potential of organic farming for sustainable agriculture and climate resilience. Organic farming has gained prominence as an environmentally friendly agricultural approach with the potential to mitigate climate change impacts. This paper presents a synthesized overview of the contributions of organic farming practices to climate change mitigation. Climate change is one of the most pressing issues facing the world today, and agriculture is a significant contributor to greenhouse gas emissions. Organic farming has gained popularity as a more sustainable alternative to conventional farming practices, but what impact does it have on mitigating climate change? This essay will explore the impact of organic farming on climate change mitigation, the effectiveness of organic farming in mitigating climate change, and the challenges and limitations of organic farming in mitigating climate change.

DOI: 10.61137/ijsret.vol.10.issue5.301
55

Review on Multi-Objective Optimization in Highway Pavement Maintenance and Rehabilitation Project Selection and Scheduling/strong>
Authors:-Sandip Sampat More, Assistant Professor Shashikant B.Dhobale

Abstract-This study review an efficient asset management framework that enables decision makers to prioritize the maintenance of their roads, while focusing on the most critical road segments. In particular, this study first extends the application of reliability theory to estimate the overall network condition. Following that, this study proposes a new consequence of failure function for the whole road network based on road segments’ reliability, age, and road agency preferences. Finally, the study proposes an efficient multi-objective optimization algorithm, with the goal of maximizing overall network performance with the least maintenance and computational cost. The suggested framework was applied to three main roads in Jordan and validated statistically by comparing its performance to that of a typical multi-objective genetic algorithm (GA) under various scenarios and utilizing multiple performance metrics.

55

Revolutionizing Gratitude Humanizing Tipping Culture and Empowering Unseen Contributors through Digital Recognition/strong>
Authors:-Tania, Professor Vanita Rani

Abstract-Tipping culture, a long-standing custom in many service sectors, has changed dramatically as digital platforms and technology have grown in popularity. The core of thankfulness, though, which is to recognize and empower the invisible contributors who work behind the scenes, is still mostly ignored. Using digital recognition, this article investigates the idea of “humanizing” tipping, emphasizing how digital platforms might transform the distribution and expression of gratitude. Blockchain, mobile apps, and peer-to-peer recognition are examples of technical advancements that service providers can use to make sure that frontline and background workers receive just recognition and compensation. In addition to increasing tipping’s monetary worth, this digital revolution fosters an inclusive and appreciative culture. The study highlights the potential socio-economic effects, psychological advantages, and ethical ramifications of strengthening frequently disregarded contributions through a more open and equal tipping ecology.

DOI: 10.61137/ijsret.vol.10.issue5.302

A Study on Consumer Attitudes towards Organic Skincare Products among Young Adults in Urban Areas/strong>
Authors:-Smeet Raut

Abstract-This study aims to explore consumer attitudes towards organic skincare products, focusing specifically on young adults residing in urban areas. The growing demand for organic products has transformed the skincare industry, with consumers increasingly seeking products that align with their values of health, sustainability, and ethical consumption. This research investigates the motivations, preferences, and purchasing behaviours of young urban consumers, examining how factors such as environmental concerns, health consciousness, and brand perception influence their choices in skincare products. Utilizing a mixed-methods approach, the study employs quantitative surveys and qualitative interviews to gather comprehensive data on consumer attitudes. The survey targets a diverse sample of young adults aged 18 to 35, encompassing various demographics and lifestyles within urban settings. The qualitative component further enriches the findings by providing deeper insights into the underlying motivations behind consumers’ preferences for organic skincare products. Preliminary findings indicate that young adults are significantly influenced by the perceived benefits of organic ingredients, such as their natural composition and lower environmental impact. Additionally, social media and peer recommendations play a crucial role in shaping their purchasing decisions. The study highlights the importance of transparency in marketing and the need for brands to effectively communicate the benefits of organic skincare products to engage this demographic. By understanding the attitudes and behaviours of young consumers towards organic skincare, this research aims to provide valuable insights for marketers and industry stakeholders, ultimately contributing to more effective strategies in the rapidly evolving skincare market. The findings will also pave the way for future research exploring the broader implications of consumer attitudes on the organic product industry as a whole.

DOI: 10.61137/ijsret.vol.10.issue5.304

An Overview of Deep Learning Techniques for Enhanced Violence Detection in Surveillance Systems/strong>
Authors:-M. Tech Scholar Dhirendra Tripathi, HoD Nagendra Patel

Abstract-This paper provides an overview of deep learning techniques aimed at enhancing violence detection in surveillance systems. As surveillance technologies advance, identifying violent activities accurately becomes critical to maintaining public safety. Traditional approaches often struggle with the complexity of video data, which includes both spatial and temporal patterns. To address this, modern deep learning methods like Convolutional Neural Networks (CNNs), InceptionV3, Long Short-Term Memory (LSTM) networks, and hybrid models have been employed to improve detection accuracy. These models effectively capture spatial features while also learning temporal dependencies, making them ideal for real-time violence detection. The review highlights preprocessing steps such as noise reduction, feature extraction, and data augmentation, which contribute to better model performance. It also examines challenges like data imbalance, scalability, and computational demands in deploying these models.

Exploring Friend Recommendation Algorithms in Social Networking Sites/strong>
Authors:-M. Tech Scholar Vipin Kumar Singh, HoD Nagendra Patel

Abstract-Friend suggestion is a highly popular feature in social networking platforms, designed to connect users with similar or familiar individuals. This concept, rooted in social networks like Twitter and Facebook, often utilizes a “friends-of-friends” approach, where users are introduced to connections through their existing social circles. Traditionally, users tend to connect not with random individuals but rather with acquaintances of their friends. However, existing friend recommendation methods have limitations in scope and efficiency. To address these limitations, we propose an enhanced buddy recommendation model. Our approach leverages collaborative filtering to improve accuracy by analyzing users’ similarities and differences based on their interests, activities, and preferences. Additionally, location-based friend recommendations have become increasingly popular as they bridge the gap between the physical and digital worlds, offering insights into users’ preferences and interests. This model will expand the range of recommendations, connecting users with others who share similar interests and reside in similar areas.

Advanced Multi Model RAG Application/strong>
Authors:-Professor Disha Nagpure, Sujal Pore, Shardul Deshmukh, Aditya Suryawanshi

Abstract-This paper presents a modular, context-aware multimodal Retrieval-Augmented Generation (RAG) application that leverages both chain-based and agentic execution strategies. Powered by Gemini 1.5 Flash as the core language model, the system integrates Langchain and Langsmith frameworks to enable dynamic document retrieval, task orchestration, and seamless handling of multiple data sources. Key features include a YouTube summarizer using transcript APIs, real-time web search via the Tavily search tool, and support for text, image, and audio inputs, with OpenAI’s Whisper model for speech-to-text conversion. The application’s contextual awareness is enhanced by chat memory fallback functions, ensuring continuous, coherent interaction across sessions. Additionally, vector databases are employed for efficient multimodal retrieval. This system represents a significant advancement in RAG applications, offering flexibility, scalability, and adaptability across various input modalities and real-time tasks.

DOI: 10.61137/ijsret.vol.10.issue5.305

Vehicle-Focused Traffic Mapping for Forecasting Urban Movement and Detecting Peak Congestion Periods/strong>
Authors:-Atharva Daga, Aditya Wandhekar

Abstract-Effectively managing urban traffic dynamics is essential for optimized city planning and administration. This research focuses on a vehicle-centric approach to traffic mapping, aiming to predict congestion levels and identify peak traffic times within urban areas. The main objective is to forecast daily traffic density and detect periods of high congestion to support improved traffic management. To achieve this, we analysed real-time CCTV footage from Nasik Smart City Office, collected from key routes—Pathardi Gaon Circle and Golf Club Ground Circle — over a continuous five-day span. The findings confirm that real-time CCTV data delivers accurate congestion predictions and enhances traffic control strategies. By applying this methodology, we provide a reliable solution for traffic authorities, enabling them to take proactive measures to mitigate traffic congestion and improve overall traffic flow. This research contributes to the advancement of intelligent transportation systems, highlighting the value of incorporating real-time data into urban traffic management solutions.

DOI: 10.61137/ijsret.vol.10.issue5.306

A Short Review on Botany, Phytochemistry and Medicinal Potential of Christ’s Thorn Jujube/strong>
Authors:-Ruwa Talib Arffa, Sivamani Selvaraju

Abstract-Ziziphus spina-christi, commonly known as Christ’s thorn jujube, is a hardy deciduous shrub native to arid and semi-arid regions of Africa and the Middle East. This species is characterized by its thorny branches, small, yellow-green flowers, and edible drupes. Z. spina-christi is of considerable ecological and economic importance; it plays a vital role in soil stabilization and desert reclamation due to its deep root system. Additionally, the plant has various traditional uses, including medicinal applications, as a source of fodder, and for its wood, which is valued for its durability. Recent studies have highlighted its potential in sustainable agriculture and agroforestry, particularly in drought-prone areas. The present review highlights the botanical characteristics, ecological significance, traditional uses, and potential applications of Z. spina-christi , underscoring its value in both cultural practices and environmental conservation.

DOI: 10.61137/ijsret.vol.10.issue5.307

Park Ments: A Revolutionary Parking Application for the Modern City/strong>
Authors:-Nikhil A. Patil, Utkarsha A. Salunkhe, Deepika S. Patil, Pooja S. Wagh, Professor Disha Nagpure

Abstract-Challenge due to limited spaces, high demand, and the difficulty of finding available spots. Park Ments is a cutting-edge mobile application designed to revolutionize parking in urban areas by providing real-time information on parking availability. Park Ments is a mobile application that provides real-time information on parking availability in cities, allowing drivers to find a parking spot quickly and easily. This application uses intelligence probability for finding a perfect parking spot which makes it easy to find a perfect parking spot. This parking spot sorted with the help of distance between the user and parking spot, price and it delivers accurate, up-to-date information to users. Park Ments predicts parking availability based on historical data and real-time traffic patterns, enabling drivers to plan their parking in advance, reducing time and stress. It offers features such as advance reservation, remote payment, and directions to parking spots, enhancing user convenience. For cities and parking operators, Park Ments helps reduce traffic congestion and optimize parking space usage. The user-friendly app will be available for both iOS and Android devices, free to download from the App Store and Google Play, with various pricing options including hourly, daily, and monthly passes. By transforming parking into a more efficient and convenient process, Park Ments aims to significantly improve urban parking experiences.

DOI: 10.61137/ijsret.vol.10.issue5.309

Fake Profile Identification and Classification Using Machine Learning/strong>
Authors:-Professor Disha Nagpure (HOD), Professor Shilpa Shide (Guide) Vaishnavi Gaikwad, Vaishnavi Panchal, Vikrant Kothimbire, Vinay Makwana

Abstract-This paper details the design and implementation of Social media platforms are essential for communication today, allowing people to connect, share, and interact. However, the rise of fake profiles on sites like Instagram creates significant challenges related to user privacy, security, and trust. This research proposes a new approach to identify and classify these fake profiles using machine learning techniques. The findings contribute to ongoing efforts to combat fake accounts, promoting a safer and more trustworthy online environment. By leveraging machine learning and a thorough set of features, the model shows promising results in detecting and categorizing fake profiles. This research also opens up opportunities for further exploration, such as integrating different data sources and adapting the model for use on other social media platforms.

DOI: 10.61137/ijsret.vol.10.issue5.310

Social Media Insights
Authors:-Professor Disha Nagpure (HOD), Professor Shilpa Shide (Guide) Vaishnavi Gaikwad, Vaishnavi Panchal, Vikrant Kothimbire, Vinay Makwana

Abstract-This paper details the design and implementation of Social media platforms are essential for communication today, allowing people to connect, share, and interact. However, the rise of fake profiles on sites like Instagram creates significant challenges related to user privacy, security, and trust. This research proposes a new approach to identify and classify these fake profiles using machine learning techniques. The findings contribute to ongoing efforts to combat fake accounts, promoting a safer and more trustworthy online environment. By leveraging machine learning and a thorough set of features, the model shows promising results in detecting and categorizing fake profiles. This research also opens up opportunities for further exploration, such as integrating different data sources and adapting the model for use on other social media platforms.

DOI: 10.61137/ijsret.vol.10.issue5.310

Social Media Insights
Authors:-Saniya M. Kadmude, Shrutika D. Bansode, Vedant S. Joge, Professor Prachi Tamhan

Abstract-This research presents a comprehensive sentiment analysis system tailored for social media comments, aiming to classify user sentiments into positive, negative, or neutral categories. With Social media’s vast user engagement—over 1 billion unique users generating extensive comment data—there exists a significant opportunity to derive insights into public opinions. This study addresses challenges inherent in analyzing social media comments, including the high volume of data, diverse linguistic expressions, the use of slang, emojis, sarcasm, and the presence of spam. We leverage a constructed annotated corpus comprising 1500 citation sentences, which underwent rigorous data normalization to enhance quality and consistency. Six machine learning algorithms— Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, Decision Tree, K-Nearest Neighbor (KNN), and Random Forest (RF)—were implemented for sentiment classification. The performance of these algorithms was evaluated using various metrics, including F-score and accuracy, demonstrating a correlation between sentiment trends and real-world events associated with specific keywords. This work contributes to the field of sentiment analysis by providing insights that can aid researchers in identifying quality research papers and understanding user attitudes towards video content.

DOI: 10.61137/ijsret.vol.10.issue5.311

A Study of the Behavioural Biases in Investment Decision-Making in Mumbai
Authors:-Urzin Pardiwalla

Abstract-This study examines how behavioural biases influence investment decision-making, challenging the rational assumptions of traditional finance theories. Investors often deviate from rationality due to cognitive biases, leading to suboptimal decisions. Key biases such as overconfidence, anchoring, herd behaviour, and loss aversion shape investment choices, potentially impacting portfolio performance and market trends. By analyzing these biases, this research sheds light on their psychological foundations and the importance of awareness in mitigating their effects. Understanding these biases helps investors and financial professionals improve decision-making processes, contributing to more informed and resilient investment strategies.

Easy Trade: Forex Trading bot Using Artificial Intelligence
Authors:-Professor Alim Khan, Rudransh Sharma, Abhinav Shukla, Kshitij Khare, Shivam Shukla

Abstract-The foreign exchange (forex) market, with its high liquidity and 24/5 trading hours, presents significant opportunities for investors. This paper discusses the development of a forex trading AI bot by a group of four college students, leveraging Python for programming and various analytical sources for strategy formulation. The project aims to create an automated trading system that utilizes machine learning algorithms and technical indicators to make informed trading decisions.

DOI: 10.61137/ijsret.vol.10.issue5.312

College Admisssion Enquiry Chatbot Using Machine Learning
Authors:-Professor Disha Nagpure, Akanksha S. Chavan, Harshali R. Salunkhe, Aryan S. Rathod, Kirankumar G. Reddy

Abstract-In recent years, there has been a significant increase in the volume of inquiries received by college admission offices, creating challenges in managing and responding to these queries efficiently. Traditional methods of handling such inquiries are time-consuming and often fail to meet the expectations of prospective students, leading to dissatisfaction and missed opportunities. This paper presents the development of an intelligent college admission inquiry chatbot, leveraging machine learning and natural language processing (NLP) techniques to automate and streamline the query resolution process. The proposed solution utilizes NLP models to classify user intents and recognize relevant entities from student queries. The chatbot is trained on a dataset comprising frequently asked questions (FAQs) and admission-related information, allowing it to provide accurate, real-time responses to inquiries regarding courses, application deadlines, eligibility criteria, and more. Key machine learning algorithms, including deep learning techniques for intent classification and rule- based systems for response generation, form the backbone of the system. The main findings indicate that the chatbot achieves a high accuracy rate in intent detection, with an F1-score of 92% and a significant reduction in response time compared to manual systems. User satisfaction surveys also revealed an improvement in the overall experience, particularly in terms of accessibility and response accuracy. In conclusion, the chatbot demonstrates the potential to enhance the efficiency and quality of admission inquiry handling in educational institutions, offering a scalable and cost-effective solution. Future improvements could focus on expanding the chatbot’s language capabilities and improving its ability to handle more complex, multi-part queries.

Student Voting Election Portal
Authors:-Professor Swati Shinde, Vaishnavi Borse, Resham Umale, Shraddha Borate

Abstract-With advancements in technology, traditional voting methods are evolving, offering more advanced solutions like online voting portals. A Student Voting Election Portal provides a modern and secure way for students to participate in elections from any location with internet access, eliminating the need for physical polling stations. This online system offers several benefits, such as improved accessibility, time and resource efficiency, greater accuracy, and transparency, making the voting process more democratic. Critical to the success of such a platform are proper voter verification and the accurate management of student information. While online voting systems have been implemented successfully in various contexts, there are still challenges and limitations to overcome for widespread adoption. This paper will explore different types of electronic voting, examine successful implementations of student election portals, and compare them to traditional voting methods, highlighting current trends and potential future developments.

DOI: 10.61137/ijsret.vol.10.issue5.313

A Cost-Benefit Analysis of Material Handling on the Productivity of Food and Beverage Manufacturing Industries
Authors:-Ms. Krupa Shetty

Abstract-Food and Beverage manufacturing companies face challenges today due to their high competitiveness, poor working conditions, and more stringent regulations. Growing demand for high quality products and frequent changes in the variety of products by the consumers had an impact on the viability of the food and beverages manufacturing sector. Working conditions for many food and beverages operatives are difficult, as it requires large number of labours for handling. In the present scenario, the production cost increases due to the handling of material from one place to another inside the factory by using the unskilled labour. Due to shortage of labour majority of the manufacturing industries are facing problem and there is a drastic reduction in total output and not achieving the required target is a common weakness In most of the small scale food and beverage manufacturing companies manual handling is adopted to transfer the raw material from one place to other, transfer of semi- finished material from one equipment to other and finally transfer the final finished products to the packing section and storage division. In all these stages movement of material takes place with help of semi-skilled workers. Because of this required quality is not achieved. Finally cost of the product increases, which they are not in a position to match the competitive market. One of the major reasons for slow growth of the Indian Economy is the improper handling of materials and unnecessary costs incurred. This research paper focuses on the benefits of utilising the material handling system with properly planed plant layout and automation, there is a drastic reduction labour cost and it avoids the damage caused by manual movement of material, which results in better- quality product with less cost of production. Good handling system also improve inventory control, less fatigue of workers, greater industrial safety with less accident potential and disruption of work, improved morale of workers.

DOI: 10.61137/ijsret.vol.10.issue5.314
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ECG Signal Classification Using Fine-Tuned MobileNetV2 for Cardiovascular Disease Detection/strong>
Authors:-Assistant Professor Nadikatla Chandrasekhar, Chennapragada Tarun, Gorle vassudeva rao, Burada Jeevan

Abstract-Cardiovascular disease, otherwise referred to as heart disease, represents one of the most common and fatal illnesses that entails injuring the heart as well as the blood vessels. These, in turn, can cause a range of complications such as myocardial ischemia, for instance, coronary artery disease, or heart failure. The appropriate and timely identification of heart conditions. Clinical practice is determined by the relevance of the illness. The sickness known as heart disease, also known as cardiovascular disease, is common and, sadly, harmful. This condition deals with the morbidity and mortality associated with the heart and blood vessels. This can cause numerous issues such as myocardial ischemia, coronary heart problems and heart failure. A timely and correct identification of heart ailments. clinical practice is guided by the relevance of the disease. Being able to identify those at risk allows for preventative measures, preventative actions, and individualized treatment plans to lessen the negative effects and slow the disease’s course. The identification of cardiac disease has seen significant growth in recent years. major improvements as a result of the incorporation of the complex. Technology and methods based on computation. Among them is the machine. predictive modeling, data mining methods, and learning algorithms frameworks that make extensive use of physiological and clinical data information.

DOI: 10.61137/ijsret.vol.10.issue5.315
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Space Debris Tracking and Prediction Models/strong>
Authors:-Sakshi Khedekar, Jayesh Jadhav, Jiya Mokalkar, Pratik Patil, Professor Manisha Mali

Abstract-In a growing risk for space activities intentionally located or accidentally resulting from the creation of space debris, monitoring and forecasting are indispensable for the protection of both crewed and uncrewed space missions. The paper presents the assessment of eight most widespread space debris tracking and prediction models: TLE based SGP4, ORDEM, MASTER, Debrisat, SDebrisNet, SDTS, CARA, SSN. For every model, a multi-faceted approach with respect to its various characteristics, accuracy, complexity, data requirement, adaptability, reliability, and usability is employed. This appraisal provides the benefits and associated drawbacks of each methodology in tackling the major issues of data, computation and construction of the complete system. The research further considers the progress of tracking devices and existing systems as well as possibilities of their improvement for the realtime challenges. The comparative assessment of the models presented in this paper will help to strategically improve current approaches to space debris control instruments, thus supporting safety and long-term operating trends in outer space. This study has been carried out in order to devise strategies that will fit the growing and dynamic endeavors of exploring space, by tracking debris with the utmost efficiency and precision.

DOI: 10.61137/ijsret.vol.10.issue5.316
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Heart Disease Prediction Using Machine Learning Techniques in Python: A Review/strong>
Authors:-Tanmay Deshmukh, Supriya Kharatmol, Professor Nishant Patil

Abstract-As the global incidence of heart disease escalates daily, there is an urgent imperative to accurately predict and diagnose these conditions efficiently. Heart illness, also referred to as cardiovascular disease, is a broad category of conditions that affect the heart, including congenital abnormalities, vascular problems, and cardiac arrhythmias. In recent decades, it has emerged as one of the world’s top causes of death. Thus, it is imperative to create accurate and trustworthy techniques for early disease detection .Heart illness, also referred to as cardiovascular disease, is a broad category of conditions that affect the heart, including congenital abnormalities, vascular problems, and cardiac arrhythmias

DOI: 10.61137/ijsret.vol.10.issue5.317
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The End of LIBOR: A Comprehensive Analysis of Financial Reforms and Market Adaptations/strong>
Authors:-Sagnik Kar Roy

Abstract-The London Interbank Offered Rate (LIBOR), a cornerstone of global finance used to set interest rates across a wide range of financial products, is undergoing a major transition due to issues of transparency and susceptibility to manipulation revealed in the 2012 LIBOR scandal. This report examines LIBOR’s historical role, its critical influence on financial markets, and the extensive regulatory reforms following the scandal, which have prompted a shift toward transaction-based alternative reference rates (ARRs) like SOFR and SONIA. The transition to ARRs presents significant challenges for financial institutions, requiring adjustments in valuation, risk management, and contract structures. Additionally, the report explores how technological innovations, such as real-time data processing and blockchain, could further enhance the reliability of benchmarks, pointing toward a future financial landscape grounded in transparent and stable interest rate standards.

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Dietary Interventions for Speech Delay and Hyperactivity in a Child with Machine Learning and AI Applications/strong>
Authors:-Sujatha Mudadla

Abstract-This study investigates the role of specific dietary changes in addressing speech delay and hyperactivity symptoms in my son. Recognizing nutrition and maternal health as influential factors in child development, I explored how targeted dietary adjustments might enhance speech clarity, attention, concentration, and behavior. The study also explores maternal influences, including anemia and stress during conception, and their potential impacts on gut health and speech development. Additionally, I examined the effectiveness of repeated oral teaching methods, such as memorizing rhymes and vocabulary, for reinforcing neural pathways. To extend the research, I explore how machine learning (ML), deep learning (DL), computer vision, and generative AI can be applied to monitor, predict, and enhance the intervention’s effectiveness.

DOI: 10.61137/ijsret.vol.10.issue5.318
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Energy Theft Detection in Smart Grids Using Graph Neural Networks (GNNs)/strong>
Authors:-Assistant Professor Dr. Pankaj Malik, Himisha Gupta, Anoushka Anand, Siddhesh Bhatt, Devansh Gupta

Abstract-Energy theft poses significant challenges to smart grid operations, leading to substantial financial losses and grid instability. Traditional machine learning approaches often fall short in detecting energy theft due to the complex and interconnected nature of smart grid systems. This paper proposes a novel approach to energy theft detection using Graph Neural Networks (GNNs), leveraging the inherent graph structure of smart grids. By representing the grid as a graph, where nodes correspond to smart meters and transformers, and edges represent electrical connections, GNNs capture both the local consumption patterns and the relationships between grid components. The proposed model aggregates node and edge features to identify anomalous consumption behaviors indicative of energy theft. We apply both Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to enhance detection accuracy by considering both the structural and consumption-related features of the grid. The model is trained and evaluated on real-world and simulated smart grid datasets, showing improved performance over traditional classification models such as support vector machines and random forests. Evaluation metrics including precision, recall, and F1-score demonstrate the model’s robustness, even in the presence of noisy data and imbalanced class distributions. This research highlights the potential of GNNs to enhance energy theft detection in smart grids, providing a scalable and interpretable solution that can adapt to evolving grid conditions. Future work includes expanding the model to incorporate temporal data for real-time detection and exploring reinforcement learning for adaptive theft prevention strategies.

DOI: 10.61137/ijsret.vol.10.issue5.319
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News Recommendation System/strong>
Authors:-Professor Disha Nagpure, Furquan M. Khan, Roshan A. Yadav, Sahil V.Prasad

Abstract-News recommendation systems have become integral to the digital media ecosystem, helping users navigate the overwhelming volume of news content generated daily. These systems employ a variety of algorithms to personalize news feeds, enhancing user engagement and satisfaction by tailoring content based on individual preferences, behavior, and demographic profiles. The underlying techniques include collaborative filtering, content-based filtering, and hybrid methods that combine multiple approaches. In recent years, the adoption of deep learning and natural language processing (NLP) has further advanced the accuracy and relevance of recommendations by enabling more sophisticated understanding of news articles and user interactions. However, challenges such as bias in recommendations, filter bubbles, and the trade-off between personalization and content diversity remain significant concerns. Additionally, ensuring transparency, fairness, and privacy in recommendation algorithms is a growing area of focus. This abstract provides an overview of the key technologies, challenges, and future directions in news recommendation systems, with an emphasis on improving the user experience while addressing ethical and societal implications.

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Adoption of Artificial Intelligence: Benefits, Challenges, and Future Prospects/strong>
Authors:-Malvika Singh

Abstract-Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, reshaping industries, driving operational efficiencies, and fostering innovation. The adoption of AI spans numerous sectors, such as healthcare, finance, retail, and manufacturing, where it is optimizing processes, enhancing decision-making, and delivering personalized services. However, while AI adoption holds significant promise, it also presents notable challenges, including ethical concerns, data privacy issues, skills gaps, and high implementation costs. This paper explores the advantages of AI adoption, the barriers it faces, and future trends that could shape its progression. By examining case studies and identifying key trends, this paper aims to provide a comprehensive overview of the adoption of AI and its potential for transforming industries worldwide.

DOI: 10.61137/ijsret.vol.10.issue5.320
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Agriculture Sustainability: A Comprehensive Review/strong>
Authors:-Rajat Kumar, Gurshaminder Singh

Abstract-Agricultural sustainability is essential for meeting global food demands, safeguarding the environment, and ensuring economic stability. This review delves into the various dimensions of sustainable agriculture, covering practices, technologies, policies and their collective effects on biodiversity, soil heath, and climate resilience. A central focus is on blending traditional agricultural knowledge with contemporary innovations to create sustainable practices that support biodiversity and soil vitality while adapting to climate challenges. The role of agroecology, which emphasizes ecological principles in agricultural settings, is highlighted as a key approach in promoting biodiversity and minimizing environmental impact. Additionally, the review stresses the importance of robust policy framework that support sustainable practices, ensure resource management, and address climate impacts. The paper also examines the main challenges hindering sustainable agriculture, such as resource depletion, land degradation, water scarcity, and economic pressures. These issues are interconnected with socio-economic factors, including access to resources, income stability, and social equity, all of which shape agricultural sustainability and impact communities reliant on farming. Resource depletion and land degradation are particularly emphasized, as they reduce productivity and soil health, leading to less resilient agricultural systems. To combat these challenges, the review suggests innovative solutions aimed at fostering resilience and sustainability. These include precision agriculture, which leverages data and technology for efficient resource use, crop diversification to reduce vulnerability to climate shifts, and regenerative farming practices that enhance soil health and sequester carbon. The potential of agroecology and regenerative practices is especially emphasized for their ability to restore ecosystems while boosting productivity. Policy interventions, particularly those that support sustainable practices, incentivize research and development in agro-innovations, and provide farmers with training and resources, are crucial for advancing sustainable agriculture.

DOI: 10.61137/ijsret.vol.10.issue5.321
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Harnessing AIML for Sustainable Optimization in Agricultural Supply Chains/strong>
Authors:-Jayesh Hajare, Anshika Mishra, Kiran Pounikar, Vikas Yadav, Assistant Professor Princy Shrivastava

Abstract-The present research addresses the integration of Artificial Intelligence and Machine Learning (AIML) to optimize agricultural supply chains with respect to critical challenges surrounding efficiencies, costs and sustainability. With agriculture experiencing mounting pressure from climate change, market volatility and resource depletion and limited long-term solutions, AIML provides a novel approach to addressing challenges in the sector. We present a comprehensive AIML framework that provides decision support throughout the agricultural supply chain by leveraging historical and real-time agricultural data. We examine different machine learning approaches with a focus on predictive analytics and optimization to enhance yield prediction, resource allocation, and efficient logistical management. Findings suggest that the AIML model not only improves efficiencies, but also contributes to advancing sustainable agricultural practices. Finally, we posit that this AIML model would lead to a possible significant reduction of waste, overhead costs and improved profits in the agricultural supply chain and will ultimately improve agricultural ecosystem resilience. The objective of the paper is to provide insight into utilizing AIML methods in agricultural supply chain management and possible implications for future research and application in this important area.

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Real-Time River Health Monitoring using Custom Dataset, YOLOv8, and Crowdsourced Solutions: A Comprehensive Review/strong>
Authors:-Assistant Professor Mrs. Vandana Navale, Yashi Solanki, Riddhi Khot, Pradnya Nalawade, Aakanksha Wadekar

Abstract-Water pollution is still a global problem, especially in urban waters. The routine process of monitoring water bodies is slow and resource intensive. This paper reviews modern approaches to monitoring water health using proprietary data, deep learning models such as YOLOv8 for pollution detection, and public service centers for initiating cleanup projects. The review describes the collection of user data and examines how the proposed system combines public research with machine learning techniques to develop good and measurable solutions to problems. It also investigates the role of public services in promo ng knowledge and environmental financing.

DOI: 10.61137/ijsret.vol.10.issue5.322
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Surface Water Cleaning Robot (SWCR) for Sustainable Environmental Protection/strong>
Authors:-T. Anilkumar, V. Abhiram, K. Sampath Kumar, R. Yashwanth Sai Ganesh, U. Bhavani Prasad, P. Aditya Raj

Abstract-The emphasis of the project is centered upon the designing and advanced construction of an ecological water cleaning system that has wireless control features which integrates advanced environmental monitoring and robotics that is operated remotely towards achieving environmental sustainability. Consequently, due to the growing concern of water pollution, there is an increasing demand of deploying an easy system which will eliminate the waste and pollutants from the water bodies in an efficient manner. The system consists of a and a rotary bracket which consists of a substantial floating platform mounted on a 12V battery, four 500 RPM DC motors and L298N motor driver for river surface navigation. The operation of this device centers around the use of an ESP32- CAM module which acts as a camera that streams real time images to the operator for effective monitoring of the device and waste collection process. This system solves the problem of debris reduction in water bodies and enhances water reclamation curbing the risks of cash intensive manual cleaning. If the technology comes into practice it is going to improve environmental protection by introducing a new approach to environmental management and promoting sustainability strategies in the protection of water bodies.

DOI: 10.61137/ijsret.vol.10.issue5.323
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The Impact of Behavioural Features on Predicting Academic Success: A Machine Learning Approach/strong>
Authors:-Nidhi Kataria Chawla, Swati Sareen, Chietra Jalota

Abstract-To discover hidden patterns from educational data, researchers are developing methods by using educational data mining. Dataset and its features/attributes determine the eminence of data mining techniques. Student’s academic performance model by using a new class of features i.e., behavioural features was built in this research paper. These are significant features as they are associated with the learner interactivity in e-learning system. Data was collected from an e-Learning system called Kalboard 360 using Experience API web service called (xAPI). After data preprocessing and feature selection, machine learning algorithms such as Decision Tree, Support Vector Machine and Artificial Neural Network were used to build the model. It is clearly visible from the results that there is a sturdy association between learner behaviours and its academic achievement. Results with above-mentioned classification methods using behavioural features attained up to 25% enhancement in the accuracy as compared to the results when same classification methods were applied on the data set without behavioural features.

DOI: 10.61137/ijsret.vol.10.issue5.324
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Seismic Analysis of C-Shaped Building with Varying Bay Length: A Review/strong>
Authors:-Vikas Patanker, Deepesh Malviya, Ankita Choubey

Abstract-This study looks at four instances of G+10 story C-shaped buildings. By considering the distinctive irregularities, engineers can design structures that satisfy performance requirements and make efficient use of materials. In order to distinguish the other three structures from the base model, we looked at the same building with varying bay lengths. The base model’s bay length is 27 meters, while the second model’s bay length is roughly 33 meters, structure III’s bay length is 39 meters and 4th models bay length is 45 meters. In this study, an irregularly shaped building model will be analyzed and designed using STAAD.Pro. Shear force, bending moment, and storey drift etc. are just a few of the parameters that will be used to compare the results with simplified analysis methods in order to illustrate the advantages of using STAAD.Pro for irregular building design.

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Forensic Analysis Model for Investigating Cybercrime Over the Network/strong>
Authors:-Midhunya.P.S, Adhulya. D, Merlin Jenifer. L, D. Suganthi, J. Mythili, Dr. N. Prabhu

Abstract-Despite significant investments in security protocols, the frequency of cybersecurity incidents continues to rise, with traditional methods proving ineffective against complex cyber-attacks. This research aims to address this issue by using a publicly accessible dataset on Advanced Persistent Threats (APTs) to develop a data-driven approach for identifying APT phases within the Cyber Kill Chain framework. APTs are sophisticated and targeted attack strategies that can bypass conventional intrusion detection systems, posing a major challenge for security professionals. The study incorporates several machine learning classifiers, including Naïve Bayes, Bayes Net, KNN, Random Forest, and Support Vector Machine (SVM), to analyze the dataset and identify APT phases, offering a promising method for improving cybersecurity detection and response.

DOI: 10.61137/ijsret.vol.10.issue5.499
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Comparative Analysis on Social Media Sites Using Sentiment Analysis/strong>
Authors:-Indhuja.G, Abinaya.K, Deekshitha.M, D. Suganthi, J Mythili, Dr. J. Viji Gripsy

Abstract-This paper evaluates user views and emotional tone in postings across many social media sites by means of a comparative analysis utilising sentiment analysis. Understanding the mood underlying user-generated material has become vital for companies, marketers, and academics as social media is playing more and more influence on public debate. Focussing on sites like Twitter, Facebook, and Instagram, the paper uses sentiment analysis methods on social media data. The performance of these models in terms of accuracy, precision, recall, and F1 score is compared using machine learning models including Support Vector Machines (SVM), Light GBM (LGBM), and Long Short-Term Memory (LSTM). The results expose how sentiment patterns vary on different platforms, therefore offering understanding of public opinion dynamics, brand perception, and content engagement. Following LGBM in precisely identifying sentiment, the study emphasises SVM and LSTM’s efficiency and analyses the ramifications of these results for content development, market research, and social media monitoring.

DOI: 10.61137/ijsret.vol.10.issue5.500
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Published by: vikaspatanker