IJSRET Volume 10 Issue 5, Sep-oct-2024

Uncategorized

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
55

A Comparative Study on the Impact of Social Media Marketing on Consumer Purchasing Behaviour/strong>
Authors:-Yazad Sarkari

Abstract-Social media marketing has emerged as a transformative force in the business landscape, fundamentally altering the way brands engage with consumers and how consumers make purchasing decisions. This report provides a comprehensive overview of social media marketing, highlighting its growing significance in the digital age and its profound impact on consumer behaviour. With billions of users actively engaging on platforms such as Facebook, Instagram, Twitter, and TikTok, businesses are increasingly utilizing these channels to reach their target audiences through tailored content and strategic advertising. The report examines the various mechanisms by which social media influences consumer behaviour. Key factors include the role of user-generated content, which enhances authenticity and trust, as well as the effectiveness of targeted advertising that leverages data analytics to reach specific demographics. Additionally, influencer marketing has emerged as a crucial strategy, with social media influencers wielding substantial power in shaping consumer opinions and preferences. The concept of social proof, where consumers look to their peers for validation, is explored in detail, demonstrating how reviews, likes, shares, and comments can significantly impact purchasing decisions. Through a review of relevant literature and case studies, this report reveals notable trends in consumer behaviour, such as the increasing importance of visual content, the demand for interactive engagement, and the rise of instant purchasing options facilitated by social media platforms. Moreover, the analysis highlights the challenges brands face in navigating the complexities of social media landscapes, including managing negative feedback and maintaining a consistent brand image.

DOI: 10.61137/ijsret.vol.10.issue5.303
55

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
55

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.

55

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.

55

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
55

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
55

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
55

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
55

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
55

Social Media Insights/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
55

Social Media Insights/strong>
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
55

A Study of the Behavioural Biases in Investment Decision-Making in Mumbai/strong>
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.

55

Easy Trade: Forex Trading bot Using Artificial Intelligence/strong>
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
55

College Admisssion Enquiry Chatbot Using Machine Learning/strong>
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.

55

Student Voting Election Portal/strong>
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
55

A Cost-Benefit Analysis of Material Handling on the Productivity of Food and Beverage Manufacturing Industries/strong>
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
55

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
55

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
55

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
55

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.

55

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
55

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
55

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.

55

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
55

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
55

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.

55

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
55

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
55

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
55

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.

55