IJSRET Volume 11 Issue 2, Mar-Apr-2025

E- Nursery Retail Project Using Clustering Algorithm & Visual Data Representation
Authors:-Savita Sawant, Supriya Morve, Janhavi Bhosale

Abstract-The “E- NURSERY RETAIL” project is a dynamic and user-friendly online platform designed to revolutionize the plant nursery retail experience. This web-based solution aims to bridge the gap between plant enthusiasts and nurseries, providing a comprehensive and accessible platform for users to explore, purchase, and engage with a diverse range of plants and gardening products. The project leverages modern e-commerce functionalities, intuitive design, and community-building features to create a seamless and enriching online environment for plant enthusiasts. The platform boosts a vast and well-curated catalog of plant species. Users can explore detailed descriptions, care guides, and customer reviews to make informed purchasing decisions. An intuitive and visually appealing user interface ensures a seamless browsing and shopping experience. In this project, the implementation of the machine learning algorithm works as a recommendation system that suggests or recommends additional products to consumers. These can be based on various criteria including past purchases, search history, demographic information and other factors. This is implemented using clustering algorithms that can be used to group similar users together based on their preferences and behaviors. Visualization of data provides graphical representation of project-related information which makes complex project data more understandable and accessible, allowing project teams and stakeholders to gain insights, make informed decisions, and communicate effectively for visual data representation.

Farmer Assist Agri Bot an Automated Seeding Arrangement by Robot in Cultivation Land
Authors:-Gk. Rameswari, L.Amarnadh, N.Ganesh, R.Venkatesh, Mrs.S.Priyanka

Abstract-In recent years, robotics in agriculture sector with its implementation based on precision agriculture concept is the newly emerging technology. The main reason behind automation of farming processes are saving the time and energy required for performing repetitive farming tasks and increasing the productivity of yield by treating every crop individually using precision farming concept. Designing of such robots is model based on particular approach and certain considerations of agriculture environment in which it is going to work. These considerations and different approaches are discussed in this paper. Also, prototype of an autonomous Agriculture Robot is presented which is specifically designed for seed sowing, forming task only.

Precision Tracking and Enumeration of Benthic Species with Yolo+ Deepsort Network Improvements
Authors:-Dhanalakshmi V, Amirthajaya T, Bhuvaneshwari A

Abstract-The health of marine ecosystems is vital for biodiversity and ecological balance, with benthic species serving as key indicators of environmental conditions. Traditional monitoring methods, such as manual surveys and video analysis, are labor-intensive and error-prone. This research proposes an AI-driven solution integrating an optimized YOLO object detection model with an improved DeepSORT tracking algorithm for accurate benthic species identification and counting. The approach addresses challenges like poor visibility, occlusion, and species overlap, enhancing monitoring efficiency for conservation and fisheries management. Experimental results demonstrate an accuracy of 87.1% mAP@0.5 and 53.3% mAP@0.5:0.95, showing improvements of 1.8% and 4.0%, respectively, over YOLOv5. The integration of DeepSORT further strengthens its application in marine ranching supervision. This AI-based method offers a reliable and automated alternative for marine ecosystem evaluation and conservation efforts./p>
DOI: 10.61137/ijsret.vol.11.issue2.201

Multimodal Sentiment Analysis
Authors:-Assistant Professor Ms.S.Prathi

Abstract-Multimodal sentiment analysis (MSA) integrates data from multiple sources, such as text, audio, and visual cues, to enhance the accuracy and interpretability of sentiment classification models. Traditional sentiment analysis predominantly relies on textual data, which can be limited in capturing non-verbal nuances like tone of voice or facial expressions. This paper explores the synergy between text, speech, and visual data in sentiment analysis tasks, addressing key challenges such as data alignment, feature extraction, and fusion techniques. We compare various fusion strategies, including early, late, and hybrid fusion, using state-of-the-art deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Experimental results demonstrate that multimodal approaches significantly outperform unimodal systems, providing higher accuracy and robustness in sentiment detection. We discuss the potential applications of multimodal sentiment analysis in fields such as social media monitoring, customer sentiment analysis, and healthcare. Finally, the paper outlines future research directions, emphasizing the need for more efficient fusion techniques and the incorporation of emerging models to advance multimodal sentiment analysis further.

DOI: 10.61137/ijsret.vol.11.issue2.202

Money Theft Deterrent: Intelligent Locking and Monitoring in Bank
Authors:-Gangireddy Jayakumarreddy, Pikkili Hari Krishna, Dr.Selvarasu.S

Abstract-This work proposes an intelligent locking and monitoring system to significantly enhance money theft deterrence in banking environments. Leveraging advanced sensor technologies and AI-driven analytics, the system provides real-time threat detection and automated response. Integrated biometric authentication and multi-factor authorization protocols fortify access control to sensitive areas and cash reserves. The system employs dynamic locking mechanisms, triggered by anomalous activity, to prevent unauthorized entry and asset removal. Continuous video surveillance, coupled with intelligent image processing, identifies and tracks suspicious behaviors within the bank premises. Remote monitoring capabilities enable swift intervention by security personnel in response to potential theft attempts. Data encryption and secure communication channels ensure the integrity and confidentiality of sensitive monitoring information. Predictive analytics models are utilized to forecast potential security breaches and proactively mitigate risks. The system aims to minimize human error and response time, thereby increasing the overall effectiveness of theft deterrence. This intelligent approach provides a robust, adaptable, and scalable security solution for modern banking institutions.

DOI: 10.61137/ijsret.vol.11.issue2.203

A Study on Comparative Analysis of Working Capital Management of Selective Pump and Motor Manufacturing Companies in Coimbatore
Authors:-Author Jhanani P, Associate Professor Dr. P. Syamsundar

Abstract-This study examines the working capital management practices of three prominent motor and pump manufacturing companies in Coimbatore: A S Engineering, RI Pumps, and LGI Equipment Limited. Working capital management is crucial to financial performance, directly affecting liquidity, operational efficiency, and profitability. This paper presents a comparative analysis over five years (2019-2024), highlighting key performance indicators such as inventory, receivables, and payables turnover. The findings suggest that A S Engineering has demonstrated consistent improvements in liquidity and receivables management, while LGI Equipment shows robust liquidity but struggles with receivables. RI Pumps, although progressing, lags in inventory and cash conversion efficiency. The study’s insights offer actionable recommendations for optimizing working capital, enhancing both financial stability and competitiveness in the pump and motor manufacturing industry.

Correlation of California Bearing Ratio with Index Properties of Subgrade Soils in Konso Zone, Snnpr, Ethiopia
Authors:-Mr. Bahiru Berisha Koyrita, Associate Professor Dr. Vasudeva Rao, Assistant Professor Mr. Democrcy Dillo, Mr. Abiy Ilto

Abstract-The California Bearing Ratio (CBR) is crucial for assessing soil strength during pavement design, particularly in evaluating subgrade performance. It is tedious and time consuming to obtain CBR tested values in the laboratory even though use of CBR as a performance parameter is widely acknowledged. It is also very difficult to prepare sample at desired level in situ density for laboratory testing. This study investigates the correlation between CBR values and various index properties of subgrade soils in of Konso Zone, Ethiopia. CBR tests were performed on twenty four soil samples in the laboratory, collected from various locations in Konso Zone. This research presents and discusses the results of CBR values predicted by linear regression and multi-linear regression in comparison with the tested results of various soil properties. Experimental findings show that CBR presents meaningful statistical relationships with the liquid limit, plastic limit, plasticity index, % finer passing through 0.075mm, maximum dry density and optimum moisture content. Soil characteristics were used in predictive model development through relationship analysis to determine CBR values. The established models delivered accurate and dependable results which function as economically sound substitutes for established CBR testing methods. The study emphasizes that using index and compaction properties can effectively evaluate subgrade strength, offering a more feasible and cost-effective approach than direct CBR testing. Consequently, the proposed equations serve as practical tools for preliminary material identification in the local context. Based on the test results, the soils are categorized as fine soils. Investigation of the experimental data indicated that there exist a good correlation among the actual value and predicted value of CBR. Most important equations are proposed, and applicable with sufficient accuracy for preliminary identification of material for the local area. California bearing ration, CBR is the function of Liquid limit (LL) and plastic limit (PL) i.e. CBR =fn(LL, PL) with recommended equation of, CBR=-0.0536 (LL)- 0.1424 (PL)+12.881. Thus, from practical point of view it is easier and feasible to use index and compaction properties to evaluate the subgrade strength characterization for road design purpose within short time and less cost than the CBR test, as a result this model can have a vital role in doing so.

Combining Data Filtration and Regression Learning for Enhancing the Forecasting of Cryptocurrencies
Authors:-Neha Sunhare, Dr. Kamlesh Ahuja

Abstract-The cryptocurrency market is highly volatile and unpredictable, making traditional financial models less effective for price forecasting. Unlike stock markets, which are influenced by earnings reports and economic indicators, cryptocurrency prices are driven by a combination of market sentiment, technological developments, regulatory changes, and supply-demand dynamics. Due to the complexity and non-linearity of these factors, machine learning (ML) has emerged as a powerful tool for predicting crypto prices with greater accuracy. The proposed work employs the steepest descent based scaled back propagation algorithm along with the data pre-processing using the discrete wavelet transform (DWT) for crypto price prediction. It has been shown that the proposed system attains lesser MAPE% error compared to previously existing techniques making it a more accurate forecasting model.

DOI: 10.61137/ijsret.vol.11.issue2.204

Machine Learning-Based Network Intrusion Detection for Iot and Smart Detection Using Recursive Feature Elimination, Binning Technique and Grid Search CV
Authors:-Damilola Akinola, Micheal Olalekan Ajinaja, Yetunde Esther Ogunwale

Abstract-The rapid growth of Internet of Things (IoT) devices and smart technologies has heightened the risk of network intrusions, demanding advanced Intrusion Detection Systems (IDS) for cybersecurity. This study proposes a machine learning-based IDS framework for IoT networks, utilizing Recursive Feature Elimination (RFE), binning techniques, and GridSearch CV for feature selection and hyper parameter tuning. The framework employs the CICIDS2017 dataset, a widely used benchmark for intrusion detection, to train and validate the models.The proposed pipeline begins with data preprocessing, including attribute verification, duplicate removal, and label encoding, followed by a robust feature selection process. RFE is used to retain the most significant features, while feature engineering incorporates domain knowledge to generate new attributes. Binning techniques are applied to handle continuous features, and GridSearchCV is employed to identify the best parameter combinations for model optimization. The framework includes several machine learning models, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM), which are optimized through the feature selection and hyperparameter tuning process.Visualization techniques are utilized to enhance understanding of feature importance and model behavior. The performance metrics highlight the effectiveness of the approach, with Random Forest achieving a notable accuracy of 99.78%, followed by Decision Tree with 99.50%. These results underscore the capability of the proposed methodology to address network intrusion detection challenges within IoT ecosystems.The framework provides a robust and scalable solution for securing interconnected IoT systems against evolving cyber threats. Its ability to efficiently detect intrusions and optimize model performance demonstrates its potential as a valuable tool for enhancing cybersecurity in IoT networks.

Development of Eco-Friendly Bricks Using Industrial and Agricultural Waste
Authors:-M P Iniya, K Sabarinathan, G Shanmugave Murugan, V Rishi

Abstract-One of the most important and often used building materials in masonry construction worldwide is brick. The environmental load brought on by trash deposition can be reduced by making bricks from waste materials. The purpose of this study is to assess the impact of adding trash made from rice husk ash. Samples were prepared with different percentages of cement, fly ash, lime, river sand, and rice husk ash. recycling a variety of waste materials, including as fly ash (40 – 60%), rice husk ash (15 – 20%), lime (10%), cement, and river sand (15 – 20%), for use in brick production. The dimensions of the brick specimen are 230 x 110 x 75 mm. Experiments are conducted to examine differences in properties including compressive strength, water absorption, hardness, and soundness. This review will lead to recommendations for additional research on the effects of that waste on bricks’ mechanical and physical properties. The uses of agricultural wastes as cheap and environmental-friendly construction materials are beneficial towards provision of affordable housing in developing country.

DOI: 10.61137/ijsret.vol.11.issue2.205

Voice Controlled Wheel Chair with Fall Detection Using Iot
Authors:-Aarthi B, Dharshana S P, Dharshini k, S.Hari Kumar

Abstract-This project aims to design and implement an assistive system for people with disabilities, combining voice- based wheelchair control, health monitoring, and emergency alert features. The system utilizes an Arduino Nano, Bluetooth communication, a mobile application, and various sensors to monitor the user’s health status in real-time. The wheelchair can be controlled via voice commands received from a mobile app, allowing users to move the wheelchair with simple verbal instructions. Health monitoring is achieved through sensors that track the user’s heart rate, body temperature, and detect falls using an accelerometer. In the event of an emergency, the system can send notifications via SMS using a GSM module, which includes the user’s GPS location and health data. Additionally, all sensor data is uploaded to the ThingSpeak IoT platform for remote monitoring and analysis. This integrated system provides not only mobility assistance but also enhances safety and well-being for disabled individuals by offering real- time health status updates and emergency alerts.

DOI: 10.61137/ijsret.vol.11.issue2.206

Voice Controlled Wheel Chair with Fall Detection Using Iot
Authors:-Aarthi B, Dharshana S P, Dharshini k, S.Hari Kumar

DOI: 10.61137/ijsret.vol.11.issue2.206

Maximizing Lifetime of IOT-Based Hetero WSNs for Sustainable Smart City Application
Authors:-Janani S, Lavanya S, Manju shri S, Mrs.K.Yazhini

Abstract-IoT-based Heterogeneous WSN (HWSN) technologies are useful instruments for accomplishing sustainability objectives in Sustainable Smart Cities (SSCs) due to their adaptability and wide range of applications. Even though WSN heterogeneity is still being investigated by researchers, it is becoming increasingly crucial to develop affordable models that address many aspects of SSC while maintaining their stability and dependability. To identify disjoint CSs that are energy-aware, we suggest a novel technique, the Required Energy Aware technique (REA). In every iteration, the REA algorithm carefully attempts to create the set that optimizes longevity while abiding by CS criteria. The output simulation increases network longevity, reduces resource consumption, and permits effective distribution of data sensing and collection activities throughout the network.

DOI: 10.61137/ijsret.vol.11.issue2.207

Future of Online Bike Rental Systems in Smart Cities
Authors:-Meenachi Sri S, Nandhini S M, Naveena M, M.Dharmalingam

Abstract-The rapid growth of urban mobility solutions has increased the demand for efficient and secure bike rental systems. This project proposes an automated bike rental system using RFID, GPS, and IoT technologies to enhance the rental experience. The system ensures accurate rentals, prevents unauthorized access, and optimizes bike availability through real-time tracking. The RFID-based scanning system enables automatic bike identification and authentication, reducing human error. Each bike has an RFID tag, while rental stations feature RFID readers for automated check-in and check-out, streamlining the rental process. GPS tracking provides real-time location monitoring, improving fleet management and user safety. Users can locate bikes, check availability, and plan trips efficiently. IoT integration connects bike locks, tracking modules, and the mobile app for seamless operation. Smart locks with RFID sensors automatically unlock bikes upon successful user verification. IoT ensures real-time updates on bike status, battery levels, and maintenance needs. The mobile application allows users to rent bikes, track rentals, and make payments securely. Secure communication protocols protect user data and prevent cyber threats. The system is scalable, supporting multiple locations and rental stations. Cloud management ensures efficient data handling and fleet coordination. By integrating automated management, real-time monitoring, and enhanced security, this system offers a reliable and safe bike rental solution for urban commuters.

DOI: 10.61137/ijsret.vol.11.issue2.208

Document Management for Loan Applications in Sales Force
Authors:-Pratham Chauhan, Bhumi Kaushal Shah

Abstract-Effective document management is critical for financial institutions handling loan applications. Salesforce, a leading CRM platform, offers robust document management solutions that streamline loan processing, improve compliance, and enhance customer experience. This research paper explores document management in Salesforce for loan applications, highlighting its features, benefits, challenges, and real-world case studies.

MATLAB Based Analysis of Synthetic Chirp Signals Using FFT and CWT
Authors:-Rudra Krishna

Abstract-This paper compares two popular methods for time-frequency analysis: the Fast Fourier Transform (FFT) and the Continuous Wavelet Transform (CWT) for a synthetic chirp signal wave. We apply these techniques to a chirp signal, which is a non-stationary signal with a frequency that changes over time. The results demonstrate the strengths and limitations of each method, highlighting the FFT’s ability to provide global frequency information and the CWT’s superior time-frequency localization. FFT renders the representation of signal only in the frequency domain. The CWT, using the morse wavelet, on other hand provides a more compact visualisation of the signal in both time and frequency domain visualising or representing the frequency domain of the signal simultaneously. The performance of these techniques is compared visually and computationally.

DOI: 10.61137/ijsret.vol.11.issue2.209

Collusion-Free MANET Communication Framework for Direct Connectivity
Authors:-Asmitha V, Jaya Malini V, Manisha M, K. Amudha

Abstract-Dynamic, infrastructure-free communication be- tween mobile devices are made possible via mobile ad hoc networks, or MANETs. Their dependability is hampered by issues including malicious activity, node cooperation, and security risks. A collusion-free MANET communication architecture for safe and effective direct mobile-to-mobile networking is pro- posed in this research. The system uses trust-based processes and sophisticated cryptographic algorithms to identify and stop node collusion. Network performance indicators like throughput, latency, and packet delivery ratio may be thoroughly analyzed through simulation using MATLAB. By improving MANETs’ overall security and dependability, the suggested method enables smooth communication in dynamic, resource-constrained con- texts. Results show that it performs better than current methods, which makes it appropriate for use in remote connectivity, military operations, and disaster recovery.

DOI: 10.61137/ijsret.vol.11.issue2.210

Window Cleaning Device
Authors:-Abinaya R, Dharanish V, Kavyasri R, Lalitha S

Abstract-Maintaining clean windows in high-rise buildings and difficult-to-reach areas is a challenging and time-consuming task. This project presents an Automated Window Cleaning Device designed to efficiently clean glass surfaces with minimal human intervention. The device is equipped with a suction motor and Velcro mechanism to adhere securely to the glass, along with wheels for smooth movement across the surface. It integrates a sprayer system for applying cleaning solution and microfiber cloths to ensure effective cleaning. The device operates on an external power supply, supported by an inbuilt backup battery to handle short-term power interruptions. Additionally, it features proximity sensors to navigate window edges and ensure full surface coverage. Users receive notifications in case of power disruptions, enhancing safety and reliability. This system offers a cost-effective, efficient, and safe solution for maintaining clean windows, reducing manual labour and improving accessibility for high-rise buildings.

DOI: 10.61137/ijsret.vol.11.issue2.211

E-Commerce Recommendation Based on Latent Features of User & Product
Authors:-Yogini Sarathe, Professor Rahul Patidar, Professor Jayshree Boaddh

Abstract-In recent years, online shopping has become an integral part of daily life, offering convenience and requiring minimal effort for purchasing products. With the rapid growth of e-commerce businesses, recommendation engines have emerged as a vital component in enhancing user experience and driving sales. These engines have gained widespread popularity due to their effectiveness and ease of integration into modern online platforms. This paper has proposed a product recommendation system that utilizes user and product features. Products that were explore by user has its own latent features that were extract and used for the recommendation. This paper extract user features as well for improving the recommendation accuracy. Experiment was done on different set of product and user counts. Result shows that proposed model has increases the prediction accuracy.

Leveraging Technology for Tax Reform in India: Emerging Trends and Impacts on Taxpayer Services
Authors:-Manikandan B, Kiruthika G, Meghana, Kavya M, Mithil Udupa, Lonika Singh U, Navyasree, N Roshan Choudhury

Abstract-The rapid application of technology in India has improved the administration of taxes, bringing along with it the solution to traditional inefficiencies, improved compliance, and better taxpayer service. Integration of digital solutions allows for transparency, reduction in fraud, and facilitates seamless revenue collection for increased economic growth. The key reforms have modernized the system by making it more efficient and user-friendly: e-Tax filing, Goods and Services Tax Network, and data analytics in tax compliance. Artificial Intelligence, machine learning, and blockchain are becoming revolutionary vehicles to detect fraud; an automated process for the benefit of taxpayers is achieved while referring to tax advice. However, it faces challenges in the form of the digital divide, cybersecurity risks, technological complexity, and resistance to change. To overcome these, strong digital infrastructure, programs for taxpayer education, and secure data management systems will be needed for the sustainable success of digital tax reforms. In line with India’s continued digital transformation, effective use of technology in building a more inclusive, efficient, and transparent tax ecosystem will be imperative.

Experimental Studies on Mechanical Properties of Self Healing Geopolymer Concrete
Authors:-S. Kavipriya, S. Adithiya, P. Gowtham, S. James Christopher

Abstract-This study investigates the development and mechanical properties of bacteria-infused self-healing ceramic-based geopolymer concrete. Geopolymer concrete, a sustainable alternative to traditional cement-based concrete, was infused with bacteria (Bacillus Megaterium) to enhance its self-healing capabilities. Geopolymer is a new development in the world of concrete in which cement is totally replaced by pozzolanic materials like fly ash and activated by highly alkaline solutions to act as a binder in the concrete mix. In this project we have totally replaced cement with ceramic powder. Therefore, efforts are made in this study to develop geopolymer concrete by employing ceramic powder as binder material and sodium hydroxide (NaOH) and sodium silicates (Na2SiO3) as alkaline activators. The activator solution (AAS) to binder solids (BS) ratio ranging from 0.4 to 1.0. Investigations are carried out to determine the fresh properties and mechanical properties such as compressive strength, splitting tensile strength, flexural strength and water absorption also carried out. Present investigation has been under taken to study the strength parameters of GPC on adding 10%, 20%, 30% of bacteria (Bacillus Megaterium) along with water in GPC. The results show that the bacteria-infused geopolymer concrete exhibits improved mechanical and self healing properties, reduced water absorption, and enhanced self-healing capabilities compared to the control concrete. This innovative material has potential applications in infrastructure development, offering a sustainable solution for concrete structures.

DOI: 10.61137/ijsret.vol.11.issue2.212

Consumer Attitudes toward Solar Panel Installation in Coimbatore
Authors:-Mukilan V, Assistant Professor Mr. T K Ashvin

Abstract-This study explores the factors influencing consumer attitudes toward installing solar panels in Coimbatore, a city known for its high solar potential and progressive outlook on environmental sustainability. Through descriptive and regression analysis of responses from 134 individuals, the study investigates economic, social, and environmental motivators and deterrents impacting solar adoption. Results indicate that trust in government-provided information, social influence, and cost-saving potential drive positive attitudes, while high installation and maintenance costs present challenges. The findings suggest targeted awareness programs and incentive-based strategies could increase solar adoption, supporting Coimbatore’s renewable energy goals.

Some Peculiarities of Creation the Laser-Induced Thin Films
Authors:-Petro P. Trokhimchuck

Abstract-Some peculiarities of generation and modeling of laser-induced films are discussing. This problem is connecting with problem of phase transformations. Difference mechanisms of generation surface and subsurface laser-induced structures are analyzing. Roles of regimes of irradiation (saturation of excitation, geometrical sizes of irradiation zone, temporal characteristics) on formation corresponding structures and geometrical form of substrate are shown. So, the surface laser-induced structures are represented of indium antimonite, indium arsenide, silicon and germanium structures, which are received after irradiation of nanosecond pulses of Neodimium laser and nanosecond and millisecond Ruby laser. Volume structures are represented for femtosecond pulse laser irradiation of silicon carbide and nanosecond pulse CO2-laser irradiation of potassium chloride. Two various models allow to explain these results. Some problems of application these results in modern optoelectronics in more wide sense (including other methods of receiving black silicon and other nanostructures) are discussing too. The application of laser radiation in more complex technological processes using ion implantation and external electromagnetic fields is also discussed.

Adapting Pre-Performance Protocols from Sports Psychology for Music Education
Authors:-Hin-Sing Au

Abstract-This comprehensive review explores how to translate the pre-performance routine protocols of sports psychology to music education. By organizing empirical research from both fields, this review aggregates evidence-based practices for performing preparation and offers structured protocols for implementation in music education. These results provide evidence that structured preparations before performance can promote performance consistency and psychological preparedness in music to a similar degree that they are known to do in sport. This review offers some useful recommendations for music educators and suggests important questions for research to follow in performance-prep technique.

The Fintech Industry in India: Current Trends and Future Prospects
Authors:-Palak Gupta

Abstract-The fintech industry in India has experienced significant growth over the past decade, driven by advancements in digital payments, regulatory support, and increased financial inclusion. This paper explores the current state of fintech in India, highlighting key segments such as digital banking, payment solutions, lending platforms, and blockchain-based innovations. Furthermore, it delves into the challenges faced by the sector, including cybersecurity threats, regulatory hurdles, and financial literacy issues. The study also provides insights into future trends, such as the rise of artificial intelligence (AI) in financial services, the role of central bank digital currencies (CBDCs), and the increasing adoption of open banking. By analyzing the trajectory of fintech in India, this research aims to provide a comprehensive understanding of its impact on the broader financial ecosystem and its future growth potential.

Circular Economy in Lithium Battery Manufacturing: Recycling Waste for a Sustainable Future
Authors:-Prabhjyot Kaur Juneja, Karan Gupta

Abstract-The exponential growth in lithium-ion battery (LIB) production has brought challenges, including significant waste generation and environmental impact. This paper explores strategies for recycling and reusing waste materials in lithium battery manufacturing to align with circular economy principles. It examines key technologies like hydrometallurgy and pyrometallurgy, evaluates their economic and environmental implications, and analyzes case studies of recycling initiatives within manufacturing facilities. By integrating experimental data and economic modeling, the study demonstrates the potential of recycling to reduce production costs, decrease reliance on virgin materials, and lower carbon emissions. The findings highlight a pathway for lithium battery manufacturers to adopt sustainable practices without compromising profitability.

DOI: 10.61137/ijsret.vol.11.issue2.219

Thermal Management during Manufacturing of Lithium-Ion Batteries
Authors:-Prabhjyot Kaur Juneja, Karan Gupta

Abstract-Thermal management is a critical aspect of lithium-ion battery (LIB) manufacturing, influencing product quality, process efficiency, and safety. This paper examines various methods for controlling temperature during key production stages such as electrode preparation, cell assembly, and electrolyte filling. Techniques like active cooling, infrared (IR) heating, and advanced thermal monitoring systems are analyzed for their effectiveness. Using case studies, experimental data, and thermal simulations, the research evaluates the impact of temperature control on material integrity, energy efficiency, and manufacturing throughput. Results indicate that optimized thermal management not only improves battery performance and lifespan but also reduces operational costs and energy consumption. Recommendations for future innovations in thermal management technologies are discussed to meet the growing demand for high-quality LIBs in electric vehicles (EVs) and energy storage systems.

DOI: 10.61137/ijsret.vol.11.issue2.220

OSINT-Based Threat Intelligence: Investigating Leaked Data on the Dark Web
Authors:-Professor Dr. Mukesh Patidar, Kasani Vignesh Kumar

Abstract-The Dark Web has become a hotspot for cybercrime, serving as a market for stolen credentials, financial data, and sensitive corporate information. It poses an emerging threat for organizations to identify and counter threats that are created from leaked data, as cybercriminals utilize advanced anonymiza- tion tools and encryption in an effort to remain anonymous to law enforcement. Open-Source Intelligence (OSINT) has been a motivating factor for cybersecurity researchers to track, analyze, and assess such threats on the basis of publicly available information as well as automated reconnaissance techniques.
This study paper examines OSINT-driven threat intelligence processes to analyze leaked data on the Dark Web. The paper explores the processes through which cybersecurity analysts use tools such as Maltego, SpiderFoot, and Scrapy in order to monitor Dark Web markets, forums, and hidden sites. The paper explores data collection methods, legal and ethical issues, and methods of evading cybercriminal detection. The study also presents real- life case studies of data breaches, breaking down the patterns in cybercrime attacks and the type of information that is most often leaked. The findings of this research provide information on the extent and impact of leaked data, establishing trends in cybercrime activity and offering countermeasures. The study highlights the importance of proactive monitoring, real-time processing, and automation in OSINT-driven threat intelligence. Future research will include the integration of AI-driven threat intelligence systems to enhance detection rates and automate Dark Web investigations, thereby improving cybersecurity defenses for or- ganizations and government agencies.

Embracing Predictive Analytics and Statistical Methods for Optimizing Financial Decision-Making in Business Intelligence
Authors:-Harinee R V, Sathya P, Subiksha T, Karthika Devi M M, Sanjana D, Monish P, Dr.R.Suganthi

Abstract-Financial decision-making demands a strategic strategy backed by data-driven insights in the quickly changing company environment. Statistical techniques and predictive analytics have become strong business intelligence tools that help firms improve resource allocation, reduce risks, and improve forecasting accuracy.(1) This study examines how statistical methods and predictive analytics can be combined to improve financial decision-making, with a focus on how they can increase strategic planning and decision accuracy. We illustrate their influence on financial performance optimization by thoroughly examining machine learning models, time series forecasting, regression analysis, and other statistical techniques. In order to demonstrate the usefulness of these approaches in business intelligence, we also go over case studies and real-world applications.(2) This paper explores how businesses can embrace these advanced methodologies to optimize financial decision-making within the broader scope of business intelligence, highlighting both the potential benefits and challenges of implementing these techniques.

IoT Based Industrial Fault Monitoring System
Authors:-Maruti Katke, Pooja Panchal, Shreya Karande, Prof. Puja Johari

Abstract-The goal of the project is to develop a system, which uses Mobile technology that keeps control of the Industrial effects and activities by using Fire, Smoke, Temperature, LDR and PIR sensor. From the sensors quantities are measured and given to the controller (Arduino). Input value will convert the analogical values into digital format and forwards to the controller. The controller will process this information and it will continuously update to server. If the sensors values exceed from the predefined values, then system will give the alert message and information is updated on the server through GPRS (General Packet Radio Service) modem. GPRS (General Packet Remote system) is the technology that underpins most of the world’s mobile phone networks. GPRS is a packet-switching protocol that allows for the transmission of data over cellular networks. Packet-switching is the process of breaking large amounts of data into smaller pieces for transmission across networks GPRS enables always-on internet access, multimedia messages, and other advanced phone features. This system has the fault monitoring capability, which leverages IoT sensors and devices to detect anomalies in machinery and processes. By continuously monitoring parameters such as temperature, vibration, and pressure, these systems can identify potential faults before they lead to significant failures. IoT (Internet of Things) applications in industrial automation allow operators to monitor and control equipment and processes remotely, which can reduce the need for on-site maintenance. This can improve efficiency, reduce costs, and enhance safety.

DOI: 10.61137/ijsret.vol.11.issue2.213

Climate Change Impact on Water Resources: Predictive Modelling with Deep Reinforcement Learning
Authors:-Assistant Professor Amit Kumar Pandey, Ms. Saloni Rathod, Ms. Sejal Gupta

Abstract-Climate change has significantly altered global hydrological patterns, leading to extreme fluctuations in water availability. This study explores the application of Deep Reinforcement Learning (DRL) to develop a predictive model for analysing climate change’s impact on water resources. By leveraging meteorological and hydrological datasets, the proposed model aims to forecast variations in water availability, quality, and demand. The study integrates climate variables such as temperature, precipitation, and humidity to enhance the decision-making capabilities of policymakers and environmental planners. The findings of this research provide a robust framework for sustainable water resource management.

DOI: 10.61137/ijsret.vol.11.issue2.214

Comparative Modal Analysis of Engineering Materials for High-Speed Machining Applications Using CATIA
Authors:-Research Scholar Nakul Bharti, Assistant Professor Neeti Soni, Assistant Professor Dr. Rajesh Rathore, Assistant Professor Praveen Patidar

Abstract-The present research investigates the comparative modal analysis of three different engineering materials – AISI 4140 alloy steel, Ti-6Al-4V titanium alloy, and carbon fiber reinforced polymer (CFRP) – using CATIA software. The study focuses on analyzing and comparing the dynamic characteristics including natural frequencies, modal stress distributions, and displacement patterns for the first ten modes of vibration in critical machine tool components. The analysis was performed on standardized test specimens with identical geometric configurations to establish a direct comparison of material-dependent behavioral patterns in high-speed machining applications. The results demonstrate that Ti-6Al-4V exhibits the highest natural frequencies ranging from 195.823 Hz to 5124.67 Hz across the ten modes, followed by AISI 4140 (171.797 Hz to 4924.49 Hz) and CFRP (98.432 Hz to 2841.31 Hz). The stress analysis reveals maximum values of 1.48E+12 N/m², 1.22E+12 N/m², and 7.14E+10 N/m² for Ti-6Al-4V, AISI 4140, and CFRP respectively at the tenth mode. Displacement patterns indicate that CFRP experiences the highest deflection (890-1240 mm), while Ti-6Al-4V demonstrates the most restricted displacement (312-587 mm). The research provides valuable insights into the dynamic behavior of these materials, facilitating informed material selection for various manufacturing applications and tooling designs. The findings particularly highlight the potential of CFRP as a lightweight alternative in applications where vibration damping and lower stress concentrations are desired. Utilizing CATIA for the component modeling and frequency analysis has enabled precise simulations and detailed insights into the material-specific behaviors under dynamic machining conditions. The findings provide essential data that could influence material selection in modern manufacturing processes. This research contributes to the field by offering a deeper understanding of how material properties affect the dynamic performance of manufacturing components, guiding production engineers in optimizing designs for precision and efficiency in various machining applications.

Token Stacking and Earn Reward System
Authors:-Shubham Gogdani, Harmit Vagadiya, Utsav Padaliya, Dhrumil Karena, Mukesh Patidar

Abstract-This paper introduces a novel approach to decentralized finance (DeFi) through a Token Stacking and Earn Reward System. By utilizing blockchain technology and smart contracts, this system allows users to stack tokens to earn rewards securely and transparently. The process is fully automated, eliminating intermediaries, which improves both efficiency and trust. This research focuses on the architecture, mechanisms, and advantages of such a system, highlighting the significant role of decentralized technologies in the evolution of financial systems.

DOI: 10.61137/ijsret.vol.11.issue2.215

Optimizing Traffic Flow and Congestion Management: An in-Depth Study of Modern Techniques and Technologies
Authors:-Nalla Keerthana, Assistant Professor Mudigonda Harish Kumar

Abstract-Traffic congestion is a persistent issue in urban areas globally, resulting in delays, increased fuel consumption, environmental pollution, and reduced quality of life. This paper examines various traffic flow and congestion management techniques, including traditional methods and modern technologies such as intelligent transportation systems (ITS), adaptive traffic signal control, and real-time data analytics. Through traffic simulation models, empirical data collection, and case studies, this research evaluates the effectiveness of these methods in reducing congestion and improving traffic flow. Results suggest that the combination of smart traffic systems, demand management, and road infrastructure improvements significantly enhances traffic performance.

Automation of Waste Segregation System Using Arduino
Authors:-C. Gnanavel, T. Gopalakrishnan, S. Ajith Arul Daniel, Vijay Ananth Suyamburajan, S. Sivaganesan

Abstract-Waste segregation is crucial for effective waste management, reducing landfill waste, and minimizing environmental hazards. This paper presents an Automated Waste Segregator (AWS) that classifies waste into metallic, wet, and dry categories using a combination of inductive, capacitive, and moisture sensors. The system incorporates an IoT-based alert mechanism to notify authorities when the waste bin reaches capacity. The proposed system offers a cost-effective and efficient solution for household and industrial waste management. Experimental results demonstrate the successful segregation of waste with high accuracy.

University Enquiry for Student Using AI ChatBot
Authors:-Sanchita Barve, Priyanka Shembade, Rutik Deshkhaire

Abstract-A chatbot is a piece of computer software that may initiate conversations between users and other machines. A broader audience can be reached with chatbot technology, which is text-based and secure. Chatbots for scholarly research are developed using AI algorithms that interpret user messages and assess user demands. Without being physically present at the company, the chatbot’s objective is to match user input when answering inquiries. The application responds to the students’ inquiries by using its intelligence. These kinds of applications can be menu-driven, form-based, command line, graphical user interface (GUI), natural processing language, etc. Web-based and TGUI are the two most popular types of user interfaces; however, sometimes another type is required. Conversational user interfaces based on chatbots are useful in this situation. One type of bot that has been utilized in chat systems is the chatbot. Users can interact with them through graphical interfaces, and this is currently the trend. They usually offer a stateful service, meaning that each session’s data is saved by the application. It’s common to be unsure about where to find information on a college’s website. It could be difficult for someone who isn’t a student or employee to locate information about colleges. A chatbot for college questions is the result of fixing these problems. It is a quick, easy, and educational widget that improves user experience on college websites by giving consumers useful information. The chatbot uses artificial intelligence (AI) and natural language processing (NLP) technology to answer user questions. Questions concerning academics, examination cells, admission, users’ attendance and grade point average, placement cells, and other diverse activities are all effectively addressed by this user interface. Chatbots are systems with integrated data that enable them to recognize user inquiries and provide answers. This approach might be an online application that provides the student with the answers to their query. Students prefer using the bot to converse and pose inquiries. After analyzing the user’s inquiry, the program provides an answer. The inquiry is answered by the machine as though it were being asked by someone. With the use of algorithms, the program answers the pupils’ query.

DOI: 10.61137/ijsret.vol.11.issue2.216

Effect of Bonding Strength on the Tensile Performance of Shape Memory Alloy Incorporated Glass Fiber Reinforced Epoxy Composites
Authors:-T. Gopalakrishnan, S. Ajith Arul Daniel, Vijay Ananth Suyamburajan, S. Sivaganesan, C. Gnanavel

Abstract-The tensile performance of Shape Memory Alloy (SMA)-incorporated Glass Fiber Reinforced Epoxy (GFRP) composites depends critically on interfacial bonding strength. This study examines the impact of SMA surface treatments (plasma, acid etching, silane coupling) on Ultimate Tensile Strength (UTS) and Young’s modulus, assessed using ASTM D3039 tensile testing. Results show that silane-coated SMA composites achieved a 33% increase in UTS and a 28% rise in Young’s modulus, with plasma-treated and acid-etched SMA wires also improving stress transfer. SEM analysis revealed that strong bonding reduced fiber pull-out and micro-cracks, while weak bonding led to early failure. These findings confirm that surface treatment enhances stress transfer efficiency, improving mechanical properties for aerospace, automotive, and structural applications.

Investigation of Momordicacharantia Seed Biodiesel with Aluminum Oxide Nano Particle on CI Engine
Authors:-Ruban M, S.Venugopal, S.Jacob, V.S Shaisundaram, Kannan S.P

Abstract-Diesel is the principle source of transportation. It adds to the success of the overall economy since it is broadly utilized because of high combustion efficiency, versatility, unwavering quality, and cost-adequacy. In any case, contamination emanations are a noteworthy disadvantage. Emission from diesel engine are a genuine risk to the earth and are viewed as one of the significant source of air pollution. The demand in biofuels from years made a scope for Momordicacharantia seed oil into biodiesels.Momordicacharantia seed oil having low calorific value contrasted with different biodiesels yet in addition upgraded us in making another alternative utilizing Momordicacharantia seed oil which has lesser emission and improvement in performance. In this exploration work, biodiesel four blends from Momordicacharantia seed oil with Aluminium oxide added substance is investigated in its emission and performance characteristics. The outcomes demonstrated B30 (30% biodiesel, 68% diesel and 2% Aluminium oxide) gives better performance when compared with different blends.

Design and Fabrication of Composite Material in Brake Pads
Authors:-S.Jacob, V.S Shaisundaram, S.Baskar, S.Ramasubramanian, Sathya Narayan M

Abstract-The use of asbestos fiber is being avoided due to its carcinogenic nature that might cause health risks. A new brake pad was produced using banana peels waste to replaced asbestos and Phenolic resin (phenol formaldehyde), as a binder was investigated. The resin was varying from 5 to 30 wt% with interval of 5 wt%. Morphology, physical, mechanical and wear properties of the brake pad were studied. The results shown that compressive strength, hardness and specific gravity of the produced samples were seen to be increasing with increased in wt% resin addition, while the oil soak, water soak, wear rate and percentage charred decreased as wt% resin increased. The samples, containing 25 wt% in uncarbonized lemon, orange and pomegranate peels. The result of this research indicates that lemon, orange and pomegranate peels particles can be effectively used as a replacement for asbestos in brake pad manufacture.

Design, Simulation and Analysis of Floating Rotor Brake Plate
Authors:-Karikalan L, Ruban M, S.Venugopal, S.Jacob, Gokula Krishnan M

Abstract-In an ever-evolving automotive landscape, brake systems play a pivotal role in ensuring vehicle safety and performance. This engineering project takes an extensive look at the design, analysis, and implications of a floating rotor brake plate, a critical component within the disc brake assembly. By examining the detailed design and modeling process, simulation analysis, and experimental testing of the floating rotor brake plate, this project addresses the limitations of traditional solid disc rotors. Key objectives include enhancing thermal management, reducing weight, and simplifying maintenance. The implications of this research extend to improving brake system design, increasing safety and reliability, reducing vehicle weight, and streamlining maintenance procedures, contributing to the automotive industry’s pursuit of safer, more efficient, and high-performance vehicles.

Review on Design of Solar Microgrid and Improvement of its Power Quality
Authors:-Sunil Yadav, Professor Amit kumar Asthana

Abstract-A microgrid, regarded as one of the cornerstones of the future smart grid, uses distributed generations and information technology to create a widely distributed automated energy delivery network. This paper presents a review of the microgrid concept, classification and control strategies. Besides, various prospective issues and challenges of microgrid implementation are highlighted and explained. Finally, the important aspects of future microgrid research are outlined. This study would help researchers, scientists, and policymakers to get in-depth and systematic knowledge on microgrid. It will also contribute to identify the key factors for mobilizing this sector for a sustainable future.

Review on a Novel Approach to Implementation of an Based Solar and Wind Power Generation Hybrid Grid
Authors:-Narendra kumar, Professor Amit kumar Asthana

Abstract-Now a day’s electricity is most needed facility for the human being. All the conventional energy resources are depleting day by day. So we have to shift from conventional to non-conventional energy resources. In this the combination of two energy resources is takes place i.e. wind and solar energy. This process reviles the sustainable energy resources without damaging the nature. We can give uninterrupted power by using hybrid energy system. Basically this system involves the integration of two energy system that will give continuous power. Solar panels are used for converting solar energy and wind turbines are used for converting wind energy into electricity. This electrical power can utilize for various purpose. Generation of electricity will be takes place at affordable cost. This paper deals with the generation of electricity by using two sources combine which leads to generate electricity with affordable cost without damaging the nature balance.

Enhancing Data Interpretation: A Deep Dive into Waterfall, Histogram, Pareto and Box & Whisker Charts
Authors:-Meha Surya K, G.Hari, Mathan Gokul K, Dinesh P R, Professor Dr. R. Suganthi

Abstract-In the era of data-driven decision-making, Impactful visualization techniques are key to gaining meaningful insights from data. This article explores four essential data visualization tools Waterfall, Histogram, Pareto, and Box & Whisker Charts each serving distinct analytical purposes. The Waterfall Chart helps track incremental changes in financial and operational metrics, while the Histogram visualizes data distribution patterns. Applying the 80/20 principle, the Pareto Chart focuses on the most significant factors impacting results to prioritize key contributors in a dataset, and the Box & Whisker Chart provides a statistical summary of data dispersion, including outliers. By understanding the functionalities and applications of these charts, professionals can enhance data interpretation, improve strategic decision-making, and present complex information more effectively. This article offers a comprehensive guide to these visualization techniques, highlighting their real- world applications and comparative advantages in various analytical scenarios.

Unlocking the Power of Data: Transformative Applications of Business Analytics in Commerce
Authors:-Ms.A.Suruthi, Ms.G.Kaviya, Ms.T.Sahana, Ms.R.Prarthana Sherin, Professor Dr.R.Suganthi

Abstract-Making data analysis to predict market trends for goods and services and to improve the operation of enterprise systems is a crucial corporate survival task in today’s cutthroat environment. However, it is becoming more and more clear that real-time data analysis is necessary for business success, and that real-time responses to analysis findings are also necessary to meet the rapidly shifting demands of regulators and customers. After outlining the problems and difficulties with current business intelligence systems, this paper outlines our vision for real-time business intelligence in the future.

Research Paper: The Effect of Temperature on the Attraction of Gravitons in a Hypothetical Framework
Authors:-Sachindra Nath Nisad

Abstract-The theory of gravity, as described by Einstein’s General Relativity, does not include a particle mediator for gravitational forces. However, in attempts to unify quantum mechanics and general relativity, gravitons are postulated as the quantum mediators of gravity. This research presents a theoretical framework in which the attraction of gravitons increases as the temperature of a system increases. We explore how temperature-induced fluctuations in quantum fields could influence the gravitational force mediated by gravitons, potentially offering new insights into the interplay between thermodynamics and gravitational forces at quantum scales.

DOI: 10.61137/ijsret.vol.11.issue2.218

AI-Powered Chatbots for Customer Support: Enhancing User Experience and Efficiency
Authors:-Sachin Patkari, Bhumi Shah

Abstract-The integration of Artificial Intelligence (AI) in customer support has revolutionized the way businesses interact with their customers. AI-powered chatbots have emerged as a pivotal tool in enhancing user experience and operational efficiency. This paper explores the development, implementation, and impact of AI-powered chatbots in customer support. We delve into the technological underpinnings, including Natural Language Processing (NLP) and Machine Learning (ML), that enable chatbots to understand and respond to customer queries effectively. Furthermore, we analyze case studies from various industries to demonstrate the tangible benefits of chatbots, such as reduced response times, improved customer satisfaction, and cost savings. The paper also addresses challenges and future directions, including ethical considerations and the potential for more advanced AI integrations. Our findings suggest that AI-powered chatbots are not only a trend but a necessity for businesses aiming to thrive in the digital age.

Remote Controlled Water Garbage Collector Using Arduino
Authors:-Professor Kadar.S.Tamboli, Suraj.S.Ghaytidak, Makrand.V.Navale, Pratik.H.Patil

Abstract-This project emphasis on design of the river waste collection. Trillions of pieces of plastic currently pollute the seas, rivers, lakes, ocean harming sea life, contaminating ecosystems and making a mess on beaches. Thus It’s important to clean up the plastic in the water, but nobody knows how best to do so yet. These days practically all the assembling procedure is being automized so as to convey the items at a quicker rate. Automation plays an important role in mass production. In this venture we have manufactured the remote worked waterway cleaning machine. Prime objective of our project is to collect all the wastes which are found floating on water bodies and to minimize labor work. These are done by using a hardware prototype and by using an Microcontroller for controlling all parts of a machine by using an smart phone by using Wi-Fi or Bluetooth. We have attempted to meet every one of the destinations to this item fruitful with the end goal that our item gets propelled in the market.

Comparative Analysis of Machine Learning Algorithms for Customer Churn Prediction
Authors:-Riyansh Agiwal

Abstract-Customer churn prediction represents a critical challenge for modern businesses across multiple sectors. This comprehensive study evaluates various machine learning algorithms for predicting customer attrition, comparing traditional approaches like logistic regression with more advanced ensemble methods such as Random Forest and XGBoost. The research demonstrates that while ensemble methods achieve superior accuracy (with XGBoost reaching 91.7% accuracy compared to logistic regression’s 83.4%), their practical implementation requires careful consideration of interpretability, computational efficiency, and business context. The integration of a Tkinter GUI significantly enhances model usability, bridging the gap between technical complexity and practical business application. This paper provides evidence-based guidance for selecting appropriate algorithms based on specific business requirements and available resources.

Transformers: A Review and Use in Text Analytics, Topic Modelling and Summarization
Authors:-Prateek Majumder, Neha Roy Choudhury, Anshuman Jha

Abstract-Automatic text summarization and zero-shot classification are crucial tasks in natural language processing (NLP), aiding in information retrieval, content compression, and text classification. Recent advances in deep learning and transformers have significantly improved the accuracy and efficiency of these tasks. This study evaluates multiple state-of-the-art transformer-based models for text summarization, including Google’s T5, PEGASUS, Facebook’s BART, and Longformer Encoder-Decoder (LED). We assess their performance using the ROUGE and BERTScore metrics to determine their effectiveness in generating concise and contextually accurate summaries. The T5 model, pre-trained on C4, achieves state-of-the-art results on many NLP benchmarks while being flexible enough to be fine-tuned to a variety of important downstream tasks [1]. Also, zero-shot classification with the facebook/bart-large-mnli model is considered in this work, with no training labels beforehand for classification of text into predefined categories. Classification accuracy for a variety of domains, including Politics, Sport, Technology, Entertainment, and Business, is considered in analysis. To classify even more precisely, a corpus with labels is fine-tuned with the BART model and improvement in prediction accuracy and loss over a range of training runs measured. Zero-shot classification, useful for general categories, is seen to have improvement room in specific domains for classification. Classification with fine-tuning of the BART model reduces evaluation loss but comes with hyperparameter search and a larger corpus for even heightened accuracy. Traditionally, zero-shot learning (ZSL) most often referred to a fairly specific type of task: learn a classifier on one set of labels and then evaluate on a different set of labels that the classifier has never seen before [2].

DOI: 10.61137/ijsret.vol.11.issue2.221

Artificial Intelligence in Cybersecurity Threat Detection
Authors:-Vaibhav Trivedi, Professor Bhoomika B. Chauhan

Abstract-The rapid growth of digital technologies has led to an exponential rise in cyber threats, making traditional security measures inadequate in combating sophisticated cyberattacks. Artificial Intelligence (AI) has emerged as a crucial technology in cybersecurity, offering enhanced threat detection through machine learning, deep learning, and behavioral analytics. AI-driven cybersecurity solutions can analyze vast datasets in real time, detect anomalies, predict potential attacks, and automate threat mitigation. By leveraging AI, organizations can strengthen their defense mechanisms against evolving cyber threats, including ransomware, phishing, and advanced persistent threats (APTs). This paper delves into the role of AI in cybersecurity, focusing on its applications in real-time threat detection, anomaly identification, and predictive analytics. Additionally, it examines the advantages, challenges, and future trends in AI-driven cybersecurity, emphasizing the importance of integrating AI with other security technologies to create a robust defense ecosystem. The findings suggest that AI-powered cybersecurity solutions significantly enhance security resilience, but ethical concerns, adversarial AI attacks, and implementation challenges must be addressed for widespread adoption.

DOI: 10.61137/ijsret.vol.11.issue2.222

Artificial Intelligence and its Scope with Special Reference to the Field of Education
Authors:-Anjani Jani, Dhaval Nathji, Ankita Hemnani, Professor Kashyap Dave

Abstract-Artificial Intelligence (AI) is revolutionizing education by introducing intelligent tutoring systems, automating administrative tasks, and enhancing personalized learning experiences. This study explores the advantages, challenges, and potential consequences of AI integration within educational settings. AI-driven technologies, such as virtual tutors and adaptive learning platforms, cater to the unique needs of students, leading to increased engagement and improved outcomes. Additionally, AI aids educators by streamlining administrative responsibilities and grading, allowing them to dedicate more time to instruction.

Enhancing Customer Relationship Management in the Automobile Industry: A Data-Driven Approach
Authors:-Kevin J Shah, Bhumi Kaushal Shah

Abstract-Customer Relationship Management (CRM) has revolutionized the automobile industry by optimizing customer interactions, enhancing service efficiency, and driving customer retention. This paper explores a structured CRM model tailored for automobile dealerships and service centers, emphasizing data-driven decision-making. The research outlines lead management, booking processes, service tracking, and customer retention strategies, demonstrating the impact of CRM automation and analytics.

Design and Implementation of a Customer Relationship Management (CRM) System for the Automobile Industry
Authors:-Rajgiri Y. Goswami, Gayathri Devraj Naidu

Abstract-Customer Relationship Management (CRM) systems play a crucial role in enhancing customer satisfaction, retention, and overall business growth. This paper presents a comprehensive design and implementation framework for a CRM system tailored for the automobile industry. The proposed system integrates lead management, booking management, service history tracking, feedback processing, customer retention strategies, and real-time reporting. The objective is to provide a centralized platform that improves customer engagement, streamlines operations, and delivers actionable insights to the business. The system’s architecture, data flow, user interface design, and performance evaluation are discussed in detail. This research highlights the benefits of automation and predictive analysis in customer relationship management for the automobile sector.

DOI: 10.61137/ijsret.vol.11.issue2.223

IoT-Based Automation and Window Monitoring System
Authors:-Nilesh Babulal Choudhary, Harshal Kisandeo Bhabad, Danish Nurrodin Shaikh, Pratik Gopal Tiwari, Professor Sanjiwani Deshmukh

Abstract-This paper presents IoT-Based Automation and Window Monitoring System development of a smart system designed to automate lighting and fan control based on occupancy detection, while simultaneously monitoring windows and doors for security purposes. The system uses Passive Infrared (PIR) sensors to detect human presence, automatically switching lights and fans on or off depending on whether a room is occupied. Magnetic or Limit switches are used to monitor the status of windows and doors, triggering an alert if they are left open beyond a specified time frame. For enhanced user convenience, notifications are sent directly to the user’s mobile device via a Wi-Fi connection, allowing real-time monitoring and control of the system from anywhere. The core of the system is built around a microcontroller (ESP32), which is responsible for managing the sensor inputs and controlling relays connected to the household appliances. The system is designed to optimize energy usage by preventing unnecessary operation of lights and fans, contributing to reduced electricity consumption. Additionally, by monitoring windows and doors, the system improves home security by notifying the user of potential security breaches. This project combines cost-effective hardware components with reliable software to create a user-friendly system that enhances both convenience and safety. It addresses the growing need for automated, energy-efficient home solutions that integrate security features, making it especially beneficial for homeowners seeking to improve their home environment with minimal effort.

Beyond Aesthetics: Analyzing the Impact of UX Design and Mobile Optimization on Online Jewellery Sales
Authors:-Sharma Lokesh. V

Abstract-The rapid growth of e-commerce has led to a transformation in the jewelry industry, where traditional brick-and-mortar stores are increasingly being replaced by online platforms. This paper explores the design, development, and implementation of an online jewelry website, with a focus on enhancing user experience, product visualization, and secure transactions. The project aims to create an intuitive, user-friendly platform that offers a wide range of jewelry products, including rings, necklaces, earrings, and bracelets, with detailed descriptions, images, and pricing. By incorporating features such as personalized recommendations, virtual try-ons, and secure payment gateways, the website will cater to the growing demand for convenient and reliable online shopping experiences. Additionally, the research will examine the importance of visual appeal, website navigation, and mobile responsiveness in attracting and retaining customers. The findings from this project highlight key challenges and best practices in building a successful online jewelry business, offering valuable insights into e-commerce trends and consumer behavior in the jewelry sector.

Enhancing Law Enforcement with Smart Glasses: A Comprehensive Investigation into Real-Time Criminal Detection Using AI and Machine Learning
Authors:-Bommireddipalli Tejaswi Bharadwaj, Siddharth Singh, Priyanshu Kumar

Abstract-Law enforcement agents can anticipate smart glasses as the next wearable technology evolution which provides innovative and enhanced crime detection capabilities. The paper develops an Intelligent Criminal Detection System (ICDS) which uses smart glasses together with artificial intelligence (AI) and machine learning (ML) features for system deployment. The system development provides police forces with better capabilities to record evidence in real time through improved facial recognition functionality. Fast suspect recognition becomes achievable through the ICDS because of Viola-Jones algorithm integration with OpenCV and YOLOv8 technology which maintains excellent performance in dynamic busy surveillance areas. This paper investigates smart glass engineering dynamics together with their contemporary implementation by police departments to understand their substantial impact on current law enforcement methods. The research evaluates devastating consequences of smart system deployment which includes privacy breaches and data security vulnerabilities together with subjects being misidentified incorrectly. The system architecture identifies the process in which smart glasses interface with police databases and current data systems. The system performance evaluation takes place in simulated environments through tests that verify its operational efficiency against conventional practices by assessing speed accuracy and dependability. This paper investigates smart glass deployment activities from real police departments at Dubai Police and New York Police Department to establish their usage in investigative functions. Research development will follow two main paths according to the paper including the improvement of real-time feedback algorithms alongside affordable systems and ethical frameworks to gain public trust. This paper includes recommended legislation which promotes appropriate use of smart glasses during law enforcement operations alongside the specified protocols. The study combines modern technology with practical field experience to expand criminal detection system knowledge about smart glass deployment.

DOI: 10.61137/ijsret.vol.11.issue2.224

Automatic Vehicle Licence Plate Recognition System Using CNN
Authors:-Assistant Professor Mr.D. Muneendra, M S Kamalesh, Rangannagari Kartheek

Abstract-This paper presents an efficient and robust method for license plate detection and recognition using a machine learning approach. The proposed system follows a three-stage process: image pre-processing, candidate extraction, and license plate verification. Initially, the input image undergoes grayscale conversion and edge detection using an extended Sobel operator. Adaptive thresholding and a novel line density filter are then applied to accurately extract potential license plate regions. A Convolutional Neural Network (CNN)-based classifier is employed to distinguish true license plates from non-license plate regions. The system is trained on diverse datasets to enhance detection accuracy under varying illumination and environmental conditions. Experimental results demonstrate the effectiveness of the proposed method in achieving high accuracy and reliability in real-time license plate recognition.

Leveraging AI for a Voice-Controlled E-Commerce Platform for the Visually Impaired
Authors:-Assistant Professor Ms.P.Priya, Sangeetha S, Jeya Harshini, Mehar Jameera T, Yoga Meenakshi

Abstract-This paper presents a novel voice- controlled e-commerce platform tailored for visually impaired users. The proposed system integrates advanced artificial intelligence (AI) techniques, including state-of-the-art speech recognition, natural language processing (NLP), and machine learning algorithms, to enhance accuracy, personalization, and overall user experience. Unique workflow diagrams illustrate the operational flow of the system as well as the specific AI modules that power contextual understanding and command execution. Experimental evaluations indicate that the integration of AI leads to significant improvements in accessibility navigation, making digital commerce more inclusive.

DOI: 10.61137/ijsret.vol.11.issue2.225

The Data Aesthete’s Compendium: Crafting Insights from the Digital Archive
Authors:-Surekaa J T, Praveen K, Suji Harini S, Dinesh Babu S, Dr. Suganthi R.

Abstract-The Data Aesthete’s Compendium: Crafting Insights from the Digital Archive explores the intersection of data science and artistic expression in interpreting digital archives. It encourages a shift from viewing data as a technical asset to recognizing it as a narrative tool. The journal focuses on transforming raw data into meaningful, engaging insights through creative and analytical approaches. Ethical considerations, including accuracy, representation, and privacy, are central to its mission. It highlights the aesthetic potential of data visualization, blending functionality with artistry. Interdisciplinary collaboration between data science, design, and social studies is emphasized to offer a holistic view. The journal also examines the future impact of emerging technologies like AI and machine learning on data interpretation. Ultimately, it aims to inspire a more thoughtful, human-centered approach to data analysis and presentation. The goal is to unlock the transformative power of data through both analytical precision and creative insight.

Growth and Development of Entrepreneurship: A Study of Self Help Groups in Mizoram State of India
Authors:-Assistant Professor Dr. T.H. Lalrokhawma

Abstract-Entrepreneurs play an important role in the development of a nation. Entrepreneurship helps improve the per capita income of a country by generating new job opportunities. It plays a significant role in increasing Gross National Product. As the GNP grows, the per capita income (PCI) also rises, leading to enhanced economic well-being for the population. Entrepreneurs in the rural parts of India are vital for checking the rate of migration of people to bigger towns and cities as they create opportunities for the rural communities. This study focus on the various factors influencing the growth and development of entrepreneurship by studying the self-help groups within Kolasib district of Mizoram state, India. The study analyse primary and secondary data collected from the respondents through structured questionnaire and personal interview. It adopts statistical tools like correlations, reliability test and factor analysis to find out the factors responsible for bringing this positive changes. The study found that these factors can be classified into three categories –internal support, eternal support and personal traits. The study also suggests the government to disclose various central and state schemes for the greater development of entrepreneurship within the state.

DOI: 10.61137/ijsret.vol.11.issue2.226

Innovation for Sustainability: Tech’s Contribution to a Greener Future
Authors:-Nisha, Devavarnani, Agalya S, Harismitha Y M, Professor Dr. R. Suganthi

Abstract-Innovative technology is becoming more necessary when it comes to sustainability and solving global environmental issues. “Tech for the Planet: Digital Innovations in Sustainability” analyzes how the development of digital technologies is changing sustainability efforts in different industries and ecosystems. The study investigates the uses of artificial intelligence (AI), the Internet of Things (IoT), blockchain, and data analytics in achieving resource optimization, environmental footprint minimization, and climate change adaptation enhancement. These technologies support smart planning and sustainable practices by enabling precise monitoring and transparent decision-making, as well as real-time data accessibility for stakeholders. The abstract focuses on real case studies and demonstrates how different digital innovations transform the energy, agricultural, transportation, and waste management industries. It also looks at important issues like digital divide, privacy issues of data, and the carbon emission of digital frameworks. The study advocates for a multi-stakeholder effort and emphasizes the role of governments, businesses, and communities in addressing technology’s impact on planetary health. This document calls attention to how digital innovations can facilitate a desirable transition toward a sustainable world while promoting necessary policies and actions.

DOI: 10.61137/ijsret.vol.11.issue2.227

Distributed Incremental Adaptive Filter Controlled Grid Interactive Residental Photovoltaic Battery Based Microgrid for Rural Electrification
Authors:-T. Gandhimathi, Associate Professor Dr.S.Pradeep

Abstract-This paper proposes the coordinated control of a hybrid AC/DC power system with renewable energy source, energy storages and critical loads. The hybrid microgrid consists of both AC and DC sides. A synchronous generator and a PV farm supply power to the system’s AC and DC sides, respectively. A bidirectional fully controlled AC/DC converter with active and reactive power decoupling technique is used to link the AC bus with the DC bus while regulating the system voltage and frequency. A DC/DC boost converter with a maximum power point tracking (MPPT) function is implemented to maximize the energy generation from the PV farm. Current controlled bidirectional DC/DC converters are applied to connect each lithium-ion battery bank to the DC bus. Lithium-ion battery banks act as energy storage devices that serve to increase the system stability by absorbing or injecting power to the grid as ancillary services. The proposed system can function in both grid-connected mode and islanding mode.

DOI: 10.61137/ijsret.vol.11.issue2.228

Math Handwriting Conversion Using AI Approach: A Lighter Alternative to Scanning
Authors:-Hien Nguyen Phuoc, Nghia Cao Trong, Linh Nguyen Hoang Anh

Abstract-Recent advances in artificial intelligence have revolutionized the way we digitize handwritten mathematical expressions, offering efficient alternatives to traditional scanning methods. These AI-driven approaches allow for real-time conversion of handwritten math to beautifully rendered equations, making them particularly valuable for students, teachers, researchers, and professionals who frequently work with mathematical notation.

Decoding Deception: A Machine Learning Approach for Detecting and Analyzing Fake News
Authors:-Y Suma Chamundeswari, Pammi Manikanta Pavan Kumar, Gidituri Jayaram, Vurigiti Sai Rohith Yadav, Yellamilli David Branham

Abstract-The spread of fake news has become a significant concern in today’s society, as misleading information can easily damage reputations and lives. To address this issue, researchers have developed fake news detection systems using machine learning techniques. The identification of fake news is rapidly gaining traction and is increasingly being adopted by various industries, either for their own use or to offer as a service to others. Machine learning (ML) and deep learning (DL) are two prominent approaches employed to determine the authenticity of news. There are various methods available for detecting false news through both ML and DL techniques. This paper presents a comprehensive analysis of fake news detection using machine learning approaches. Upon thorough examination, it was found that several ML and DL algorithms have been applied in this domain, with the Support Vector Machine (SVM) being the most commonly used ML method, and Long Short-Term Memory (LSTM) being the most widely applied DL technique.

DOI: 10.61137/ijsret.vol.11.issue2.229

Intelligent Loan Risk Assessment: A Machine Learning Framework for Personalized Credit Evaluation
Authors:-Ch. Veera Gayathri, Nurukurthi Sirisha Kumari, Yarramsetti Prasanna, Donipati Sravani, Yellamilli Joseph Branham

Abstract-Banks are essential to the global financial system, and one of their primary sources of income comes from loan interest. However, if borrowers fail to repay these loans, it can turn profits into substantial losses, highlighting the importance of assessing the risk of default before approving a loan. Machine learning techniques can be an effective method for quickly and accurately evaluating whether a credit risk should be approved. This study explored six machine learning models—Decision Tree, Random Forest, Support Vector Machine (SVM), Multi-layer Perceptron (MLP) Artificial Neural Network, Naive Bayes, and a stacking ensemble model—to predict the credit risk associated with a loan. Using a dataset of twenty factors typically found in loan applications, the stacking ensemble model achieved the highest accuracy at 78.75%. The Random Forest model, though slightly less accurate at 78.15%, was more efficient while yielding comparable results. Key factors such as credit amount, account status, age, loan duration, and loan purpose were identified as the most influential indicators of credit risk. The findings of this research further support the efficacy of machine learning models for predicting loan default risk.

DOI: 10.61137/ijsret.vol.11.issue2.230

Edge AI for Disaster Management in Smart Cities
Authors:-Manoj Gautam Tenkale

Abstract-As urbanization continues to expand, smart cities face growing risks from both natural and human-induced disasters. Conventional disaster management systems predominantly rely on centralized cloud computing, which can lead to delays, bandwidth limitations, and reduced reliability, particularly in emergency situations. Edge AI presents an innovative solution by enabling real-time data pro-cessing at the edge of the network, minimizing response time and enhancing decision-making during crises. This paper examines how Edge AI can improve disaster preparedness, detection, and response in smart cities. Through the integration of edge computing, IoT sensors, and AI-driven models, hazards can be detected in real time, predictive analytics can anticipate potential threats, and emer-gency actions can be executed autonomously—without requiring constant cloud connectivity. Case studies focusing on flood forecasting, earthquake monitoring, and fire detection illustrate the practical benefits of Edge AI in disaster mitigation. Additionally, the paper explores key challenges, including computational constraints, cybersecuri-ty risks, and the complexities of integrating Edge AI with existing urban infrastructure. The findings suggest that, when combined with IoT and 5G technology, Edge AI provides a robust, scalable, and efficient framework for disaster management. This approach ensures faster, local-ized, and more reliable emergency responses, enhancing resilience in modern smart cities.

Unveiling Energy Insights: An Explainable AI-Driven Framework for Precision Household Consumption Forecasting
Authors:-K. Srikanth, Atthuluri Lahari Prathyusha, Kanaparthi Jyothi Sravani, Vittanala Aswitha, Botta Durga Sanjay

Abstract-Effective energy management is essential for promoting sustainability, reducing carbon emissions, conserving resources, and cutting costs. However, traditional energy forecasting methods often fall short in terms of accuracy, indicating a need for more advanced solutions. Artificial intelligence (AI) has emerged as a valuable tool for energy forecasting, but its lack of transparency and interpretability makes it difficult to understand its predictions. To address this, Explainable AI (XAI) frameworks have been developed to enhance the transparency and interpretability of AI models, particularly those considered “black-box” models. This paper examines household energy consumption predictions by comparing various forecasting models using evaluation metrics such as the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). After testing with unseen data, the best-performing model is selected, and its predictions are explained through two XAI techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These methods help identify key factors influencing energy consumption forecasts, such as current consumption patterns and previous energy usage. The study also highlights the importance of XAI in developing predictive models that are both reliable and consistent.

DOI: 10.61137/ijsret.vol.11.issue2.231

MindScope: A Comprehensive Review of Mental Health Assessment via Social Media Using Machine Learning and Deep Learning

Authors:-N.V.S. Sowjanya, Penjerla S S N V M Sri Raj Kumar, Kanakala Tanuja Nirmala Gnaneswari, Suravarapu Dharani Sri, Geddam Hema Alekhya, Karam Mohitha Bramarambika

Abstract-Artificial intelligence holds significant potential to revolutionize healthcare. Machine learning (ML) and deep learning (DL) techniques have been increasingly utilized for predicting and diagnosing a wide range of diseases. In addition, social media platforms such as Twitter, Facebook, and Reddit have become popular outlets for individuals to share their emotions and experiences. Following the COVID-19 pandemic, mental health concerns have escalated, prompting numerous studies that apply ML and DL models to analyses social media data for predicting mental health issues. This research aims to offer an in-depth review of the ML and DL algorithms applied to the prediction of various mental health disorders. It presents an extensive overview of 37 research papers, analysing and compiling a table of the accuracy of these algorithms across four key mental health conditions: Depression, Anxiety, Bipolar Disorder, and ADHD. The study is intended to serve as a foundational resource for future researchers and practitioners, offering insights into the performance of different ML and DL approaches. Additionally, it includes a compilation of publicly available datasets, providing valuable resources for ongoing research in this area.

DOI: 10.61137/ijsret.vol.11.issue2.232

GaitAI: Cutting-Edge Machine Learning for Biometric Gait Recognition and Analysis
Authors:-M. V. Rajesh, Putsala Pujitha, Pothula Mohana Surya Kumari, Guttula Naveen Sagar, Veesam Vamsi, Dara Prudhvi Narayana

Abstract-This study offers a comprehensive investigation into the field of gait recognition in biometric analysis, focusing on the specific challenges associated with using gait as a biometric feature. The research evaluates various machine learning (ML) algorithms, including Individual Node Evaluation, Statistical Inference, Regression Modelling, Support Vector Machines, Nearest Neighbour Classification, Decision Tree Structures, Random Forest Ensembles, and Multilayered Neural Networks. Thorough testing is conducted to assess the performance of each model in accurately identifying individuals based on their unique gait characteristics. The approach emphasizes extensive preprocessing to maintain data quality and relevance. Additionally, Sequential Backward Selection (SBS) is employed for feature selection, along with dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which improve the model’s focus on key features. The research also investigates deep learning models, analysing different architectures to assess their effect on gait recognition accuracy. A detailed comparative analysis evaluates the advantages and limitations of each method, providing valuable insights for the field. By exploring a variety of ML and DL approaches, this study sets a benchmark for future developments in biometric security. It highlights the potential of gait recognition as a reliable, non-invasive identification method, paving the way for the creation of more advanced and precise biometric systems that are crucial for the evolving needs of security and personal identification.

DOI: 10.61137/ijsret.vol.11.issue2.233

IntelliMaint: AI-Driven Predictive Maintenance and Performance Optimization for Mechanical Systems
Authors:-V. Suvarna, Thalisetty Anusri, Kolli Satya Surya Teja, J Jyothisai, Gayatri Chakrani Palla, Malladi Venkatesh

Abstract-To enhance the accuracy of predictions and enable real-time monitoring of mechanical parts’ operational status, a deep learning model was initially developed using a convolutional neural network (CNN) structure to extract features from the mechanical components. Subsequently, another deep learning model was designed to process these extracted features through a fully connected layer for data fusion and classification, facilitating the prediction of lifespan and monitoring of health status. This trained model was then integrated with a monitoring system, creating a comprehensive solution for predicting the lifespan and tracking the health of mechanical parts. Finally, the system underwent continuous optimization and updates to improve both its prediction accuracy and real-time responsiveness, while also adapting to various operating conditions and environmental factors. The results demonstrated that the deep learning model achieved a mean absolute error (MAE) of 2.1, a root mean square error (RMSE) of 2.5, and a mean absolute percentage error (MAPE) of 10%, reflecting strong performance. This approach holds significant potential for practical application in the engineering field.

DOI: 10.61137/ijsret.vol.11.issue2.234

AutoRCuff: CNN-Autoencoder-Based Intelligent Detection of Rotator Cuff Tendon Tears from Ultrasound Imaging
Authors:-Y. Suma Chamundeswari, Vella Anusha, Yaramati Lakshmi Satya Sri, Akula Deepika, Lokesh Kumar Boora, Yamana Sri Sai Raghunandan

Abstract-Rotator cuff muscle tears are among the most prevalent musculoskeletal injuries, and ultrasound imaging serves as an effective diagnostic tool. However, the interpretation of these scans requires specialized expertise, often leading to significant delays in diagnosis. This study introduces an AI-driven approach to accelerate the detection of full-thickness rotator cuff tears, reducing assessment time from months to mere minutes. The proposed method consists of two key steps: first, segmentation of the humeral cortex and subacromial bursa, followed by classification of tears based on these identified regions. Automated segmentation in ultrasound imaging poses challenges due to speckle noise, low contrast, and image artifacts. To overcome these, we employ a CNN-based autoencoder that directly predicts the boundary contour points of relevant anatomical structures instead of traditional pixel-wise semantic segmentation. This approach enhances interpretability by focusing on clinically significant landmarks rather than relying on a black-box classifier. The study utilized a dataset of 206 patients, comprising 10,080 training images and 2,520 evaluation images. The proposed segmentation model outperformed the conventional UNet, achieving a Dice coefficient of 94.2% and a Hausdorff Distance of 2.8 mm, compared to UNet’s 90.5% DC and 6.8 mm HD. Following segmentation, a VGG-16-based classification model achieved an accuracy of 81.0%, with a sensitivity of 78.5% and specificity of 76.2%. The implementation of AI-powered ultrasound for rotator cuff tear detection has the potential to facilitate early and precise diagnosis, significantly improving patient outcomes. This automated system can be deployed in primary care settings such as general practitioner clinics and emergency departments, empowering lightly trained personnel to perform initial assessments efficiently.

DOI: 10.61137/ijsret.vol.11.issue2.235

Overview of Cloud-Based Custom Objects in ERP Cloud Implementation
Authors:-Uma Maheswara Rao Ulisi

Abstract-Enterprise Resource Planning systems are critical for streamlining business processes, but the increasing complexity of modern enterprises demands greater flexibility and customization. Cloud-based custom objects have emerged as a transformative solution in ERP implementation, offering enhanced scalability, adaptability, and integration capabilities. This paper explores the role of cloud-based custom objects in ERP systems, examining how they enable organizations to tailor ERP solutions to specific business needs without compromising system integrity or performance. By leveraging cloud infrastructure, companies can efficiently create, modify, and manage custom objects, ensuring better alignment with dynamic business environments. Through a case study analysis and a review of current best practices, this paper highlights the benefits, challenges, and potential impacts of adopting cloud-based custom objects within ERP frameworks. The findings demonstrate that, when implemented strategically, cloud-based custom objects can significantly improve the agility and effectiveness of ERP systems, ultimately contributing to enhanced operational efficiency and business success.

DOI: 10.61137/ijsret.vol.11.issue2.236

Abstract-Financial decision-making demands a strategic strategy backed by data-driven insights in the quickly changing company environment. Statistical techniques and predictive analytics have become strong business intelligence tools that help firms improve resource allocation, reduce risks, and improve forecasting accuracy. This study examines how statistical methods and predictive analytics can be combined to improve financial decision-making, with a focus on how they can increase strategic planning and decision accuracy. We illustrate their influence on financial performance optimization by thoroughly examining machine learning models, time series forecasting, regression analysis, and other statistical techniques. In order to demonstrate the usefulness of these approaches in business intelligence, we also go over case studies and real-world applications. This paper explores how businesses can embrace these advanced methodologies to optimize financial decision-making within the broader scope of business intelligence, highlighting both the potential benefits and challenges of implementing these techniques.

Recycling War-Damaged Structures: Sustainable Use of Waste Aggregates in Concrete Mix Design
Authors:-Arnold D. Velasquez, Maria Fe Y. Lacsado

Abstract-Waste from construction and demolition—especially in post-conflict reconstruction—had severe implications for resources and the environment. This research used Waste Demolished Aggregate (WDA) as a replacement for natural aggregates to examine its sustainability in concrete mixtures, assessing workability and compressive strength with varying replacement percentages. The study correlated experimental results from three concrete mixing batches to provide a comprehensive overview of the impact of WDA on concrete functionality. Overall, it demonstrated how such a strategy can maximize environmental protection by minimizing landfill waste and the need for virgin aggregates, which brings economic benefits through such reconstruction. Minimal differences in workability were observed across the three batches based on concrete mixing slump test results. The first batch slump ranged from 75mm to 80mm, the second batch from 79mm to 80mm, and the third batch from 79mm to 82mm. However, at higher WDA percentages, there was a slight difference in workability and slump. The trend observed in compressive strength tests for the three batches indicated a declining pattern of strength with higher WDA content, consistent with the literature, which attributed this observation to the porous structure and irregular shape of WDA particles. The statistical testing demonstrated a significant reduction in strength at both the 25% and 50% WDA replacement levels. The findings indicated that 25% WDA and 50% WDA resulted in a reduction of compressive strength of approximately 23% and 31%, respectively, compared to natural aggregates. These results had significant implications for the use of WDA in structural applications. Although non-structural applications for WDA such as drainage systems, pathways or non-load-bearing walls suggest potential, WDA may well have limited application at best in structural concrete without further adjustment of the mix to overcome strength losses.

DOI: 10.61137/ijsret.vol.11.issue2.237

Survey Paper on Air Quality Monitoring Systems
Authors:-Jagruti Vinod Patil, Khushi Anchanesh Kharche, R.V.Patil-Kalambe

Abstract-Air Quality Monitoring (AQM) systems play a crucial role in assessing environmental conditions, protecting public health, and aiding government policies. This paper presents a comprehensive survey of AQM systems, including their evolution, the latest sensor technologies, and the integration of the Internet of Things (IoT). It also covers the significance of the Air Quality Index (AQI), real-world applications, global case studies, challenges, and future trends like Artificial Intelligence (AI) and low-cost sensors. Continuous innovation in AQM systems is necessary to ensure cleaner air and a healthier environment.

The Future of Quantum Computing in Cybersecurity: Opportunities and Threats
Authors:-Dr. Sonam Arvind Singh, Dhriti Jain

Abstract-Quantum computing is a revolutionary technology that has the potential to transform multiple industries, including cybersecurity. While it offers promising advancements in computing power, it also threatens conventional encryption methods that safeguard sensitive data. Cryptographic algorithms such as RSA and ECC, which form the backbone of modern digital security, may become obsolete as quantum computers develop the ability to crack them. This paper explores the risks associated with quantum computing in cybersecurity, discusses quantum-resistant cryptographic solutions, and examines strategies for transitioning toward a post-quantum security framework. The study also evaluates the role of government, industries, and researchers in mitigating potential cybersecurity threats.

Role of Assistive Technology to Enhance Learning and Participation in Inclusive Education
Authors:-Sujash Kumar Mandal, Indrani Ruidas, Assistant Professor Dr. Laxmiram Gope

Abstract-Assistive technology plays a crucial role in enhancing the learning experiences and outcomes for physically challenged and disabled pupils, thereby leveraging educational opportunities tailored to their specific levels of disability. This paper emphasizes the importance of assistive technological solutions that facilitate participation in inclusive education, highlighting how innovative technologies can contribute to mainstreaming education and fostering societal integration for special-category learners. By providing tools such as software, adaptive devices, and personalized learning applications, assistive technology supports academic achievement and promotes independence and agency among students with disabilities. Integrating these technologies into educational settings is increasingly essential for creating an equitable learning environment where all students can thrive optimally, ensuring that barriers to education are minimized and that every learner can succeed.

DOI: 10.61137/ijsret.vol.11.issue2.238

The Power of Business Analytics: Definition, Techniques, and Challenges
Authors:-Greeshma Muraly

Abstract-In recent decades, business analytics has become widely utilized across various industries, significantly contributing to the enhancement of enterprise value. With the rapid progress in science and technology during the Big Data era, business analytics methods have been evolving quickly. Consequently, this paper summarizes the definition, techniques and challenges of business analytics based on current research paper.This paper also highlights the existing challenges in the field and identifies open research areas that warrant further attention. All research papers were sourced from the Web of Science and Google Scholar databases and selected according to specific criteria. This paper aims to provide valuable insights for researchers in business analytics by presenting the most recent techniques, diverse applications, and future research directions.

DOI: 10.61137/ijsret.vol.11.issue2.239

Asynchronous Javascript Programming
Authors:-Ravi Gosai, Professor Kashyap A. Dave

Abstract-JavaScript, with its rich feature set of being dynamic, interpreted, object-oriented, and first-class functions, has gained immense fame in the last few years. JavaScript adopts an event-driven/custom I/O non-blocking model which technically translates to higher performance for application work through Node.js. In order to use these behaviors, various design patterns implementing asynchronous programming for JavaScript have emerged. Unfortunately, it is terribly difficult to choose a right pattern and implement a good asynchronous source code, which leads to the application being strongly robustless and source code of poor quality. Building from our previous work with exception handling code smells for JavaScript, the current study is aimed at exploring the impact of three asynchronous programming patterns in JavaScript on the quality of source code and application.

DOI: 10.61137/ijsret.vol.11.issue2.240

Optimizing Cloud Workloads with Azure AI and Machine Learning for Cost and Performance Efficiency
Authors:-Waseem Mansoor

Abstract-Cloud computing has revolutionized enterprise operations by offering scalability and flexibility. However, optimizing cloud workloads for cost efficiency and performance remains a challenge. This paper explores how Microsoft Azure’s AI and machine learning (ML) services can enhance workload optimization. We discuss the integration of AI-powered tools like Azure Machine Learning, Cost Management + Billing, and Auto scale to reduce operational expenses and improve efficiency. This research presents case studies demonstrating AI-driven cost savings and performance gains. A comparative analysis of traditional vs. AI-driven cloud optimization methods is also included.

Survey Paper on Smart Wardrobe System Using Artificial Intelligence
Authors:-Assistant Professor Dipali Mane, Nikita Nalawade, Anupama Yadav, Sanket Tawari

Abstract-The Smart Wardrobe aims to revolutionize the way users manage their clothing collections and make outfit decisions. In today’s fast-paced fashion environment, individuals often struggle with organizing their wardrobes effectively and selecting appropriate outfits for various occasions. This web application leverages modern web technologies and the Internet of Things (IoT) to provide users with a seamless, interactive platform for managing their clothing items and creating personalized outfit combinations. The application features a user-friendly interface that allows users to upload details of their clothing items, including categories, sizes, colors, and images. By utilizing advanced filtering and sorting options, users can quickly access their wardrobe inventory and discover new outfit combinations based on their preferences and upcoming events. Additionally, the Smart Wardrobe Web App incorporates a recommendation system that suggests outfits tailored to users’ tastes, weather conditions, and social occasions. This project not only promotes efficient wardrobe management but also encourages sustainable fashion practices by helping users make the most of their existing clothing items.

PDF Malware Detection: Toward Machine Learning Modeling With Explainability Analysis
Authors:-P. Ahalya Sri Chandana, I.H.N.V.P. Manikanta, V. Venkat Mahesh, G. Naveena, P. Anusha, Ms.A. Harini

Abstract-The Portable Document Format (PDF) is a widely used file type that has become a target for fraudsters who embed harmful code to compromise users’ systems. Traditional detection techniques often fall short in effectively identifying PDF malware due to its versatile nature and reliance on a limited set of features. This work aims to enhance PDF malware detection through the development of a comprehensive dataset consisting of 15,958 PDF samples, encompassing benign, malicious, and evasive behaviors. We utilize three established PDF analysis tools—PDFiD, PDFINFO, and PDF-PARSER—to extract significant characteristics from these samples. Additionally, we derive various features proven effective in classifying PDF malware. An efficient and interpretable feature set is constructed through rigorous empirical analysis of the extracted and derived features. We evaluate several baseline machine learning classifiers, achieving a notable accuracy improvement of approximately 2% with the Random Forest classifier using the selected feature set. Furthermore, we enhance model explainability by generating a decision tree that provides rules for human interpretation, showcasing the effectiveness of Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, and SVM with Optimal Hyperparameters in the context of PDF malware detection.

A Comprehensive Study on Tourism Management Systems: Trends, Challenges, and Future Opportunities
Authors:-Manish Kailas Sangale, Assistant Proffesor Minal Patil

Abstract-The tourism industry has witnessed tremendous boom in current years, driven through globalization, technological improvements, and increasing consumer demand for seamless journey reports. This paper offers a complete have a look at at the improvement and implementation of a Tourism Management System (TMS), a web-based totally utility designed to streamline journey-associated operations such as bundle reserving, resort reservations, and patron control. The examine explores the technological framework, which includes the usage of HTML, CSS, JavaScript, PHP, and MySQL, and evaluates the gadget’s effectiveness in addressing the challenges faced by way of conventional tour corporations. The paper also highlights the system’s potential to offer a person-friendly interface, enhance operational performance, and improve customer pleasure. Furthermore, the take a look at discusses future opportunities for integrating superior technology which include AI, IoT, and blockchain to further enhance the gadget’s competencies.

Efficient File Compression and Data Representation Using Video Pixels:RGB and Binary Modes
Authors:-Rogith K K, Assistant Professor Mrs. V. Latha Sivasankarı

Abstract-File storage and transfer have become increasingly important in a data-driven world where efficiency and reliability are critical. Traditional file compression techniques often fail when subjected to lossy compression algorithms used in video formats, leading to data corruption. This research introduces a novel approach to encoding file data into video frames using two distinct modes: RGB and binary. RGB encoding maps data bytes to the colour channels of pixels, offering high efficiency, while binary encoding represents each bit as black-and-white pixels, ensuring resilience to compression artifacts. To further enhance user-friendliness, the system embeds encoding settings in the first video frame, automating the decoding process and reducing manual intervention. This hybrid system provides a practical balance between efficiency and robustness, making it suitable for real-world applications such as secure data transfer, archival storage, and multimedia-based information systems. This paper evaluates both encoding modes and proposes solutions to minimize data corruption caused by video compression. The research concludes with potential future enhancements, including hybrid systems that combine RGB and binary encoding, integration with AI-based algorithms for adaptive encoding, and scalability for 4K and 8K video resolutions. These advancements aim to optimize both storage efficiency and data integrity, addressing the growing demand for reliable file storage solutions.

DOI: 10.61137/ijsret.vol.11.issue2.241

Theory of Optical Emission Spectroscopy
Authors:-Sumit Kumar, Professor Dr. Mohit Kumar

Abstract-Characterization of material is a challenging task because of its diversity and the type of material. To know the chemical properties of the material some instrumental techniques could be used. The OES is the basic technique to measure the attenuation of light as it passes through a material. As light travels through a material, the energy of the incident photons can transfer electrons into higher energy states. In other words, electrons in the material absorb this light. In most optically active materials, UV and visible photons will promote electrons to higher energy electronic orbitals, while IR photons will increase the vibrational energy of the electrons.

The Role of ERP Cloud in Data Transformation and Implementation Challenges
Authors:-Uma Maheswara Rao Ulisi

Abstract-Oracle Cloud is well-suited for large corporations in industries such as finance, healthcare, telecommunications, retail, and manufacturing. These organizations require robust, secure, and scalable cloud solutions to manage complex workloads, global operations, and sensitive data

DOI: 10.61137/ijsret.vol.11.issue2.242

Fruit Quality Classification Using AI: A Machine Learning-Based Approach
Authors:-Rishav Raj, Dinesh Sapkal, Assistant Professor Bhumi Shah

Abstract-This research explores the application of machine learning techniques, particularly Convolutional Neural Networks (CNNs), to automate the classification of fruit quality using the Kaggle fruit dataset. Traditional methods of fruit grading, which rely on human inspection, are labor-intensive and prone to errors, leading to inefficiencies in the agricultural supply chain. To address this issue, we employ a deep learning approach to develop a model capable of accurately classifying fruit quality based on visual attributes such as color, texture, and shape. The dataset, consisting of images of various fruit types labeled with quality classifications (ripe, unripe, overripe, defective), was preprocessed to standardize the data and augmented to prevent overfitting. A CNN model was trained using this dataset, and its performance was evaluated using standard metrics, including accuracy, precision, recall, and F1 score. The results demonstrate that the CNN model achieved an accuracy of over 90%, outperforming traditional machine learning algorithms like Support Vector Machines (SVMs) and Random Forests. The findings suggest that machine learning models, especially CNNs, can significantly enhance the speed, accuracy, and consistency of fruit quality classification, making them viable for real-time, automated grading systems in the agricultural industry. This approach has important implications for reducing labor costs, improving efficiency, and minimizing food waste by ensuring that only the highest-quality produce reaches consumers. Future work can extend these results by incorporating multi-sensory data and integrating real-time fruit sorting systems for large- scale agricultural applications.

A Comparative Study to Forecast Bitcoin Price Using Machine Learning & Time Series Models
Authors:-Pasumarthy Manasa Komali, Ruma Dutta

Abstract-Block chain is an emerging technology and cryptocurrency is one of the backbone of blockchain technology in the financial sector. Even though other cryptocurrencies have been around for a few years, Bitcoin has kept its position as the most valuable cryptocurrency. On the other hand, Bitcoin’s price has been very unpredictable, making it almost impossible to guess where it might go in the future. The goal of this research is to find out which model is the most accurate and best at making predictions. Several different machine learning and time series approaches are used to determine Bitcoin prices. The goal of this article is to see how well it is possible to predict where the price of Bitcoin in US Dollars will go in the future. US Dollars are used to figure out how much Bitcoin costs. The price information came from a website called Coin market cap. We used machine learning techniques like Random Forest, XG Boost, kNN and time series models like AR, MA, ARIMA, Prophet. It has been found in our experiments that time series models give better results than machine learning approaches.

DOI: 10.61137/ijsret.vol.11.issue2.243

New Global Parking System
Authors:-Samik Choudhury, Shivam Gupta, Rohit Yadav, Dr Shraddha Oza

Abstract-In this current era of modernization, we are all tied under the constraints of time, and it’s the highest priority of all values. Traffic nowadays is a major problem in major cities, and a bigger problem than that is the lack of a proper parking system. People who park their vehicles in no-parking zones often annex someone else’s spot, or park vehicles on roads and create bizarre traffic situations. In rush hours, things even worsen more, and, at last, we people are the ones who suffer. Shopping malls, hospitals, airports, and railway stations are the worst hit areas due to improper parking management. All these upper hurdles demand one thing, and that is a smart and efficient parking system. This project will enhance the experience of parking using Internet of Things (ESP8266 ) which will give the parking spots to be appear on a map near to the desired location. The parking spots will be detected using IR Sensors at each location , connected with a common server to ensure end-to-end services for the user.

AI for Climate Change Prediction
Authors:-Anant Kajrolkar

Abstract-Climate change poses a significant threat to global ecosystems, human health, and economic stability are all seriously threatened by climate change. Reducing its effects and directing policy decisions require timely and accurate climate projections. Even though they work well, traditional climate models have trouble managing large datasets, guaranteeing real-time adaptation, and producing accurate long-term projections. Through the integration of cutting-edge machine learning techniques, this study investigates how artificial intelligence (AI) can revolutionize climate prediction. Large-scale environmental datasets from sensors, satellites, and historical climate records are used by AI-driven climate models to find intricate patterns and improve prediction accuracy. AI also enhances real-time climate monitoring, enabling adaptive strategies and quick detection of environmental changes. This research examines the effectiveness of AI in climate prediction by comparing its performance with traditional models in terms of accuracy, computational efficiency, and real-time capabilities. The study also highlights the application of AI in critical areas such as predicting extreme weather events, monitoring greenhouse gas emissions, and supporting data-driven policy decisions. Key methodologies include using deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for processing complex climate datasets. Additionally, AI’s role in optimizing computational resources and enhancing energy efficiency is investigated, making it a sustainable solution for large-scale climate modelling. The findings demonstrate that AI-driven models offer significant improvements in prediction accuracy, faster processing times, and greater adaptability to changing environmental conditions. These advancements provide valuable insights for policymakers, enabling data-backed decisions that can effectively address climate-related challenges. The study concludes that integrating AI with climate science will play a pivotal role in developing sustainable, scalable, and efficient solutions for future climate predictions and mitigation efforts.

Comparative Analysis of Zero-Shot, Few-Shot, and One-Shot Learning in Machine Learning
Authors:-Shreya Parmar, Yash Ganar, Lee Henriques, Professor Poonam Thakre

Abstract-Few-shot, one-shot, and zero-shot learning are ma- chine learning paradigms that aim to enhance model performance in scenarios with limited labeled data. Few-shot learning (FSL) enables models to generalize from a small number of examples per class, leveraging techniques such as meta-learning and transfer learning. One-shot learning is an extreme case of FSL, where the model learns to classify new instances based on a single example, often using metric-based learning approaches like Siamese networks. Zero-shot learning (ZSL) takes this further by enabling models to recognize new classes without any labeled examples, relying on semantic attributes or embeddings to bridge the gap between seen and unseen classes. These approaches are particularly valuable in real-world applications such as natural language processing, computer vision, and robotics, where ob- taining large labeled datasets is challenging.

Analysis of Fibre Reinforced Metal Matrix Composite Leaf Spring for off Road Vehicle
Authors:-Mr. V.V.N. Sarath, Kurada Praveen Srikanth, Peyyala Manikanta, Sanku Jaswanth Charan, Seelam Bhaskar Sai Ram

Abstract-The Automobile Industry has shown keen interest for replacement of steel leaf spring with that of glass fiber composite leaf spring, since the composite material has high strength to weight ratio, good corrosion resistance properties. The present study searches the new material for leaf spring. In present study the material selected was glass fiber reinforced plastic (GFRP) and the epoxy resin is used against conventional steel. A spring with constant width and thickness was fabricated by hand lay-up technique which was very simple and economical. The numerical analysis is carried via finite element analysis using ANSYS software. Stresses, deflection and strain energy results for both steel and composite leaf spring material were obtained. Result shows that, the composite spring has maximum strain energy than steel leaf spring and weight of composite spring was nearly reduced up to 85% compared with steel material. This paper describes design and FEA analysis of composite leaf spring made of glass fiber reinforced polymer. The dimensions of an existing conventional steel leaf spring of a light commercial vehicle are taken for evaluation of results.

DOI: 10.61137/ijsret.vol.11.issue2.244

Review on Enhancement of BLDC Parameters Using ANN Controller across Electric Vehicle
Authors:-Akash Baghel, Professor Amit kumar Asthana

Abstract-In this paper, a review of the advances in brushless synchronous motors is presented because there has been an increasing interest in advanced motor control and to address the weaknesses of conventional motor control. The traditional motor control strategies, for example, proportional plus integral controllers (PIs), are simple and easy to maintain. On the contrary, they require accurate tuning and are affected by motor parameter variations. To address these challenges and many others (power factors, torque ripple, current limit, voltage limit, speed limit), advanced control methods are required to enhance the performance of the motor drive control. The advanced control techniques include model predictive control, slide mode control, reinforcement learning, and fuzzy logic control. This paper provides a comprehensive review of advances in control methods and addresses the challenges and limitations associated with their practical application.

Review on Modeling and Analysis of Load Flow Analysis across Solar Power Generator
Authors:-Rahul Chikane, Professor Amit kumar Asthana

Abstract-Distribution system advancing is very important for many Researchers and power engineers. Fast load flow techniques are highly desired as conventional techniques are consuming very long time. Power flow in the distribution system is one of the most critical point in the grid for analysis study. The integration of variety of renewable energy for the sake of sustainability from consumers and utility to the existing grid requires extra modification to obtain the stability and costsaving for consumer and utility. This paper presents a review of distribution network load flow analysis after the renewable energy resources penetrated the grid. It also reviews the main old techniques that are with us until today and some of their introduced modification.

Medi Sync: The Next Gen AI Renaissance Elevating Allied HealthCare by Leveraging Neural Networks and Machine Learning Techniques Pioneering a New Era in Global Allied Healthcare
Authors:-N.V. Vijaya Lakshmi

Abstract-The integration of neural networks and machine learning technologies in allied healthcare has the potential to revolutionize diagnostic accuracy, treatment personalization, and patient care. This study focuses on practical applications and strategies for implementing these advanced technologies to optimize healthcare processes in real-world scenarios. By leveraging artificial intelligence, this study seeks to enhance diagnostic imaging, predictive analytics, personalized treatment plans, and remote patient monitoring. A key innovation explored is the Geo Health Sync ID, a centralized health record system designed to improve diagnosis accuracy, streamline medical histories, and enhance treatment outcomes by enabling global access to healthcare data. Additionally, this is a proposal of the development of an AI-powered chatbot and wearable device that utilizes neural networks to monitor patient vitals in real-time, detect anomalies, and provide early alerts to healthcare providers. Addressing challenges such as data privacy, AI model fairness, and seamless clinical integration, this study aims to bridge existing gaps and establish a more efficient, patient-centric healthcare ecosystem. This initiative holds the potential to transform allied healthcare by improving patient outcomes, reducing healthcare costs, and driving innovation through AI-driven decision-making and automation. With the rapid advancements in artificial intelligence (AI), neural networks and machine learning have become integral to allied healthcare. These technologies offer predictive analytics, disease diagnosis, treatment recommendations, and administrative efficiency. Machine learning algorithms, particularly deep learning models, process vast amounts of healthcare data, enhancing accuracy in medical imaging, patient monitoring, and personalized medicine. This paper explores the applications, benefits, challenges, and future scope of neural networks in allied healthcare, including real-world case studies and implementation strategies.

DOI: 10.61137/ijsret.vol.11.issue2.245

Enhancement of Process Parameters for Mex Process Using Cura 5.9.0 and Minitab Softwares
Authors:-Mr. G.V.N. Santhosh, Pedagadi Santhosh Kumar, Sai Naresh Masakapalli, Dupalli Dharmaraju, Yaraka Sai Venkata Vasu

Abstract-The Material Extrusion (MEX) process, commonly known as Fused Deposition Modeling (FDM), is widely used for manufacturing complex geometries with minimal material wastage. However, optimizing key printing parameters is crucial for improving efficiency while maintaining print quality. This study focuses on enhancing the MEX process using Cura 5.9.0 and the B to optimize layer height, line width, and wall count, aiming to reduce print time and material consumption. The Taguchi DOE method was employed to systematically analyze the effects of these parameters on printing performance. A set of experiments was conducted by varying layer height, line width, and wall count within practical limits. The primary objectives were to minimize printing time and optimize material usage while maintaining structural integrity. Print time and material consumption were recorded for each experiment, and statistical analysis was performed using Minitab to determine the optimal parameter combination. The results show that layer height significantly influences printing time, as higher layer heights reduce the number of layers but may impact surface quality. Line width affects material flow and print strength, while wall count directly impacts material consumption. The optimized parameter settings achieved a significant reduction in print time and material usage, ensuring an efficient balance between speed, material economy, and part durability. This study demonstrates that using Cura 5.9.0 in combination with DOE techniques provides a structured methodology for enhancing the efficiency of the MEX process. The findings are beneficial for industries and researchers seeking to optimize print settings, reduce operational costs, and improve sustainability in additive manufacturing.

DOI: 10.61137/ijsret.vol.11.issue2.246

Generative Design Optimization for Advance Manufacturing Process
Authors:-Mrs. K. Aravinda, Komali Naga Ramakrishna, Robba Rithwik, Sahukari Vijay Kumar, Komarthi Gagan Venkat Jayanth

Abstract-Brake pedals are critical components in automotive applications, requiring a balance between high stiffness, low weight, and manufacturability. Traditional design approaches often result in suboptimal structures with excessive material usage. This study explores the generative design optimization of a brake pedal using Fusion 360, targeting maximum stiffness and minimum mass while considering different manufacturing constraints for milling and additive manufacturing (AM). Generative design algorithms were employed to generate multiple optimized pedal designs by defining material properties, boundary conditions, and load cases. The milling-based design focused on constraints like tool access, machining orientations, and material removal feasibility, whereas the AM-based design leveraged organic lattice structures and topology optimization to achieve minimal material usage while maintaining structural integrity. The optimized models were analyzed using finite element analysis (FEA) to compare stress distribution, deformation, and weight reduction for both manufacturing methods. Results indicate that additive manufacturing allows for a more complex, lightweight design with internal lattice structures, resulting in a higher stiffness-to-weight ratio compared to the milling approach. However, the milled design exhibits superior fatigue resistance and is better suited for high-load conditions due to the absence of microstructural porosity. A comparative evaluation of material usage, manufacturing feasibility, and mechanical performance highlights the trade-offs between AM and milling-based designs. This research demonstrates how generative design tools can optimize brake pedal geometry for different manufacturing processes, leading to weight savings and enhanced performance while ensuring manufacturability. The findings provide valuable insights into process-dependent design optimizations and serve as a reference for future lightweight automotive component development.

DOI: 10.61137/ijsret.vol.11.issue2.247

A Role of AI in Traffic Management: A Study on Emergency Ambulance Services
Authors:-Dr. Shruti Bekal, Palak Vikas Gadiya, Kashish Jain, Sanjana Kankliya, Rashi Jain, Rabee Ahmed, Rajratna Karande

Abstract-This research delves into an innovative app that seamlessly integrates AI-driven traffic management with emergency ambulance services, presenting users with a holistic solution to address both transportation efficiency and urgent medical needs. By harnessing advanced algorithms and real-time data analysis, the app optimizes traffic flow, reduces congestion, and delivers personalized travel routes tailored to the prevailing conditions. Moreover, it offers a dedicated emergency ambulance service equipped with priority response times and specialized care for its subscribers. The subscription-based model of the app affords users a plethora of benefits, including access to enhanced features, exclusive discounts on additional healthcare services, and a flexible payment structure catering to diverse user preferences. In .essence, the app epitomizes the convergence of technology and human-centric design, empowering users to navigate urban landscapes safely and efficiently while ensuring immediate access to critical medical assistance when circumstances demand. Through its innovative approach and commitment to user-centricity, the app stands as a beacon of progress in shaping the future of mobility and healthcare accessibility in the communities.

DOI: 10.61137/ijsret.vol.11.issue2.248

Orientation Optimization of Material Extrusion Process Using Minitab Software
Authors:-Mr. M. Sunil Raj, Undurthi Bharath Kalyan, Nagabathula Manohar, Yannana Chaitanya Krishna Chowdary, Tatapudi Anil

Abstract-Optimizing the orientation of a part in the Material Extrusion (MEX) process is crucial for reducing print time, energy consumption, and material waste while maintaining part quality. This study focuses on optimizing the printing orientation and layer height using the Taguchi method in Minitab to achieve an efficient and sustainable additive manufacturing process. The research employs the Taguchi Design of Experiments (DOE) approach to systematically evaluate the effects of different orientation angles and layer heights on print time and energy consumption. Experiments were conducted by printing samples at various orientations and layer thicknesses, and the response variables—total printing time and energy usage—were recorded. Signal-to-noise (S/N) ratios were analyzed in Minitab to determine the optimal parameter settings that minimize both print time and energy usage. The results indicate that the print orientation significantly affects deposition path efficiency, while layer height plays a key role in determining the number of layers and total energy required. The optimized orientation and layer height configuration led to a substantial reduction in energy consumption without compromising part accuracy and mechanical integrity. This study demonstrates that using the Taguchi method in Minitab for orientation optimization provides a structured and statistical approach to improving additive manufacturing efficiency. The findings can be applied to enhance the sustainability of FDM-based 3D printing, reducing material wastage and operational costs while improving process efficiency.

DOI: 10.61137/ijsret.vol.11.issue2.249

Design and Dynamic Analysis of Formula Car
Authors:-Mr. D. J. Johnson, Nambu Sri Venkata Siva Sai Lakshman Royal, kakaraparthi Venkata Subrahmanyam, Sabbavarapu Nooka Subrahmanyam, Manepalli Bhavannarayana, Perapu Anand Rao

Abstract-The wheel hub and spindle are critical components in the suspension and steering system of a Formula Student car, directly influencing its vehicle dynamics, handling, and safety. This project focuses on the design, analysis, and optimization of the wheel hub and spindle assembly, aiming to ensure structural integrity, reduce weight, and improve overall performance under dynamic loading conditions. Using SolidWorks, the wheel hub and spindle were meticulously designed to meet the requirements of a Formula Student car, emphasizing lightweight construction while maintaining sufficient strength. The design process involved careful consideration of materials, geometry, and manufacturing feasibility to create a durable and efficient assembly. The geometry was optimized to reduce unsprung mass, which is crucial for enhancing vehicle stability and handling. The designed components were analyzed using ANSYS Workbench to simulate real-world conditions and assess their structural performance. Static and dynamic load analyses were performed to evaluate stress distribution, deformation, and factor of safety under various scenarios, such as cornering, braking, and acceleration. The results of the analysis demonstrate that the design meets the functional and safety criteria for a Formula Student car, ensuring reliability during competitive racing conditions. This study provides valuable insights into the integration of design and analysis tools for optimizing vehicle dynamics, contributing to the advancement of motorsport engineering.

DOI: 10.61137/ijsret.vol.11.issue2.250

Condition Design and Material Optimization of Axial Gas Turbine Under Static Loading
Authors:-Dr. G. Avinash, Markanda Raghaveandra, Matta Sri Ammayya, Tirukkovalluri Sri Jaiyanth Kowshik, Yedidha Chaitanya, Challa Sai Durga Kishore

Abstract-Micro turbines are becoming widely used for combined power generation and heat applications. Their size varies from small scale units like models crafts to heavy supply like power supply to hundreds of households. Micro turbines have many advantages over piston generators such as low emissions less moving parts, accepts commercial fuels. Gas turbine cycle and operation of micro turbine was studied and reported. Brief description on CAD software and CATIA studied and reported. Different parts (Inlet. Storage, Nozzle, Rotor, coupling, outlet, clip, housing) of turbine are designed with the help of CATIA (Computer Aided Three Dimensional Interactive Analysis) software. Then they were assembled to a single unit and coupled to a generator to produce power. The turbine is of Axial input and axial output type.

DOI: 10.61137/ijsret.vol.11.issue2.251

Neural Nap Guard: CNN-Driven Drowsiness Detection Using OpenCV
Authors:-FMrs.T.Satya Aruna, Sri Sai Akshitha Manepalli, Penugula Navya Nalini, Mayukha Vundavilli, Shaik Moula Ali, Ulamparthi Sateesh

Abstract-Driver drowsiness detection is crucial for road safety, as drowsy driving is a leading cause of accidents. This research presents a machine learning-based approach for detecting driver drowsiness using OpenCV and Keras frameworks. The system employs a camera to capture real-time video footage of the driver’s face. Key features such as eye closure and mouth movements are extracted through preprocessing techniques using OpenCV. These features are then used to train a Convolutional Neural Network (CNN) model with a large labeled dataset of video frames depicting both alert and drowsy drivers.Once trained, the CNN model predicts the driver’s drowsiness level in real-time, helping to identify signs of fatigue early. In addition to real-time alerts, the system provides a graphical representation of the drowsiness score over time, enabling continuous monitoring of the driver’s fatigue levels. This feature allows for proactive intervention, ensuring safer driving conditions.The proposed system utilizes deep learning techniques, particularly CNNs, which have proven effective in recognizing facial features and behaviors associated with drowsiness. The integration of real-time monitoring and visual feedback enhances the system’s accuracy and response time. By leveraging such advanced technology, this approach has the potential to significantly reduce traffic accidents caused by drowsy driving, offering a valuable tool for improving road safety and preventing accidents related to driver fatigue.

Design and Implementation of a Secure Bootloader Using Public Key Cryptography Research
Authors:-Manav Vaghela, Abhaysinh Parmar, Professor Lata Butiya

Abstract-A secure bootloader plays a critical role in ensuring the integrity and authenticity of firmware during system startup. Traditional bootloaders lack robust security mechanisms, making them vulnerable to tampering and malware injections. Public Key Cryptography (PKC) provides a strong foundation for secure bootloaders by enabling digital signatures and authentication mechanisms to verify firmware integrity before execution. This research explores the design and implementation of a secure bootloader using Public Key Cryptography to prevent unauthorized modifications and ensure a trusted execution environment. The proposed bootloader employs asymmetric encryption to verify signed firmware images using RSA or ECC (Elliptic Curve Cryptography). Additionally, hash-based verification techniques such as SHA-256 are used to ensure firmware integrity. Experimental evaluations demonstrate that the proposed bootloader effectively prevents unauthorized code execution, ensuring a secure boot process with minimal performance overhead. This paper discusses the system architecture, cryptographic implementation, evaluation metrics, and future research directions for enhancing bootloader security.

DOI: 10.61137/ijsret.vol.11.issue2.252

Design and Simulation of Steady State Thermal Analysis of Exhaust Engine Valve
Authors:-Mr. N. Raghuveer, Puli Hari Shankar, Kadiyala Rupananda Ganesh Kumar, Ramavath Arun Kumar Naik, Sai Durga Nishith Medisetti

Abstract-Internal combustion engines produce exhaust gases at high temperature and pressures. As these hot gases passes through the exhaust valve ,temperatures of the valve ,valve seat, and the stem increase .To avoid any damage to the exhaust valve assembly , heat is transferred from the exhaust valve from different parts ,Especially the valve seat insert during the opening and closing cycle as they come into contact each other . In this article, A Finite-Element method is used for modeling the transient thermal anaiysis of an exhaust valve .The temperature distribution and resultant thermal stresses at each opening and closing time are obtained. Detailed analyses are performed to estimate the boundary conditions of an internal combustion engine. The modeling includes exhaust valve, seat, guide, and spring. The analysis continues until a steady state condition is obtained. In this study ANSYS is employed for modeling and analysis of the exhaust valve. A Methodology is developed for transient thermal analysis of the exhaust valve.

DOI: 10.61137/ijsret.vol.11.issue2.253

Car Damage Discovery Using Machine Literacy
Authors:-Gangireddy Naveena

Abstract-Ultramodern auto insurance diligence faces significant resource destruction due to claim leakages, which affects their payouts. presently, visual examinations and attestations are performed manually, causing detainments in claim processing. former studies have demonstrated that image bracket is attainable with a small dataset by transferring and repurposing knowledge from models trained for different tasks. Our ideal is to develop an auto damage classifier using a deep literacy model to directly descry colorful damage types from auto images. still, the limited dataset can be a determining factor. Training a Convolutional Neural Network from scrape is challenging due to the failure of large datasets. In this design, we explore classifying images of damaged buses to assess the financial value of the damage. This bracket task is delicate due to the lack of standardized datasets and potentially non-discriminative clauses. We employed a pretrained YOLOv8 model to train a classifier for grading the dataset, testing three different cases damaged or not, damage position (front, reverse, or side), and damage inflexibility (minor, moderate, or severe).

Edge Computing and 5G Networks: Enabling the Future of Connectivity
Authors:-Mihir Dhruv, Professor Kashyap Dave

Abstract-The integration of edge computing and 5G networks is revolutionizing modern digital infrastructure by enabling faster data processing, reduced latency, and enhanced network efficiency. As 5G technology promises ultra-fast connectivity and higher bandwidth, edge computing complements it by decentralizing computation, bringing data processing closer to the source. This synergy addresses critical challenges such as network congestion, real-time data handling, and improved security. The combination enhances applications across industries like autonomous vehicles, smart cities, healthcare, and industrial automation. This paper explores the architecture, benefits, challenges, and future potential of edge computing in 5G environments, providing a comparative analysis of traditional cloud-based approaches and edge-driven solutions. The findings highlight the importance of this convergence in driving innovation and shaping the next generation of digital services.

Promptify: The Prompt Marketplace
Authors:-Yassir Farooqui, Ansh Rawal, Krushna Soni, Pooja Maurya, Trupti Devakar

Abstract-Promptify represents a novel approach to AI-driven content creation by offering a centralized online mar- ketplace for buying and selling creative and technical prompts. The platform bridges the gap between prompt creators and consumers, streamlining the process of ac- cessing high-quality prompts across diverse categories such as creative writing, technical tasks, and artistic projects. Existing platforms lack centralized resources, quality moderation, and secure payment mechanisms, leaving users with fragmented solutions. Promptify addresses these challenges by providing secure trans- actions via Stripe, user shop management systems, and community engagement features. The platform uses modern technologies like Next.js, MongoDB, and Prisma ORM to ensure scalability, security, and perfor- mance optimization. The paper details Promptify’s ar- chitecture, implementation, and testing methodologies, emphasizing its potential to democratize access to qual- ity prompt resources. Future work explores AI-based prompt recommendations, blockchain-based ownership verification, and multilingual support.

DOI: 10.61137/ijsret.vol.11.issue2.254

Evolving Wireless Technologies: Innovations Shaping Next-Generation Communication Networks
Authors:-Assistant Professor Mrs.P.Nirmala Priyadharshini, Assistant Professor Mr.G.Jayaseelan

Abstract-Wireless communication has undergone a remarkable transformation over the past decades, driven by rapid technological advancements. This paper explores the evolution of wireless technologies, from early mobile networks to emerging innovations such as 6G, edge computing, AI-driven communication, and quantum networks. Key challenges, including spectrum management, security, and energy efficiency, are analyzed alongside the potential of new wireless paradigms. The study highlights how next-generation communication networks will redefine connectivity, enabling seamless, ultra-reliable, and high-speed data exchange for future applications such as smart cities, autonomous systems, and the metaverse.

Revitalizing Traditional Methods for Early Keratoconus Detection: A Primary Care Approach
Authors:-Nisha Talukdar

Abstract-Keratoconus (KC) is a progressive disorder of the cornea, marked by its thinning and protrusion, which results in a conical shape that can lead to vision impairment if not identified promptly. The precise etiology of KC is not well understood, and most individuals affected do not have a familial history of the disorder. It commonly manifests during adolescence, presenting as unilateral visual impairment due to increasing myopia and irregular astigmatism that deteriorates over time. The eye that is not affected typically retains normal vision and exhibits minimal astigmatism at the initial stages. Despite improvements in diagnostic technologies, early identification of KC remains difficult, especially in areas with limited resources. This review emphasizes economical yet often overlooked techniques for identifying keratoconus (KC), such as detection of the oil droplet sign using a direct ophthalmoscope and null-screen testing. These methods, when integrated with contemporary artificial intelligence technology, can significantly improve accuracy, efficiency, and accessibility. These techniques also prioritize portability and affordability. When integrated with artificial intelligence and mobile technology, they present promising opportunities for early detection, which can help mitigate disease progression. Such screening tools could significantly improve access to screening in remote locations and prove essential for evaluating patients who are uncooperative.

Zero-Trust Architecture for E-Commerce: Implementing Decentralized Identity on A MERN Platform
Authors:-Ayush Kumar, Divya Patel, Assistant Professor Rashmi Pandey, Assistant Professor Shivangi Patel

Abstract-The rapid growth of e-commerce has brought convenience to consumers but has also led to increasing cyber threats and data breaches. Traditional security measures, primarily reliant on centralized identity management, pose critical vulnerabilities that can be exploited by malicious actors. To address these concerns, this research proposes an advanced security model utilizing Zero-Trust Architecture (ZTA) combined with Decentralized Identity (DID) within a MERN stack-based e-commerce platform. This approach ensures that every access request is verified, significantly reducing unauthorized access risks. Furthermore, blockchain-backed DID solutions offer a tamper-proof identity verification system, empowering users with greater control over their credentials while eliminating the dependency on third-party identity providers. This paper explores the implementation, benefits, and real-world applicability of this security model, highlighting its ability to enhance trust and improve cybersecurity in modern e-commerce platforms.

DOI: 10.61137/ijsret.vol.11.issue2.255

Image Inpainting Based on Patch-GANs
Authors:-Harsh Mandaliya, Jaimin Vasani, Professor Reena Desai

Abstract-Image inpainting is a crucial task in computer vision that focuses on reconstructing missing or corrupted parts of an image while maintaining structural consistency and visual realism. Traditional inpainting methods, such as diffusion-based and exemplar-based techniques, often struggle to restore fine textures and complex structures, leading to blurry and unrealistic results. The advent of Generative Adversarial Networks (GANs) has significantly enhanced image inpainting by learning to generate plausible image content. However, conventional GAN-based models emphasize global image coherence while neglecting finer local details, causing inconsistencies in high-texture regions. To overcome these limitations, PatchGAN-based inpainting evaluates image realism at the patch level rather than analyzing the entire image as a whole. This technique employs multi-scale discriminators that ensure improved texture synthesis and structural continuity at different spatial resolutions. Experimental studies reveal that PatchGAN-based models outperform conventional GAN-based methods in terms of perceptual quality, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), producing sharper and more realistic image restorations. This review explores the advancements in PatchGAN-based inpainting, highlighting its benefits, architectural components, and future research directions to further enhance image reconstruction quality.

DOI: 10.61137/ijsret.vol.11.issue2.256

Pneumatic Sheet Bending Machine
Authors:-Dr.N.S.Aher, Autade Kadambari Vilas, Chavan Vaishnavi Sambhaji, Pawar Rutuja Machhindra, Naikwade Sanskruti Mahendra

Abstract-We have Developed a Pneumatic Sheet Bending Machine, it is a cost-effective, efficient, and automated solution for bending thin metal sheets using pneumatic power, addressing the challenges of laborintensive manual methods and expensive hydraulic systems. It utilizes a pneumatic cylinder that applies controlled pressure to bend metal sheets with precision, speed, and consistency, reducing manual effort and the need for multiple operators. The machine is designed to be compact, portable, and easy to operate, making it ideal for small to medium-scale workshops, metal fabrication units, and the automotive industry. Its high-speed operation enhances productivity while maintaining accuracy, making it a valuable tool for producing brackets, enclosures, and sheet metal components. However, the machine is limited to bending thin sheets and requires an air compressor, which may not always be available in smaller setups. Future advancements could improve its capacity for thicker sheets, integrate automated controls for enhanced precision, and utilize advanced pneumatic technologies for greater efficiency. Overall, this innovation provides a practical and scalable alternative to traditional bending methods, significantly improving productivity, reducing labor costs, and making metal fabrication more accessible to small industries.

DOI: 10.61137/ijsret.vol.11.issue2.257

Transport Management & Supply Chain
Authors:-Tejas Rohit, Prince Kumar, Assistance Professor Harshita Gaikwad

Abstract-Transport management is a critical component of supply chain logistics, influencing cost efficiency, operational effectiveness, and overall business performance. This study explores the role of transport management in optimizing supply chains by examining the key challenges, technological advancements, and sustainability initiatives Key findings indicate that the integration of digital tools, such as transport management systems (TMS) and artificial intelligence (AI), enhances route planning, cost optimization, and supply chain visibility. Additionally, sustainable transport practices, including alternative fuel sources and electric vehicles, contribute to cost savings and environmental benefits. The study concludes that businesses must adopt modern transport strategies to remain competitive and resilient in a rapidly evolving logistics landscape.

DOI: 10.61137/ijsret.vol.11.issue2.258

Employee Attitudes towards the Organization: A Study on Job Satisfaction and Organizational Commitment
Authors:-Amit Jaywant Dalvi, Assitant Professor Amol Baviskar

Abstract-Employee attitudes significantly influence organizational success. This study examines employee attitudes, job satisfaction, and commitment within an organization. Based on a comprehensive literature review and primary data, the research identifies key factors affecting employee engagement and recommends strategies for improving workplace morale and performance.

Time Management Strategies for Increased Productivity in Startups
Authors:-Viraj Vijay Darekar, Professor Amol Baviskar

Abstract-Time management is a critical factor in the success of any business, especially startups. Unlike large corporations with established workflows, startups operate in a highly dynamic and resource-constrained environment, making productivity a key determinant of their success. Efficient time management enables startups to prioritize tasks, allocate resources wisely, and achieve business objectives with minimal waste of time and effort. This paper explores various time management strategies used in startups and their impact on productivity. The study also examines tools and techniques that can help startup teams improve their efficiency and meet business goals.

Strategic Planning in Engineering Design: A Focus on Cost and Schedule
Authors:-Tushar Sudhakar Loharkar, Professor Amol Baviskar

Abstract-This paper gives the strategic planning process within the engineering design phase of onshore oil well projects, with a specific emphasis on cost and schedule management. Highlighting the critical role of Front-End Engineering Design (FEED) and detailed design, this study examines methodologies and techniques vital for achieving project success within budgetary and temporal constraints. Through a detailed analysis incorporating both quantitative and qualitative methods, the paper offers a comprehensive understanding of the inherent complexities and challenges in managing project cost and schedule. It elaborates on quantitative approaches such as Monte Carlo Simulation and advanced Cost Estimation Models, alongside schedule-oriented techniques like the Critical Path Method (CPM). The objective is to equip project managers and stakeholders with actionable insights for optimizing resource allocation, mitigating financial and timeline risks, and employing industry best practices to enhance project predictability and efficiency, ultimately ensuring project sustainability and profitability in the oil and gas sector.

Hybrid Intrusion Detection System Using SVM for Anomaly and Misuse Detection in Networks
Authors:-Professor& HOD Jayshree Boadh, Seema Narware

Abstract-Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats. Traditional IDS methods, including anomaly-based and misuse-based detection, have limitations such as high false positive rates and inability to detect novel attacks. A hybrid IDS combines both techniques to enhance accuracy and efficiency. Support Vector Machines (SVM) have proven to be effective in classifying network traffic for intrusion detection. This paper reviews the application of SVM in hybrid IDS, its advantages, challenges, and potential improvements for network security.

Customer Churn Prediction: Leveraging Machine Learning for Enhanced Retention Strategies
Authors:-Anshul Dave

Abstract-Customer churn prediction has become a critical focus area for businesses seeking to maintain customer relationships and protect revenue streams. This research paper provides a comprehensive examination of current methodologies, models, and implementation strategies for effective customer churn analysis. Modern approaches to churn prediction demonstrate significant improvements in accuracy and interpretability, enabling businesses to proactively address customer attrition before it occurs. By leveraging advanced analytics and machine learning techniques, organizations can identify at-risk customers, implement targeted retention strategies, and gain valuable insights into factors driving customer decisions.

Intelligent Career Guidance System for College Students to Sustain in the Emerging Job Sector
Authors:-Danish Homavazir, Dr Meenakshi Thalor

Abstract-An advanced career guidance recommendation sys- tem is an innovative algorithm that differentiates itself from traditional recommendation algorithms, which work through a link to provide relevant recommendations and are heavily dependent on user behavior. These algorithms are trained to understand various aspects relating to an applicant—their skills, qualifications, work experience, and preferences—and utilize these data points to create customized recommendations based on their profile and career aspirations. Additionally, these intelligent talent recommendation systems continuously evolve, refining their suggestions by analyzing user feedback and performance insights. This study introduces an innovative approach to talent rec- ommendation specifically designed for college students who are preparing to enter the emerging job sector based on a Rank- Based Sequential Deep Learning (RBS-DL) Model. By studying students’ skills, qualifications, interests, and career aspirations, the algorithm intends to offer personalized recommendations. The effectiveness of the recommendation algorithm enhanced by RBS-DL is assessed through both simulated experiments and empirical verifications. Results show significant improvement in recommendation accuracy and relevance over traditional approaches. For the RBS- DL algorithm, students showed a higher job offer acceptance rate by 40% and around 30% increment in job satisfaction levels. This algorithm also learns from user interactions, adjusting its recommendations over time based on real-time user feedback.

DOI: 10.61137/ijsret.vol.11.issue2.259

Automated Brain Tumour Detection from MRI Using Fine Tuned Efficientnet-B0
Authors:-Assistant Professor T.Vineela, R.Nagamani, V.Sammilita, V.V.Komalatha, N.Sravanthi

Abstract-Brain tumour disease arises from the uncontrolled growth of cells. Detecting brain tumours early is crucial for successful treatment. Many current diagnostic methods are cumbersome, demand significant manual input, and yield less-than-ideal results. The EfficientNet-B0 architecture was utilized to diagnose brain tumours through magnetic resonance imaging (MRI). This refined architecture was applied to classify four distinct stages of brain tumours from MRI images. The fine-tuned model achieved 99% accuracy in identifying four different brain tumour classes: glioma, no tumour, meningioma, and pituitary. The proposed model excelled in detecting the pituitary class, with a precision of 0.95, recall of 0.98, and an F1 score of 0.96. It also performed exceptionally well in identifying the no-tumour class, with precision, recall, and F1 score values of 0.99, 0.90, and 0.94, respectively. The precision, recall, and F1 scores for the Glioma and Meningioma classes were also notably high. This proposed solution holds significant potential for improving clinical assessments of brain tumours.

DOI: 10.61137/ijsret.vol.11.issue2.260

Securing IoT Data in Cloud: Techniques and Best Practices
Authors:-Harshal Ganesh Chavan

Abstract-The rapid adoption of the Internet of Things (IoT) has led to a massive increase in data generation, often stored and processed in the cloud. This presents significant security challenges due to the inherent vulnerabilities of IoT devices and cloud infrastructures. This paper explores the techniques and best practices for securing IoT data in the cloud, including encryption, access control, data integrity mechanisms, and advanced security protocols. By reviewing current research and technological advancements, the paper outlines strategies that can mitigate risks such as unauthorized access, data breaches, and cyber-attacks. The paper aims to provide insights into safeguarding the integrity, confidentiality, and availability of IoT data within cloud ecosystems. Securing IoT data in the cloud is crucial due to the sensitive nature of the data and the growing number of connected devices. Key techniques include encryption to protect data in transit and at rest, strong authentication and authorization measures to control access, and edge computing to process data closer to the source for better security. Best practices involve regular software updates, minimizing the amount of collected data, using multi-layer security approaches, ensuring compliance with regulations, and continuous monitoring for potential threats. By following these strategies, organizations can protect IoT data in the cloud from unauthorized access and cyberattacks.

WebVision – A Multi-Model AI Approach to Privacy-Preserving Web Accessibility
Authors:-Mrs.Punashri Patil, Vinay Basargekar, Shraddha Thorbole, Yashraj Dhamale, Saurabh Rai

Abstract-This paper adheres to web accessibility through a privacy-centric, AI-powered approach via an extension in Chrome. The extension implements a multi-model architecture that combines Google Chrome’s built-in AI capabilities using Gemini Nano with a JavaScript library transformer.js to process machine learning (ML) models directly in the browser that is web content run locally on users’ devices. Unlike existing solutions that rely on cloud processing or limited built-in browser features which might hinder the user’s privacy, our extension prioritizes user privacy by performing the computational/processing tasks on-device while providing comprehensive accessibility features. System also has voice commands for hands free navigation, generates summaries based on prompts and utilizes moondream(AI model) to provide detailed descriptions of images present in the web-content. Performance metrics indicate that the local processing approach maintains robust functionality while preserving user privacy.Our user testing shows remarkable improvements in web browsing for people with diverse accessibility needs. Users reported faster navigation, better understanding of content, and greater independence compared to traditional screen readers and similar tools. Our approach of processing information locally on users’ devices maintains strong performance while protecting privacy. This research advances accessible technology by showing how AI models can be integrated into browser extensions to make the web more inclusive without compromising privacy or requiring powerful computers.

DOI: 10.61137/ijsret.vol.11.issue2.261

Harnessing AI and ML for Enhanced Cyber Defense in Electric Vehicle Security
Authors:-Rohit Jadhav, Dr.Meenakshi Thalor

Abstract-Electric vehicle information security improves through the implementation of AI & ML in automotive sector. A targeted study about data-driven implementations of AI and ML within electric vehicles represents a necessary research need. This paper describes the present situation regarding AI and ML implementations in EVs. Comprehensive analysis of pertinent studies and articles enabled our team to find important content after analyzing how different subjects tie together within these documents. This recent research has shown that AI along with ML technologies increasing appear as critical tools in protecting EV information security through improved authentication sys- tems and better attack detection methods. Table I shows different implementation applications of Machine learning techniques, encompassing deep learning and neural network models while blockchain technology shows increasing applications. The Study data shows the man in middle intrusion detection receives the highest attention at 75% while authentication covers 20% of the literature and prevention takes up only 5%. Deep learning consti- tutes 70% of analyzed works with neural networks representing 15% and remaining studies employing alternative methods..

DOI: 10.61137/ijsret.vol.11.issue2.262

Donation System or Food and Clothes Based on AIML
Authors:-Shraddha Laxman Hiwrale

Abstract-Donate Management System Food and Clothes using AI In India, traditional donation systems lack transparency and often mismatch resources and poor allocation. India has millions of people who struggle to make ends meet with food and cloth donations to help feed them. Rural areas and people who are affected by natural disasters are also, in documents, in need of food and clothes. Using artificial intelligence, this system improves decision-making as it analyzes donation patterns and demand and uses real-time data to link donors and recipients. We are also leveraging AI algorithms to prioritize critical needs, reduce waste, and ensure equitable distribution to communities in need. The system has features such as registering the donor and recipient, automatic allocation. One cannot ignore the extreme disparity in wealth distribution in India where we have so many NGOs, government schemes and volunteer organizations and they are selflessly working to provide food and clothing to the needy when they need it, but still the effort is getting lost due to logistical inefficiencies, resource wastage and lack of real-time data.

DOI: 10.61137/ijsret.vol.11.issue2.263

Heart Disease Risk Assessment Using Machine Learning Algorithms
Authors:-Sneha Gonjari, Dr. Meenakshi Thalor

Abstract-Modern technology is changing the healthcare land- scape,and perhaps no greater impact is being made in the diagno- sis and prediction of heart disease. Inthis research work, machine learning (ML) models are applied to predict the probability of heart disease for the individual patient based on personal- related features such as age, blood pressure, cholesterol levels, and life experience information. Although several studies have implemented ML techniques, there are still challenges in limited datasets, accuracy, andinterpretability of the models used. The proposed system is intuitive as compared to otherswhere health information from the users is entered and risk is returned. If the model predicts a high risk, it recommends that the individ- ual seea health care provider. Night trip focuses on accessibility with this tool available to the generalpublic as well as medical doctors, uniting personal health with professional diagnostics. The system enhances prediction reliability responsively toheart disease prevention by harnessing the combined strengths of multiple models.

DOI: 10.61137/ijsret.vol.11.issue2.264

AI-Powered Image Processing for Plant Disease Detection in Viticulture
Authors:-Yash Bhalekar, Dr Meenakshi Thalor

Abstract-Agriculture is on the brink of a revolutionary change during the era of advanced technology meets age-old industries. This article give a Advanced tools for plant disease detection at an early stage is in demand for sustainable agriculture. Traditional methods, which rely on visual inspection, are slow and error- prone. This paper talks about the use of artificial intelligence and image processing for the accurate and effective diagnosis of plant diseases. Although quite promising, recent attempts suffer from small datasets as well as lack of generalization. As we are working with deep models, we have also significantly emphasized the diversity between the views in the dataset which not only makes our work more accurate but also can scale better than previous work. Hence, this research aids sustainability in agriculture with strong, automated solution for disease detection.

DOI: 10.61137/ijsret.vol.11.issue2.265

Smart Location Recommendation for Group Meetups: A Machine Learning Perspective
Authors:-Mrs. Anuja S. Phapale, Niranjana Patil, Janhavi Parihar, Shraddha Joshi

Abstract-With the increasing reliance on technology for location-based services, finding an optimal and fair meetup spot for a group remains a challenge. This research proposes a machine learning-based approach to recommend the most suitable meetup location based on the geographic inputs of multiple users. The system utilizes clustering algorithms to identify equidistant locations while incorporating user preferences such as venue type (e.g., cafes, parks, malls). Google Maps API is leveraged for real-time location data and distance calculations, while various machine learning models, including K-Means and DBSCAN, are compared for efficiency and accuracy. The system enhances decision-making by offering optimized suggestions, ensuring fairness and accessibility for all participants. Future improvements include incorporating real-time traffic data and personalized recommendations based on user behavior.

DOI: 10.61137/ijsret.vol.11.issue2.266

Using an Adaptive Learning Tool to Improve Student Performance and Engagement in a University Course
Authors:- Nikhil Bhamare, Dr Meenakshi Thalor

Abstract-College courses are haunted by participation and grade problems, particularly in large or online classes. This experiment investigates the impact of an adaptive learning (AL) system, CogBooks®, on student achievement in a blended statistics course. We employed a quasi-experimental experiment with two groups: an AL system (N=100) group and a group of students instructed using traditional techniques (N=100). Partic- ipation was measured with validated surveys, and performance was measured with standardized grades. Results indicated a statistically significant difference in the AL group, a 15 percent boost in average grades (p < 0.05) and a 20 percent boost in reported participation measures compared to the control group. The system’s real-time feedback and individually tailored learning paths efficiently addressed individual students’ needs. Surprisingly, our solution is offline-capable, offering accessibility in low-bandwidth settings—a major benefit compared to cloud- capable solutions. These findings present AL as a cost-effective, scalable solution to higher education, with potential applications to STEM and humanities courses. Long-term retention effects and compatibility with generative AI tools might be topics for future research.

DOI: 10.61137/ijsret.vol.11.issue2.267

Railway Train Accident Prevention System
Authors:- Harsh Malik, Mohd.Zubair

Abstract-Railway accidents remain one of the most critical concerns in transportation safety, causing severe casualties, infrastructure damage, and economic losses. With the increasing reliance on rail networks for both passenger and freight transportation, the need for an efficient and proactive accident prevention system is more crucial than ever. The conventional railway safety mechanisms primarily depend on human intervention, which is prone to errors and delays. This paper presents an Arduino-based Railway Train Accident Prevention System designed to detect potential hazards in real time and prevent accidents before they occur. The system integrates ultrasonic sensors, IR sensors, and GSM modules to detect obstacles, track fractures, and mechanical faults, allowing for immediate corrective action. The automated emergency braking system ensures that the train comes to a halt upon detecting an obstacle, significantly reducing collision risks. Additionally, the GSM module enables remote communication with railway authorities, alerting them to potential hazards and allowing swift intervention. This system is cost-effective, easy to implement, and scalable for various railway environments, from urban transit systems to remote tracks in rural regions. The integration of real-time monitoring, automated responses, and remote alert mechanisms ensures enhanced safety and efficiency in railway operations. Furthermore, the study explores potential improvements such as integrating AI-driven predictive analytics and GPS tracking, which could further optimize railway safety and accident prevention. Railway transportation is one of the most widely used modes of travel and freight movement across the world. However, despite its efficiency, railway accidents continue to pose serious challenges, resulting in loss of human lives, damage to infrastructure, and significant financial losses. The primary causes of railway accidents include track obstructions, derailments, signal failures, and mechanical malfunctions, which are often exacerbated by human error, adverse weather conditions, and inadequate track maintenance. Traditional railway safety measures depend largely on human supervision, making them prone to inefficiencies and delays. Hence, there is an urgent need for an intelligent, automated system that can effectively detect hazards in real time and take immediate corrective actions to prevent accidents before they occur. This paper proposes an Arduino-based Railway Train Accident Prevention System that utilizes multiple sensors, automation, and wireless communication to enhance railway safety. The system is designed to detect obstacles, track fractures, and other mechanical faults through the integration of ultrasonic sensors, infrared (IR) sensors, and GSM modules. The ultrasonic sensors are responsible for identifying any obstructions on the railway tracks, while IR sensors help in detecting cracks or faults in the railway tracks. Additionally, the GSM module plays a crucial role in remote communication by sending immediate alerts to railway authorities in case of detected hazards, enabling swift action to mitigate risks. One of the most crucial features of this system is the automated emergency braking mechanism, which is activated as soon as an obstacle is detected in the train’s path. By automatically stopping the train before a collision can occur, this feature significantly reduces accident risks and enhances passenger safety. Unlike conventional safety systems, which require constant human monitoring and intervention, this smart, automated solution operates independently, ensuring continuous surveillance and real-time response to potential threats. This minimizes human dependency and reduces the chances of delayed accident prevention measures. The proposed system is designed to be cost-effective, scalable, and adaptable, making it suitable for implementation in various railway infrastructures, including high-speed rail networks, freight transport, and suburban train systems. Additionally, the research explores potential enhancements such as AI-driven predictive analytics, GPS tracking, and machine learning algorithms that could further optimize railway safety by predicting potential risks before they arise. Future advancements could also enable data-driven predictive maintenance, ensuring that railway authorities can proactively address track faults before they escalate into major safety hazards. By leveraging modern technological advancements, this research presents a comprehensive and intelligent approach to railway accident prevention. The integration of real-time monitoring, automated braking, wireless alerts, and data-driven analytics ensures that railway operations are not only safer but also more efficient and reliable. This system provides a promising solution to the global challenge of railway safety and aims to revolutionize railway transportation by significantly reducing accident rates and improving overall operational efficiency.

Advanced Machine Learning Approaches for Detecting Phishing Websites
Authors:- Ms.Gauri Shamkant Dighe

Abstract-The development of new methodologies for identifying phishing attacks in the context of an increasing digital world is compromised due to lacking research and execution. This paper focuses on versatile approaches in Artificial Intelligence (AI) and Machine Learning (ML) to almost single-handedly eliminate phishing attempts. The work takes a holistic approach to problems of URL structure, content, and behaviors by XGBoost, LightGBM, Naïve Bayes, and CatBoost, as well as Graph Neural Network GNN. Multiple features are captured; for example, URL length, number of dots, slashes, numeric characters, and special characters will all be used for model training. Monitoring the system in real time and adapting it to new phishing paradigms makes it possible to tactically protect users and organizations from the continuous, unpredictable changes of cyber threats. This study covers the approach of employing diverse machine learning methods to combat phishing in a more direct and secure manner.

DOI: 10.61137/ijsret.vol.11.issue2.268

Detection of Human in Flames Using HOG & SVM
Authors:- kajal Hake

Abstract-This project is designed to assist in locating individuals trapped in fire emergencies by integrating two interconnected components: fire detection and human identification. The system employs the YCbCr color space standard to detect fire and flames within the environment. To identify individuals amidst the fire, it leverages the HOG combined with a SVM classifier. Motion-based feature selection techniques are utilized for human activity recognition in video sequences. To ensure seamless operation of both modules, they are systematically integrated. Fire detection is carried out using a trained model that incorporates a diverse range of human feature sets. Additionally, moving objects are identified using a combination of a color median filter and background differencing, following four distinct rules. A critical aspect of this approach is the dependency between fire detection and human identification—ensuring that if a fire is detected, the system actively searches for trapped individuals. The primary objective of this methodology is to enhance the efficiency of locating individuals in hazardous fire conditions, enabling rapid rescue operations. This system can support firefighters in strategic decision-making and identifying high-risk zones.

DOI: 10.61137/ijsret.vol.11.issue2.269

Sentiment Analysis with Deep Learning for Social Media Texts: A Comprehensive Study
Authors:- Satwik Sanjay Garje

Abstract-This research document shows how deep learning helps detect emotional content in social media data. Natural language processing (NLP) department Sentiment analysis discovers text sentiment orientation within documents. Thanks to social media’s fast growth companies and researchers depend on understanding verbal reactions from content producers. Because social media texts follow informal writing styles with slang words and emojis plus frequent hashtag usage they require special handling methods. Deep learning models including CNNs RNNs and Transformers now better recognize linguistic details from text datasets than ever before. The research examines every aspect of using deep learning methods to analyse social media text sentiment including the tools, data sets used, present problems and future prospects. This study proposes new methods to create better sentiment analysis systems for social media platforms.

DOI: 10.61137/ijsret.vol.11.issue2.270

A Framework for Non-Invasive Wearable Health Monitoring Using Flexible Biosensor Tattoos
Authors:- Assistant Professor Mrs. Anuja S. Phapale, Shreyas Patil, Saurabh Patil

Abstract-The increasing demand for continuous, non-invasive health monitoring has spurred innovations in wearable technology. This paper presents a conceptual framework for a flexible, skin-adhering biosensor tattoo designed to monitor vital physiological parameters-heart rate, hydration levels, and glucose concentration-in real time. Leveraging photoplethysmography (PPG), bioimpedance analysis (BIA), and sweat-based electrochemical sensing, the proposed system integrates ultra-thin sensors, a low-power microcontroller, and Bluetooth Low Energy (BLE) for wireless data transmission to a mobile application. The framework emphasizes energy efficiency through a hybrid power system combining flexible micro-batteries with thermoelectric and piezoelectric energy harvesting. A user-friendly mobile interface provides live health metrics, AI-driven anomaly detection, and historical trend analysis, enhancing proactive healthcare. Unlike conventional wearables, such as smartwatches and continuous glucose monitors, this tattoo-based system offers superior comfort, non-invasiveness, and customization potential, addressing limitations like bulkiness and frequent charging. The design incorporates waterproofing via hydrophobic nano-coatings and biocompatible materials, ensuring durability and skin safety. While currently a research-based concept, the framework builds on established technologies, demonstrating feasibility through existing sensor methodologies and flexible electronics research. This paper outlines the system architecture, technical workflows, and potential applications, targeting athletes, diabetic patients, and health-conscious individuals. Future directions include prototype development, clinical validation, and expanded health metric integration. The proposed system promises to redefine wearable health technology by merging advanced biosensing with seamless, everyday wearability.

DOI: 10.61137/ijsret.vol.11.issue2.271

AI-Powered Smart Agricultural Ecosystem: Enhancing Weather Prediction, IoT-Based Soil Health Monitoring, Direct Market Access, and Financial Services for Farmers
Authors:- Ms. Anusshka Prakash Teli

Abstract-As we know the ongoing challenges of agriculture sector necessitate the integration of advanced technology to ensure sustainable farming. Agriculture plays a crucial role in our life as food is the important part of human survival, it is necessary to look out for the ongoing problems in agriculture sector. With increasing challenges to the agricultural sector, the integration of advanced technologies into sustainable farming practices is essential to enhance productivity. Being the backbone of the global economy, agriculture faces threats from climate change, resource inefficiencies, and market access limitations. This paper introduces an AI- Powered Smart Agricultural Ecosystem that combines advanced technologies to provide holistic solutions to these issues. The system is supposed to enhance the prediction of weather through AI and allows farmers to take the right decisions in planting and harvesting. IoT-based soil health monitoring helps to assess soil in real-time to save efficient usage of resources, ensuring that crop yield is improved. Blockchain technology gives farmers direct access to the market, thereby reducing intermediaries between buyers and sellers and ensuring transactions are transparent. AI-powered financial services will give personalized credit scores and microloans to facilitate the farmers. By harnessing the most recent advances in technologies, such as machine learning, IoT, and blockchain, this system bridges existing gaps in agricultural research and practices. Unlike the typical approaches, this proposed framework combines predictive analytics, real-time monitoring, and financial inclusion into one integrated ecosystem. While it increases productivity, this will also promote environmental sustainability by improving resource use efficiency. This research is novel because it fully integrates technology to tackle the critical challenges that farmers face. This ecosystem is expected to increase profitability, reduce resource wastage, and ensure better market access, thus significantly contributing to the transformation of the agricultural sector into a more resilient and sustainable domain.

DOI: 10.61137/ijsret.vol.11.issue2.272

Satellite Image Analysis for Agricultural Field Forecasting Using Machine Learning
Authors:- Sarthak Harshad Belvalkar, Dr Meenakshi Thalor

Abstract-With the progression of machine learning methods recently, a branch of artificial intelligence was revealed for forecasting and prediction in agriculture field. That is a benefit to works that have to do with agriculture. Recent developments in agricultural practices and methods have highlighted the importance of accurate monitoring, particularly with regards to field monitoring such as paddy areas, in order to take timely control measures for food security and other supportive actions. Moreover, regular monitoring of an area, a landscape, and the entire earth is beneficial by using one of the important sources, that is satellite images, providing information through multi-temporal images. Best source of images complexity because they are indifferent of atmospheric conditions like wind, sun light etc. It combines deep learning specifically convolutional neural networks (CNNs)–and satellite imaging to model crop yield. We suggest a hybrid model that uses data from various sources and real-time integration to provide scalability, accuracy, and reliability to solve practical challenges, such as dataset diversity as well as computational efficiency.

DOI: 10.61137/ijsret.vol.11.issue2.273

AI/ML-Driven Phishing Defence: Crafting a Robust Email Security Framework
Authors:- Sahil Milind Gedam

Abstract-Phishing attacks, which use people’s vulnerability to trick them into disclosing personal information, continue to be the most widespread threat type, at least for the time being. These types of attacks typically involve phony emails purporting to be from reputable sources, such as banks, companies, or government buildings. Even though standard email filtering methods are somewhat helpful in the fight against phishing, they are very unlikely to detect sophisticated phishing channels like spear phishing and zero-day phishing that are used today. As a result, using machine learning (ML) and artificial intelligence (AI) to address email security is becoming more popular. They can learn from sampled volumes of emails and use that knowledge to better identify phishing and non-phishing emails. Here, we suggest developing a phishing detection system with AI/ML, which will be instrumentally essential to ensuring dependable and flexible email security. To categorize emails according to features taken from the subject, body, and links of the emails, the system uses Random Forest, Support Vec- tor Machines (SVM), and Neural Networks. We trained and evaluated these models to determine the feasibility of phishing identification using both phishing and benign email corpora. The study’s accomplishments included a higher detection accuracy in comparison to traditional methods and a further decrease in misrecognition, both of which enhance security overall. Notably, the suggested system is robust and adaptable to sophisticated phishing attacks by combining a multi-model approach with learning mechanisms.

DOI: 10.61137/ijsret.vol.11.issue2.274

Dynamic Profits: Leveraging Reinforcement Learning in Evolving Financial Markets
Authors:- Dr Meenakshi Thalor, Kamlesh Nanasaheb Bari

Abstract-Electronic trading or algorithmic trading has changed the landscape of financial markets as data is processed and analyzed, which enables instant decision making. The application of reinforcement learning (RL) in algorithmic trading has the ability of constant improvement and optimization in ever- changing environments. Autonomous, intelligent systems that can operate in the unpredictable financial market conditions are required at an ever-growing rate. Trading agents can learn market optimal decision-making strategies through reinforcement learning, which makes it a good fit for real-time usage. The primary aim of this study is to overcome the challenge posed by the traditional algorithmic trading approaches that target high market volatility and non-stationary data using pre- programmed strategies. Most of the published studies are concentrated on the theoretical aspects of the models while very little attention is given to their application, transaction cost, slippage, and market impact. In RL based trading systems, learning needs to be stable or the trader risks overfitting, setting risk parameters for exploration and exploitation can also Markov Decision Process be very difficult. We develop a custom RL framework that compensates for transaction costs at the breakeven point, where other methods fail. Rather than focusing on other reward functions, our method can actually be implemented in real-time trading situations.

DOI: 10.61137/ijsret.vol.11.issue2.275

AI-Enhanced Symptom Checker Using BioBERT for Disease Prediction
Authors:- Assistant Professor Mrs.Punashri Patil, Siddhi Uttekar, Pooja Shingade

Abstract-Precise disease diagnosis using symptoms is of paramount importance in efficient healthcare but is frequently incomplete in conventional symptom-checking frameworks that depend upon rule-based techniques or sparse data. Pre-trained in biomedical text for transfer learning tasks, the NLP model literature, for predicting diseases through patient-reported symptoms. Through Bio BERT fine-tuning on open-source symptom-disease datasets, the system accurately maps symptoms to potential diseases, overcoming limitations like symptom variability and overlapping disease presentations. The proposed approach is compared with Naive Bayes (NB), as well as other conventional machine learning models, This includes Bayes, Random Forest, and Support Vector Machines(SVM).Experimental outcomes illustrate that the fine-tuned Bio BERT model has an accuracy rate of 89%, surpassing conventional methods by far. The system is also equipped with capabilities to improve and learn over time by incorporating user feedback to enhance its predictions. This study identifies the possibility of AI-driven symptom checkers to transform healthcare by offering real-time, accurate, and individualized disease prediction, alleviating the pressure on healthcare systems, and enhancing patient outcomes.

DOI: 10.61137/ijsret.vol.11.issue2.276

Smart Agriculture using Machine Learning
Authors:- Dipika Medankar, Dariyan Naagar, Aakanksha Nimbalkar, Prajwal Naukarkar, Assistant Professor Mrs. Anuja S. Phapale

Abstract-Crop productivity is paramount to world food security, and precise crop yield forecasting is critical to maximizing farm operations. Classic forecasting techniques hardly consider the intricacies of interaction between climatic factors, soil properties, and crop growth stages. The rapid progress in Machine Learning (ML) and Deep Learning (DL)in recent years has transformed crop prediction by utilizing extensive data from meteorological archives, soil sensors, and remote sensing technologies. This research examines different ML methods, such as Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Hybrid AI models, to improve crop yield prediction. By incorporating major agrarian parameters like temperature, rainfall, humidity, soil moisture, and nutrient levels, AI-based models can offer more accurate and dynamic predictions, supporting farmers and policymakers in decision-making.The paper also addresses issues like data quality, model interpretability, and climate change adaptation, and possible solutions like IoT-based real-time monitoring and Explainable AI (XAI).

DOI: 10.61137/ijsret.vol.11.issue2.277

Project Management of Metallurgy Microscopes: From Design to Manufacturing
Authors:- Viraj Vijay Dare, Professor Amol Baviskar

Abstract-Metallurgy microscopes are critical tools in material analysis, quality control, and industrial research. The manufacturing of these microscopes involves intricate processes, including design, prototyping, testing, and final production. Efficient project management is essential to ensure cost-effectiveness, risk mitigation, and product quality. This study examines the project management methodologies used in the development of metallurgy microscopes, highlights key challenges, and provides recommendations for enhancing efficiency in the production lifecycle.

Symbiotic UX Design Interfaces
Authors:- Aashish Manchanda

Abstract-The integration of Artificial Intelligence (AI) into human-environment interactions offers transformative potential for promoting sustainability and enhancing ecological understanding. This paper explores the concept of symbiotic interfaces—platforms facilitating seamless collaboration between AI and humans—to foster informed decision-making and sustainable practices. Through user interviews and survey analyses, we examine public perceptions, challenges, and future directions in developing AI-driven systems that augment human capabilities while promoting environmental stewardship. Additionally, this study highlights real-world applications of AI in areas such as wildlife conservation, urban planning, and resource management, demonstrating how these technologies can create measurable environmental impact. By leveraging AI’s analytical power and human intuition, we propose actionable strategies for designing systems that empower users to adopt sustainable behaviours and make ecologically responsible choices.

Review of Evaluation of the water quality index in Gujarat Region
Authors:- Girish R. Lauhar, Bhagyesh C. Contractor, Girish D. Jagad

Abstract-The Water Quality Index (WQI) is a numerical indicator that summarizes the complex information about water quality into a single value. It is based on three types of parameters in water quality index (WQI) is physical, chemical, and biological parameter is that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) selection of parameter (2) assignment of weighted (3) calculation of water quality index parameter (4) overall rating each water quality parameter on world health organization (WHO) or (bureau of Indian standard (BIS). Water quality rating and status (0-25)-Excellent water quality, (26-50)-Good water quality, (51-75) -Poor water quality, (76-100) -Very poor water quality, (>100) -Unsuitable for drinking. After giving water quality rating, we will check whether the water is dirty or not. If it is dirty, then do not use it. For usefulness, treat it and then use it for drinking.

A Study on Role of Technological Innovation in e-Entrepreneurship
Authors:- Assistant Professor P. Siva Kumar, Assistant Professor Garima Agrawal

Abstract-The industry 4.0 characterized by the integration of digital technologies, automation and data into business operations Entrepreneur is an innovative thinker committed to the company he puts innovations and inventions into Connecting Q-commerce with together. The most prestigious firm Infosys established Quantum Living labs(QLL) using quantum computing in various business sectors like Finance, logistics, healthcare, demonstration and many more. Nowadays many reputed companies are using Quantum technology for Big data analytics. The purpose of the present study is to identify the various factors that will affect global market entrepreneurship the issues and challenges. The data for the purpose of the study has been collected through Secondary sources, which mainly includes books, articles, magazines newspapers and websites.

Consumer Behavior Analysis
Authors:- Chirayu. R. Jain

Abstract-Consumer behavior has seen a remarkable change over the years, especially in the retailing industry where Kirana shops and street markets, which had been the consumers’ preferred destinations for shopping for a very long time are now fading away and the trend and preference has moved towards supermarkets. Through an analysis of consumer opinion on shopping behaviors, and major influencing factors, this study hopes to shed light on the forces driving this change. Objective of the research pertains to finding out consumer’s opinion on shopping grocery from traditional markets and modern huge supermarket complexes, and the reasons for shifting or adopting traditional means. Random sample of not more than 55 consumers of different age groups questioned through a questionnaire provided, various questions related to their opinion about purchasing patterns about shopping grocery & household items. Based on the data received, it is concluded that regardless of age group, supermarkets and Kirana stores are almost equally popular and preferred and hence, evolution of supermarkets has partially replaced Kirana stores, but it could not completely eradicate its presence in the market as supermarkets and Kirana stores have their own advantages and disadvantages.

Work –Life Balance of Women Employees: Challenges and Strategies
Authors:-Greeshma Muraly

Abstract-Women across the world encounter difficulties in managing what their professional work and personal life. These research paper exams the difficulties of workload and life balancing in women particularly among working women and entrepreneurs. It analysis the key factors affecting work life balance, the impact of overload on mental and Physical health and Strategies for achieving stability. This paper also highlights policies and support systems that can help woman in managing the responsibilities effectively.

DOI: 10.61137/ijsret.vol.11.issue2.278

A Study on Financial Planning of an Individual
Authors:-Mr. Siddhesh Somnath Wavhal, Professor A. V. Gupta

Abstract-The level of income and the proportion of savings reflect an individual’s standard of living within society. It is not sufficient to merely accumulate savings; it is equally important to invest them in profitable opportunities to ensure capital growth over time rather than letting it remain idle. Such investments circulate across various sectors, including households, the private sector, and government bodies. This flow of capital enables the economically weaker sections to access diverse financial assets, fostering both wealth creation and the expansion of financial services within the economy. Additionally, effective financial planning is crucial for every individual when making investment decisions. It involves setting clear investment objectives, understanding the expected rate of return, and assessing the risks involved. Financial planning is a strategic process where an individual evaluates their financial goals, identifies outstanding debts, and outlines the necessary steps to achieve these goals within a specific timeframe. It quantifies financial needs and helps in making informed decisions.

Data Breach Monitoring System
Authors:-Ms.Dhivya K, Abinaya M, Janaki R, Kavya S, Sowbarnika P N

Abstract-Current data breach monitoring systems provide continuous oversight of network activity and data access points, leveraging analytics and machine learning to detect unauthorized access to sensitive information. These systems generate real-time alerts for rapid response actions, such as isolating compromised devices and blocking suspicious IPs, while also supporting regulatory compliance through detailed activity logs. This project proposes enhancements in anomaly detection precision using adaptive machine learning algorithms that update behavior baselines dynamically and reduce false positives, improving threat identification accuracy. Additionally, it integrates seamlessly with automated incident response platforms for efficient containment and remediation. Future developments aim to incorporate predictive analytics to anticipate breaches by analyzing evolving behavioral trends, enhancing proactive defense capabilities and the system’s adaptability to emerging cyber threats.

DOI: 10.61137/ijsret.vol.11.issue2.279

Molten Salt Reactors: A Revolutionary Approach to Sustainable Nuclear Energy
Authors:-Mr. Vishwanath G Barve, Miss. Kshitija V Joshi

Abstract-Molten Salt Reactors (MSRs) represent a paradigm shift in nuclear energy technology, offering inherent safety features, high efficiency, and reduced waste production. Unlike conventional solid-fuel reactors, MSRs operate using liquid fuel mixed with molten salts, allowing for greater thermal stability and fuel utilization. This paper explores the history, design principles, technological advancements, and future potential of MSRs in the global energy landscape. Additionally, we address the challenges associated with their implementation, including material compatibility, regulatory concerns, and economic feasibility.

Challenges Faced by Turmeric Exporters with Special Reference to Erode District
Authors:-Assistant Professor Dr. M. Kowsalya, Ms. V. Ashvitha

Abstract-Turmeric, a key agricultural product, holds significant economic importance for India, with Erode District in Tamil Nadu being one of the largest turmeric production hubs. However, turmeric exporters from Erode face a series of challenges that impede their competitiveness in the global market. These challenges include inadequate infrastructure, fluctuating market prices, lack of quality standardization, and the complexities of international trade regulations. Additionally, issues such as inconsistent supply due to climatic variations, pest attacks, and post-harvest losses further exacerbate the situation. This study explores these challenges in detail, focusing on their impact on the growth and sustainability of turmeric exports from Erode District, and provides recommendations for overcoming these hurdles through policy interventions, technological upgrades, and market diversification.

DOI: 10.61137/ijsret.vol.11.issue2.281

Heart-Disease System: A Rule-Based Prediction Model for Heart Disease Symptoms and Causes
Authors:-Chaudhari Khushikumari

Abstract-Heart disease remains one of the leading causes of mortality worldwide. Early detection and prevention are crucial to reducing the risks associated with cardiovascular diseases. This paper presents a rule-based predictive system for heart disease diagnosis. The system leverages patient data, including medical history, lifestyle factors, and clinical symptoms, to provide an accurate assessment of potential heart disease risks. Implemented in Python, the system follows a structured decision-making approach based on medical guidelines and expert knowledge. The proposed system aids in decision-making for healthcare professionals and individuals by evaluating multiple health indicators to predict heart disease risk. This research paper discusses the dataset used, feature selection, rule formulation, system architecture, and evaluation criteria. The study demonstrates that expert-driven approaches can enhance early detection and contribute to improved patient outcomes.

DOI: 10.61137/ijsret.vol.11.issue2.282

Development of an E-commerce Website for Inventory Management and Efficient Online Shopping Delivery
Authors:-Fenil Mehta

Abstract-This paper details the development and implemen- tation of a robust e-commerce website designed to seamlessly integrate inventory management and efficient online shopping delivery. The platform leverages the power of PHP for dynamic website functionality and MySQL for reliable and scalable database management. The primary objective of this project is to create a user-friendly system that simplifies inventory tracking for administrators while simultaneously providing a seamless and satisfying shopping experience for end-users. The website achieves this by dynamically updating stock levels in real- time, automating order processing from placement to fulfillment, and integrating a comprehensive delivery system to ensure smooth and timely delivery of purchased goods. This integrated approach aims to minimize manual intervention, reduce errors, and improve overall operational efficiency for businesses. The system’s features include a user-friendly interface for browsing and purchasing products, a secure payment gateway integration for safe transactions, and a detailed order tracking system that keeps customers informed about their purchases. Furthermore, the administrative backend provides tools for managing product catalogs, tracking inventory levels, generating reports, and over- seeing the delivery process. The solution is designed to be scalable and adaptable to the evolving needs of businesses, ultimately supporting them in streamlining their operations, optimizing resource allocation, and improving customer satisfaction through a more efficient and reliable e-commerce platform. The paper will further discuss the system architecture, implementation details, testing procedures, and potential future enhancements.

Memory-Augmented Large Language Models: Overcoming Catastrophic Forgetting in Continual Learning
Authors:-Pavan Kumar Adepu

Abstract-This paper proposes a novel strategy for mitigating catastrophic forgetting of lifelong learning via memory-augmented large language models. Coupling external memory modules with standard deep learning frameworks, our methodology enables the model to retain context information over long periods of time and retrieve such information, preventing previously learned facts from being overwritten by new input data. We demonstrate our approach on the real WikiText-103 dataset, with the results of our experiments showing an extensive improvement in the retention of long-term dependencies and overall model performance. Our findings suggest that memory augmentation is a promising means to enhance the resilience of language models in ever-changing, dynamic settings and laying the groundwork for more robust and adaptable continual learning systems.

DOI: 10.61137/ijsret.vol.11.issue2.283

Industrialization of Data Science Process Transitioning From Artisanal to Scalable Workflows
Authors:-Swati Kashinath Pawar

Abstract-The growing reliance on data science for business intelligence, automation, and decision-making necessitates a shift from artisanal, manually driven approaches to industrialized, automated, and scalable workflows. This paper explores methodologies, platforms, and MLOps (Machine Learning Operations) systems that facilitate the transition, increasing productivity, reducing errors, and improving deployment rates. By analyzing best practices and case studies, this research highlights the importance of standardization, automation, and integration in modern data science workflows. By industrializing data science, businesses can reduce human intervention, minimize errors, accelerate model deployment, and ensure regulatory compliance. This shift enables organizations to extract greater value from data, optimize operational processes, and foster innovation. However, challenges such as data governance, ethical AI considerations, and maintaining model interpretability must be addressed for successful implementation. As data science matures, organizations are shifting from artisanal, manually intensive workflows to scalable, automated processes that enhance efficiency, reproducibility, and deployment speed. Traditional data science workflows often rely on custom, one-off scripts and ad-hoc methodologies, which hinder scalability and collaboration. This transition involves adopting standardized frameworks, automation tools, and cloud-based infrastructures that streamline data preprocessing, model training, deployment, and monitoring.

Deep ThyroidScan: Multilayer Recursive Neural Network (ML-RNN) for Accurate Detection and Classification
Authors:-Dr.RadhaKrishna, L.Durga Sarath Kumar, D.Sai Karthikeya, L.V.M.Rajeswari, K.Sai Durga, Y.Sambasiva Rao

Abstract-Thyroid disease is one of the most prevalent illnesses worldwide, affecting over 42 million individuals in India alone. The thyroid gland, a small organ located in the neck, plays a crucial role in regulating metabolic processes by secreting essential hormones. Any dysfunction in the thyroid gland can significantly impact overall health. Accurate testing for thyroid disorders is vital for effective treatment, as early diagnosis can help balance hormone secretion and mitigate related complications.However, the increasing number of thyroid patients and the shortage of medical professionals pose challenges to traditional diagnostic methods. To address these issues, a deep learning-based Multi-Layer Recursive Neural Network (ML-RNN) is employed to enhance diagnosis. This approach focuses on preprocessing the input data, selecting relevant features from standard datasets, extracting key attributes, and classifying thyroid conditions into normal, hyperthyroid, and hypothyroid categories.The first stage of this process involves preprocessing, which includes data cleaning, splitting, and handling missing values to enhance data quality. Next, feature selection is performed using the Fisher score method to identify an optimal subset of features. Data analysis is then conducted based on Region-of-Interest (ROI) volumes. Finally, classification is carried out using ML-RNN, which improves accuracy in detecting thyroid disorders and assessing the risk of developing the disease. The model demonstrates high performance in terms of accuracy, recall, positive predictive value, and negative predictive value, making it a reliable tool for thyroid disease prediction.

DOI: 10.61137/ijsret.vol.11.issue2.284

Hybrid Physics-Guided Deep Transfer Learning for Accurate Traffic State Estimation
Authors:-Mrs.V.Anantha Lakshmi, Geetha Usha Sri, M.Sri Harshitha Meghana, N.Dhathri, M.Chaitanya, P.SrujanaSai.

Abstract-Accurately estimating traffic states is a crucial aspect of transportation engineering, enabling effective traffic control and operations. In recent years, Physics-Regulated Deep Learning (PRDL) has gained significant attention due to its ability to achieve higher accuracy while requiring less training data compared to conventional deep learning (DL) approaches. However, a key challenge of PRDL is the lengthy training time required for closely related but distinct tasks.To address this limitation, this paper introduces a hybrid physics-regulated deep transfer learning approach that leverages the strengths of transfer learning, PRDL, and DL to enhance estimation accuracy and reduce computational costs, particularly in scenarios with limited observation data. The proposed framework includes two transfer learning variants designed to extract and transfer essential features from pre-trained models to new but similar traffic environments. This hybrid approach integrates deep learning training, minimizing computational overhead by eliminating physics-based loss calculations during training.Simulation results demonstrate that, compared to traditional PRDL methods, the proposed transfer learning approaches improve estimation accuracy by over 12% on average while reducing training time by more than 50% on average. These findings highlight the potential of hybrid transfer learning techniques in accelerating the adoption of PRDL for traffic state estimation, making it a valuable tool for transportation systems with limited computational resources.

DOI: 10.61137/ijsret.vol.11.issue2.285

AI-Driven Business Intelligence: Machine Learning-Powered Dynamic Pricing Strategies for E-Commerce Optimization
Authors:-Mr.A.Janardana Rao, S.Sri Gowri Sai Priya, I.Rupa Kamalini, D.Keerthi Sri, B.Mohan Kalyan, M.D.N.Chaitanya Lahari

Abstract-The rapidly evolving e-commerce landscape demands dynamic pricing strategies to maximize revenue and maintain a competitive edge. This study examines the integration of machine learning (ML) and business intelligence (BI) to enhance pricing strategies, addressing the shortcomings of outdated models in adapting to digital market shifts. While ML has proven valuable in various business applications, its potential for dynamic pricing in e-commerce remains underexplored, particularly when combined with BI. Existing research lacks a comprehensive analysis of how these technologies can work together for pricing optimization. To bridge this gap, the study employs the Support Vector Machine (SVM) algorithm, known for handling complex and nonlinear relationships in large datasets. By leveraging BI tools to collect, process, and analyze crucial data, the approach establishes a real-time pricing framework. The findings reveal that ML-powered BI systems significantly enhance a company’s ability to set accurate prices and swiftly respond to market fluctuations. The adaptability of the SVM model ensures pricing decisions are both precise and responsive to dynamic market conditions, leading to a more effective and competitive pricing strategy.

DOI: 10.61137/ijsret.vol.11.issue2.286

A Unified HAPS-LEO NTN Architecture for 6G: Enabling Hybrid RF-FSO Backhaul and Distributed Federated Learning
Authors:-Aakash Jain, Prakhar Vats, Priyanshu Singh, Shreya Tiwari. Mohammed Alim

Abstract-As communication systems evolve towards beyond-5G and 6G, the demand for high data rates, minimized latency, global connectivity, and distributed intelligence intensifies. Traditional terrestrial and backhaul networks face limitations in scalability, bandwidth, and coverage, particularly in challenging environments. This paper proposes a unified, multi-tier Non-Terrestrial Network (NTN) architecture integrating Low Earth Orbit (LEO) satellites and High Altitude Platform Stations (HAPS) to address these challenges. We explore a hybrid RF-Free-Space Optical (FSO) communication model to leverage the strengths of both technologies, enhancing backhaul efficiency and resilience against atmospheric disruptions. The architecture incorporates Contact Graph Routing (CGR) for optimized data routing in dynamic backhaul scenarios and a distributed Hierarchical Federated Learning (HFL) framework, utilizing HAPS as intermediate servers, to enable privacy-preserving, scalable machine learning across the network. This unified approach offers a versatile platform for future communication systems, supporting both high-performance backhaul and distributed intelligence. Simulated performance results, adapted from component studies, demonstrate the potential advantages of this integrated architecture in terms of latency, throughput, scalability, and learning accuracy.

DOI: 10.61137/ijsret.vol.11.issue2.287

AI-Powered Fraud Detection: Secure Online Transaction Monitoring Using Machine Learning
Authors:-Mrs.G.Tejasri Devi, T.Sai Srinath, G.Naga Kastusi, V.Anshitha, G.Janitha Sree, G.Jashwitha

Abstract-Fraud detection remains one of the most critical challenges in financial transactions, driving on going research and the adoption of advanced technologies such as machine learning. Financial transaction fraud detection aims to explore and compare various machine learning approaches to assess their effectiveness, challenges, and potential future developments comprehensively.This paper begins by highlighting the importance of fraud detection in financial transactions, emphasizing the widespread impact of fraudulent activities on individuals, businesses, and the overall economy. While traditional fraud detection methods have been valuable, they often struggle to counter increasingly sophisticated and evolving fraudulent schemes. As a result, more advanced techniques are required to enhance detection accuracy.Machine learning-based approaches have emerged as a promising solution, enabling algorithms to analyse vast amounts of transactional data and identify patterns indicative of potential fraud. In particular, supervised learning techniques—such as logistic regression, decision trees, and support vector machines—have gained significant popularity in fraud detection due to their ability to classify transactions as legitimate or fraudulent based on historical data.

DOI: 10.61137/ijsret.vol.11.issue2.288

Rapid Depression Detection Using Extreme Learning Machine: An AI-Driven Approach
Authors:-Assistant Professor Mrs.G.V.Rajeswari, Ch.Harikiran, K.L.Rishitha, K.H.Venkat Ganesh, B.Raj Kumar, V.L.Apoorva.

Abstract-Depression is one of the most prevalent psychological and mental health disorders, affecting a significant number of people worldwide. In recent years, Extreme Learning Machine (ELM) techniques have gained preference for addressing various health-related disease detection and prediction challenges. ELM is a single hidden layer feed-forward neural network (SLFN) that offers significantly faster convergence compared to traditional machine learning (ML) methods while delivering promising results. Although numerous studies have explored the application of ML models for depression detection, limited research has focused on utilizing ELM for this purpose. This study implements Extreme Learning Machine (ELM) alongside other ML techniques for depression detection, comparing their performance. The results demonstrate that ELM outperforms other methods, achieving the highest accuracy of 91.73%.

DOI: 10.61137/ijsret.vol.11.issue2.289

AI-Powered Ransomware Defence: Cutting-Edge Machine Learning Techniques for Threat Detection
Authors:-Assistant Professor Mrs.P.Satyavathi, M.Naga Sai Ganesh, N.V.Gowtham Kumar, V.S.V.Satya Yaswanth, G.Satya Nandini, V.Giri Sathvika.

Abstract-The increasing frequency and sophistication of ransomware attacks, there is a growing need for dynamic and effective detection and mitigation strategies. Traditional signature-based approaches often fall short in identifying new and evolving ransomware variants. This paper explores the application of machine learning techniques for ransomware detection, aiming to enhance the accuracy and adaptability of detection mechanisms. It provides a comprehensive analysis of various machine learning methods and algorithms, evaluating their effectiveness in identifying ransomware patterns. The findings offer valuable insights into the advancement of cybersecurity solutions, emphasizing resilience and proactive defense against the ever-evolving ransomware threat landscape.

DOI: 10.61137/ijsret.vol.11.issue2.290

DeepSpineNet: Advanced Deep Learning for Multi-Class Spine X-Ray Condition Classification
Authors:-Assistant Professor Mrs.L.Yamuna, D.Sahithi, S.Sri Divya, A.Chakri, S.Asritha, J.Sree Varenya.

Abstract-Addressing the complexchallenges of automated spine X-rayanalysis, our research introduces Deep Spine, a deep learning model designed for the multi-class classification of diverse spine conditions. Utilizing Convolutional Neural Networks (CNNs), Deep Spine demonstrates exceptional proficiency in identifying a range of spinal abnormalities, including Scoliosis, Osteochondrosis, Osteoporosis, Spondylolisthesis, Vertebral Compression Fractures (VCFs),Disability, Other, and Healthy cases. Trained on a Kaggle dataset, Deep Spine achieves high accuracy and robustness, ensuring reliable performance in classifying spinal conditions. The incorporation of transfer learning techniques further enhances its generalization capability, enabling the model to adapt effectively across different datasets. This approach not only strengthens its diagnostic accuracy but also highlights its potential for automated diagnosis and decision support in musculoskeletal radiology. This research contributes to the evolving intersection of artificial intelligence and medical imaging, demonstrating the transformative potential of deep learning in spine X-ray analysis. By leveraging AI-driven advancements, Deep Spine offers a promising step toward enhancing clinical outcomes, improving diagnostic precision, and revolutionizing spinal healthcare.

DOI: 10.61137/ijsret.vol.11.issue2.291

Interpretable AI for Intelligent Event Detection and Anomaly Classification in Healthcare Monitoring Systems
Authors:-Assistant Professor Mrs.K.S.R.Manjusha, D.Ashok Kumar, M.Harish, M.Hari Sathvik, M.Vinsy, A.Sri Sai Keerthi.

Abstract-Artificial intelligence (AI) is transforming healthcare by automating the detection and classification of events and anomalies, enhancing patient monitoring and intervention. In this context, events refer to abnormalities caused by medical conditions such as seizures or falls, while anomalies are erroneous data resulting from sensor faults or malicious attacks. AI-based event and anomaly detection (EAD) enables early identification of critical issues, reducing false alarms and improving patient outcomes. The advancement of Medical Internet of Things (MIoT) devices has further facilitated real-time data collection, AI-driven processing, and transmission, enabling remote monitoring and personalized healthcare. However, ensuring the transparency and explainability of AI systems is crucial in medical applications to foster trust and understanding among healthcare professionals. This work presents an online EAD approach utilizing a lightweight autoencoder (AE) on MIoT devices to detect abnormalities in real time. The detected abnormalities are then explained using Kernel SHAP, a technique from explainable AI (XAI), and subsequently classified as either events or anomalies using an artificial neural network (ANN). Extensive simulations conducted on the Medical Information Mart for Intensive Care (MIMIC) dataset demonstrate the robustness of the proposed approach in accurately detecting and classifying events, regardless of the proportion of anomalies present.

DOI: 10.61137/ijsret.vol.11.issue2.292

Enhancing Cloud Security Using Blockchain-Based Authentication
Authors:-Assistant Professor Mrs. Punashri Patil, Yash Chavhan, Savi Dhoble, Tejas Patil

Abstract-Cloud computing is essential for modern enterprises, providing scalable and cost-efficient solutions for data storage and processing. However, security challenges such as unauthorized access, data breaches, and insider threats persist. Traditional authentication methods like passwords and two-factor authentication (2FA) have inherent vulnerabilities, including phishing attacks, credential theft, and centralized failures [1][2]. Blockchain-based authentication offers a decentralized, tamper-proof security mechanism that eliminates single points of failure and enhances trust. Existing research has explored blockchain’s role in cloud security, but challenges such as scalability, computational overhead, and latency remain [3]. This paper presents an optimized blockchain-based authentication model that enhances access control while addressing these limitations. Our approach leverages decentralized identity management, smart contract-based access control, and an efficient consensus mechanism to improve security, reduce computational overhead, and ensure seamless authentication. This model enhances security, scalability, and performance in cloud environments, making it a viable alternative to traditional authentication systems.

DOI: 10.61137/ijsret.vol.11.issue2.293

Skin Disease Detection Using Image Processing and Machine Learning
Authors:-Prathvish Shetty, Nikhil Shinde, Ashirwad Swami, Sahil Hanwate

Abstract-Skin diseases affect millions worldwide, making early and accurate diagnosis essential for effective treatment. Traditional diagnostic methods rely on manual visual inspection, which can be subjective and prone to errors due to variations in expertise and environmental factors. Misdiagnosis or delayed treatment can lead to severe complications, especially in conditions like melanoma. This paper presents an automated approach to skin disease detection using image processing and machine learning. The proposed system enhances image quality through preprocessing, extracts crucial features, and classifies skin conditions using algorithms like Support Vector Machine (SVM) and Convolutional Neural Networks (CNN). Experimental results demonstrate high classification accuracy, highlighting AI’s potential in dermatology for faster and more consistent diagnoses. By integrating artificial intelligence into dermatological assessments, this research aims to bridge the gap between conventional diagnosis and AI-assisted solutions, making skin disease detection more accessible, precise, and efficient.

DOI: 10.61137/ijsret.vol.11.issue2.294

Artificial Intelligence in Cyber Security
Authors:-Karmvirsinh Jadeja, Chirag Chauhan, Prof. Mansi Gosai

Abstract-The development of Artificial Intelligence (AI) has found some uncommon inertia from technological advancement. The world of AI appears everywhere and raises questions of admiration and censure. Its increasing usage has both pros and cons in the domain of cyber security, and it is a regular item in the development and operational processes of advanced technologies. This paper is a deep dive into the use of AI in cyber security, focusing on its advantages, challenges, and discriminating negative impacts. It also studies AI-based models that can enhance or compromise safety concerning different infrastructures and cyber networks. The paper critiques the participation of AI in postulating cyber security applications, suggests ways to chalk out the birth of new technologies against the threats and weaknesses generated from AI, and comments on the socio-economic implications of AI interfering with cyber security.

DOI: 10.61137/ijsret.vol.11.issue2.295

Miscellaneous Trends in it
Authors:- Yuvraj Lolage, Sara Lonare, Aditi Londhe, Mrs. Anuja S. Phapale

Abstract-The vast, ever-shifting landscape of human innovation information technology (IT) stands as both a mirror and a catalyst of our collective aspirations. Information technology repeatedly shapes our modern world, exerting influence upon government, industries and daily life. Beyond the headline-grabbing revolutions of artificial intelligence, cloud computing, and blockchain lie quieter, yet equally transformative, currents of change. As humanity ventures further into the digital age, it becomes clear that technology is not merely a tool; it is a partner in shaping the narrative of progress and a testament to the boundless curiosity that drives us to explore the unknown. This paper seeks to delve into these emerging trends, exploring their technical gradation and their broader implications for society. By analyzing their significance, potential applications, and implications for the future, the study aims to provide a comprehensive understanding of how these emerging trends are influencing the broader IT domain.

DOI: 10.61137/ijsret.vol.11.issue2.296

Redesigning Metal Motor Control Panel into Plastic
Authors:- Mayuresh Dhananjay Shedge

Abstract-This project encompasses many aspects and has several parts. We are working on an industrial project which will be implemented by the company- “TechArch Pvt. Ltd.” located in Chakan. Primarily, the project focuses on introducing a new product into the market as a replacement to an already existing product (panel which holds the controller, electrical parts and switches which operate a motor remotely). This existing product has a few shortcomings which have been overcome in our new product. Also, a lot of emphasis is given to reduce the cost of the product by implementing several methods like easing the manufacturing flow, choosing proper material and design, integrating two product designs so as to have a common mould for both, reducing child parts and removing extra steps in manufacturing. Our motive to pursue this project was to utilise prior knowledge in plastic injection moulding gained from internships in the field and recognizing and fulfilling a scope for a successful product which will prove profitable. The result will be a safe, affordable, lighter and aesthetic product which will fulfil all needs of the customers.

Mobile Controlled Water Garbage Collector Using Arduino
Authors:- Professor Kadar.S.Tamboli, Suraj.S.Ghaytidak, Makrand.V.Navale, Pratik.H.Patil

Abstract-This project focuses on designing a river waste collection system. Trillions of pieces of plastic currently pollute our seas, rivers, lakes, and oceans, harming marine life, contaminating ecosystems, and creating messes on beaches. Cleaning up plastic from the water is crucial, but the most effective methods to do so are still unknown. Today, much of the manufacturing process is automated to deliver products more efficiently. Automation plays a significant role in mass production. In this project, we have developed a remote-controlled water cleaning machine. The primary objective is to collect all the waste floating on water bodies while minimizing manual labor. We achieved this by creating a hardware prototype and using a microcontroller to control the machine’s components through a smartphone, utilizing Wi-Fi or Bluetooth connectivity. We have aimed to meet all the objectives for this product successfully so that it can be launched in the market.

EKLAVYA: 1-to-1 Online Tutoring Platform
Authors:- Ms Nimisha Amrutkar, Ms Mansi Gawade, Ms Manasi Patil, Professor Mohan Bonde

Abstract-In today’s world of technology, education has undergone a revolutionary shift towards online and personalized learning. Traditional classroom environments often fail to serve individual student needs, leading to a growing demand for one- to-one learning. The COVID-19 pandemic led to a rapid growth of online education, focusing on the need for effective virtual learning solutions. The main problem with virtual learning is the lack of personal interactions and individual support. As learning goes beyond traditional education, Eklavya platform encourages one-to-one learning and provides curated content with different connect tools for help. In addition to this, this platform provides an integrated solution where users can access essential features like resource sharing, parental control, video conferencing and progress tracking on a single platform. This makes Eklavya, more inclusive and user-friendly solution.

The Impact of Artificial Intelligence on Labor Markets: A Comprehensive Analysis
Authors:- Vanshika, Ishaan, Tanisha, Rashmi Ranjan, Aman Daga

Abstract-Artificial intelligence AI) is revolutionizing industries globally, significantly impacting labor markets. This paper explores AI’s dual role in job displacement and creation, skill evolution, and economic implications. Using secondary data from extensive literature, it identifies trends, challenges, and opportunities associated with AI-driven transformations in employment. The findings emphasize the importance of reskilling initiatives, ethical considerations, and policy interventions for sustainable workforce development. This research contributes to understanding AIʼs role in shaping the future of work while addressing gaps in existing studies.

Smart Parking Management Assessment Using Machine Learning Algorithms and IOT
Authors:-Assistant Professor Meenakshi Thalor, Ishwari Abuj

Abstract-With the growing number of vehicles and limited parking infrastructure, parking space man emerged as a major challenge in urban areas. In this paper, an extensive study of machine l models in an IoT-supported space is given, focusing on proposing an ML-based model that available parking space. The study compares the performance of several models Typed as (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic and Naive Bayes (NB) regarding to “precision, recall, accuracy, and F1-score performance results obtained after running ML models on the data with 65% and 85% threshold are com meaningful insights about their efficiency of prediction in parking vacancy. Random Forest (RF) model shows the best performance based on those metrics in all evalu high precision, recall, accuracy and F1-score values. The IoT-enabled environment shows t showing its effectiveness in falsely predicting parking space availability. In contrast, K- ne (KNNs), decision tree (DT), logistic regression (LR), predicting Naive Bayes (NB) with co exhibit relatively lower performance in crowded parking GLES scenarios. The paper ends deployment of intelligent predictive models, especially random forest, improves substantial and performance of smart parking system as well as it frees waiting time for cars, and henc parking resource utility as well as it decreases real-time travel congestion and increases use environments.

DOI: 10.61137/ijsret.vol.11.issue2.297

Smart Stroke Detection: Cutting-Edge Machine Learning and Optimized Algorithms for Early Diagnosis
Authors:-Mr. N.V.S Gopalam, K.Tanoosh, Ch.Sowjanya, Y.Navatej, K.Banny, B.Lakshmi Jahnavi

Abstract-Stroke, which ranks as the second leading cause of death worldwide, requires prompt and precise prediction for effective intervention. This research investigates the use of advanced machine learning techniques to improve stroke prediction models. Initially, classifiers such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) were applied, followed by the incorporation of advanced algorithms like Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) to enhance predictive accuracy. Various evaluation metrics, including accuracy, sensitivity, error rates, and log loss, were employed to assess the performance of the models. The findings demonstrate the effectiveness of machine learning algorithms, with XGBoost achieving an impressive accuracy rate of 98%. Additionally, LGBM played a significant role in boosting overall accuracy. These results highlight the critical contribution of advanced machine learning techniques to enhancing stroke prediction. By leveraging these state-of-the-art predictive models, the study advocates for their integration into clinical settings, aiming to expedite accurate diagnoses, improve patient care, and advance stroke detection capabilities. Keywords: Brain Stroke, Machine Learning, Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost).

DOI: 10.61137/ijsret.vol.11.issue2.298

Next-Gen Gait Recognition: Advanced Machine Learning for Precision Biometric Analysis
Authors:-Mr.Y.Ravi Bhushan, K.Charan Praveen Kumar, M.Sushma, T.Lasya Srivallika, Ch.Geetha Sri, K.D.V.Chaitanya

Abstract-Stroke, which ranks as the second leading cause of death worldwide, requires prompt and precise prediction for effective intervention. This study conducts a comprehensive exploration of gait recognition in biometric analysis, addressing the unique challenges of using gait as an identifier. It systematically evaluates various machine learning algorithms, including Individual Node Evaluation, Statistical Inference, Regression Modelling, Support Vector Machines, k-Nearest Neighbours, Decision Trees, Random Forests, and Neural Networks. Each model undergoes rigorous testing to assess its effectiveness in accurately identifying individuals based on their gait patterns. The methodology emphasizes thorough preprocessing to maintain data integrity and relevance, incorporating Sequential Backward Selection (SBS) for feature selection and dimensionality reduction techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to enhance model efficiency. Additionally, the study explores deep learning architectures, analysing their impact on recognition accuracy. A detailed comparative analysis highlights the strengths and weaknesses of each approach, offering valuable insights into the field. By evaluating a range of ML and DL techniques, this research sets a benchmark for future advancements in biometric security, reinforcing gait recognition as a reliable, non-invasive identification method and paving the way for advanced biometric systems in security and personal identification.

DOI: 10.61137/ijsret.vol.11.issue2.299

Intelligent Railway Track Fault Detection Using Image Processing and Fuzzy Logic for Enhanced Safety
Authors:-Mrs. G.Tejasri Devi, P.H. Naga Datta Sanjeev, A.Kasi Viswanadh, A.Sankar, P.Veera Mahesh, Y.Lakshmi Chakradhar

Abstract-The advancement of railway transportation vehicles significantly affects the transportation network. Various errors occur due to the utilization of train lines, arising from both manufacturing defects and improper rail usage. Early detection and correction of these faults are crucial, and several techniques have been developed to address this issue. One effective method involves the use of camera-based systems. By employing cameras mounted on railway vehicles, images of rail components are captured and analysed to identify potential defects. This paper proposes a method for detecting and classifying defects on rail track surfaces using image processing techniques. The system relies on high-resolution images obtained from specialized cameras installed on railway inspection vehicles. These images are analysed to identify and assess various track anomalies, including cracks, welding defects, track misalignment, and ballast deterioration. The image processing workflow involves pre-processing, feature extraction, and segmentation to isolate the rail area and detect potential faults. To prioritize maintenance activities, fuzzy logic is applied after identifying and evaluating the severity of defects. This approach is particularly effective in handling the uncertainty and imprecision associated with track condition assessments. Fuzzy rules and membership functions are designed to assign severity levels to the extracted features of each defect category. This method offers a comprehensive and adaptable solution for improving railway track maintenance and ensuring operational safety.

DOI: 10.61137/ijsret.vol.11.issue2.300

Android Flight Price Prediction Web-Based Platform: Leveraging Generating AI for Real-Time Airfare Forecasting
Authors:-Mrs. M. Mani Deepika, P. Nasivi Ramya Anjani, V. Sai Jyothika Chowdary, Y. Anitha Chowdary, M. Swarna, K.Vamsika

Abstract-The aviation industry faces significant challenges in accurately and swiftly predicting flight fares due to the sector’s dynamic nature. Factors such as fluctuating demand, fuel prices, and route complexities contribute to this unpredictability. To address these issues, this research introduces a novel approach leveraging generative artificial intelligence (GAI) to forecast airfares in real time with high precision. The proposed framework integrates generative models, deep learning architectures, and historical pricing data to enhance predictive accuracy. Utilizing GAI within an advanced web engineering framework, this method effectively captures intricate patterns and relationships within historical airline data. By employing deep neural networks, the model efficiently processes diverse scenarios, extracting critical insights to improve the understanding of key factors influencing flight costs. Furthermore, the approach prioritizes real-time forecasting, enabling rapid adaptation to market fluctuations and providing valuable insights for dynamic pricing strategies.

DOI: 10.61137/ijsret.vol.11.issue2.301

AI-Driven Global Solar Radiation Prediction: Harnessing Machine Learning and Satellite Imagery for Accurate Forecasting
Authors:-Mrs.A.Srujana Jyothi, M.Siri Sathvika, M.Madhur, I.Chathurya, G.Ram Subhash, K.V.K.Varma

Abstract-Accurate prediction of Daily Global Solar Radiation (DGSR) is crucial for applications in renewable energy, agriculture, and climate studies. This paper explores the effectiveness of Machine Learning (ML) algorithms and satellite imagery in enhancing DGSR prediction accuracy. Traditional ML models typically rely on various meteorological parameters (e.g., temperature, wind speed, atmospheric pressure, and sunshine duration) and radiometric parameters (e.g., aerosol optical thickness, water vapour). In this study, we investigate the impact of incorporating normalized reflectance from satellite images across different spectral channels to improve prediction accuracy. We employ two ML-based regression models: Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results indicate that the selection of input parameters significantly affects the accuracy of daily solar radiation forecasts. Moreover, the ANN model outperforms SVM, demonstrating superior predictive capability.

DOI: 10.61137/ijsret.vol.11.issue2.302

AI-Powered Mental Health Insights: A Comprehensive Review of Machine Learning & Deep Learning Approaches for Social Media Analysis
Authors:-Mrs.R.Veera Meenakshi, B.Vanitha Sri, V.N.V.Karthikeya, P.G.Pranava, K.Uday Meher, A.Sreeja

Abstract-Artificial intelligence is revolutionizing healthcare, particularly in the prediction and diagnosis of various diseases through machine learning (ML) and deep learning (DL) algorithms. With the widespread use of social media platforms like Twitter, Facebook, and Reddit, individuals frequently express their thoughts and emotions online. Mental health has emerged as a significant concern, especially following the COVID-19 pandemic, prompting researchers to leverage ML and DL techniques to analyse social media data for mental health prediction. This study offers a comprehensive review of ML and DL algorithms applied to the prediction of mental disorders, based on an analysis of 37 selected research papers. It presents a comparative accuracy table of ML and DL models for four key mental disorders: Depression, Anxiety, Bipolar Disorder, and ADHD. The findings aim to provide a foundational reference for researchers and practitioners, assisting in future advancements in this field. Additionally, this study compiles a list of publicly available datasets, serving as a valuable resource for further research in mental health analysis using artificial intelligence.

DOI: 10.61137/ijsret.vol.11.issue2.303

Urban Flood Hazard Assessment: Harnessing Ensemble Machine Learning for Next-Generation Risk Analytics
Authors:-Mrs.T.Sankaramma, Ch.Mahesh, M.Venkata Sai Harshith, Shaik Saad, V.Venkata Sai Sanjay, Shaik Mohammad Ashiq Ilahi

Abstract-Urban flood hazard assessment through an ensemble machine learning approach minimizes the bias of individual models and offers a more detailed insight into the evolution of flood risks over time. By integrating diverse models, this approach increases the precision of flood event predictions. In this research, we utilized an ensemble machine learning framework to analyse flood hazards. The results reveal that the ensemble model outperforms conventional methods, such as the classification and regression tree (CART) and random forest (RF). The generated hazard maps confirm the accuracy of the data, facilitating public awareness and pinpointing regions vulnerable to flooding.

DOI: 10.61137/ijsret.vol.11.issue2.304

Big Data Analytics for Real-Time Fraud Detection in Insurance Claims
Authors:-Shaba Khatoon , Asst.Prof. Ankita Srivastava, Prof.Shish Ahmad

Abstract-The integration of Artificial Intelligence (AI) and Big Data Analytics is revolutionizing industries by optimizing efficiency, accuracy, and security. In healthcare and insurance, AIdriven Intelligent Document Processing (IDP) automates workflows such as claims automation, medical data extraction, and regulatory compliance management. By utilizing Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR), IDP accelerates document classification, data validation, and anomaly detection, reducing errors by 90% and cutting processing time by 80%. In the financial sector, AI enhances fraud analytics, risk modeling, and compliance monitoring. Advanced deep learning architectures, pattern recognition, and predictive analytics improve credit risk assessment and real-time fraud mitigation. AI-powered anomaly detection techniques identify suspicious transactions, reducing cybersecurity threats and financial fraud losses.

DOI: 10.61137/ijsret.vol.11.issue2.305

Air Pollution Detection Using MQ135 and ESP3266
Authors:-Assistant Professor Mrs. Anuja S. Phapale, Om Mahajan, Vedant Mahanavar, Kiran Mangde, Pranav Patil

Abstract-Air pollution is a serious environmental and health problem worsened by an increase in industrialization and urban sprawl. Real-time air quality monitoring is critical for understanding access to pollution and minimizing risks. This paper presents a low-priced portable air quality monitoring and measuring system based on MQ135 gas sensor and ESP8266 microcontroller. The system is made to detect key pollutants, such as carbon monoxide, carbon monoxide (CO), carbon dioxide (CO2), ammonia (NH3), benzene, and other volatile organic compounds (VOCs). The readings, which will ensure accurate readings from the developed system at any point in time in the future The proposed developed system has many features, including low power consumption, wireless communication, and real- time data acquisition, which make it suitable for mobile and remote applications.. MQ135 ensures accurate detection of pollutants and the sensor is linked with the ESP8266-12E module, which transfers data to the cloud for further processing. A specialized software component parses the collected data to interpret air quality trends for the users and estimate their health impact. This low-cost and easy- to-use system individual and community- level air quality monitoring. Its portability and real-time data provide another useful tool for researchers, environmental agencies, and policymakers. The experimental results demonstrate that the system successfully covers the fluctuations in pollution. Future improvements could include the incorporation of artificial intelligence methods for enhanced prediction and a wider air quality analysis spectrum.

DOI: 10.61137/ijsret.vol.11.issue2.306

Rainfall Prediction Using Machine Learning Techniques: A Comprehensive Study
Authors:-Savita Prabha, Ayush Kashyap, Mohit Kumar Pandit, Muskan kumari, Rishi kumar Mishra

Abstract-Thus, dynamic rainfall prediction is an important activity across different fields like meteorology, agriculture, and disaster management, etc. Efficient forecasting helps in damage control against severe weather and thereby helps in water resource management optimization as well as agricultural productivity enhancement. Recent advancements in machine learning have brought a drastic enhancement in the reliability of rainfall predictions using huge datasets of weather information [1]. This study examines multiple machine learning algorithms to analyze rainfall prediction based on key meteorological parameters. Statistical techniques were employed to filter significant climate variables influencing rainfall patterns. These correlations were visualized using heat maps and other graphical tools, demonstrating the impact of factors such as temperature, humidity, atmospheric pressure, and wind speed on rainfall trends [2]. To enhance forecast accuracy, this research integrates machine learning models that improve weather-dependent system predictability. The findings could assist in better decision-making for weather risk management, providing valuable insights for meteorologists, farmers, policymakers, and disaster management authorities [3].

Seismic Analysis and Design of Multi-Storey Building With and Without Shear Wall G+15 Using Staad.Pro
Authors:-B Sri Kalyan, A Ganga Nagini, M Leela Archana, P Prathyusha, K V V Harsha Vardhan, M Durga Prasad, P T V S Varma

Abstract-A multi storey building is a building that consists has multiple floors above ground in the building. Multi-storey buildings aim to increase the floor area of the building without increasing the area of the land and saving money. Analysis of multi-storey building frames involves lot of complications and edacious calculations by conventional methods. To carry out such analysis is a time-consuming task. Substitute frame method for analysis can be handy in approximate and quick analysis instead of bidding process. Till date, this method has been applied by designers for vertical loading conditions. The represented plan given to office purposes can accommodate with minimum facilities. Generally, buildings may be failed by bending moments, shear forces acting on members of the building. By keeping these failures in mind, we designed beams, columns, footings by considering maximum loads on members. For loads calculation, substitute frame method is used for reducing the complexity of calculations and saving time. We know R.C structural system is most common nowadays in urban regions with multi-bay and multi-storey’s, keeping its importance in urban regions especially, A building frame consists of number of bays and storey. A multi-storey, multi- paneled frame is a complicated statically intermediate structure. A design of R.C building With and without shear wall of G+15 storey frame work is taken up. The building in plan (30m x 20m) consists of columns built monolithically forming a network. The design is made using software on structural analysis design (STAAD-PRO). The building subjected to both the vertical loads as well as horizontal loads. The vertical load consists of dead load of structural components such as beams, columns, slabs etc and live loads. The horizontal load consists of the wind forces thus building is designed for dead load, live load and wind load and seismic loads as per IS 875. The building is designed as two-dimensional vertical frame and analyzed for the maximum and minimum bending moments and shear forces by trial-and-error methods as per IS 456-2000. The help is taken by software available in institute and the computations of loads, moments and shear forces and obtained from this software.

DOI: 10.61137/ijsret.vol.11.issue2.307

Earthquake Performance of Multistorey Building by Using Sap2000
Authors:-B. Sri Datta Subramanayam, P. Jagadeeswar Reddy, D. Vinay, K. Shameela Keerthi, G. John Mukesh, N. Mani Kumar, V. Durga Vinod Kumar

Abstract-Earthquake in the simplest terms can be defined as Shaking and vibration at the surface of the earth resulting from underground movement along a fault plane. The vibrations produced by the earthquakes are due to seismic waves. Among different types of dampers, viscoelastic [VE] dampers are used for this numerical study. Viscoelastic dampers are considered to be better than most of the passive energy dissipation devices. Researches done on the improvement of its performance for analyzing structures have always been in vogue. The significant change in the response of the structures to make it resistant to earthquake and wind forces is the main idea behind using such devices. In this numerical study, seismic response of G+4 structures will analyze having with and without damper. The damper will be considered in the place of critical sections. Modeling and analysis of the structures and installation of the dampers are done by using finite element modeling software [SAP2000]. Time history analysis was used to simulate the response of the structures. The structure is design in accordance with seismic code is 1893: 2000 under seismic zone III with the help of SAP 2000. Damper systems are designed to prevent injuries to the residents by absorbing the input seismic energy and reduce the deformations in the structure.

DOI: 10.61137/ijsret.vol.11.issue2.308

Design and Structural Behaviour of Modern Structure by Using ETABS
Authors:-B. Sri Datta Subramanyam1, S.S.S.Narendra2, N. Lakshman3, D.V. Venkata Sai4, G. Gnana Das5

Abstract-ETABS stands for extended three-dimensional analysis of building systems. The main purpose of this software is to design multi-storeyed building in a systematic process. The effective design and construction of an earthquake resistant structure have great importance all over the world. This project presents multi-storied residential building analysed and designed with lateral loading effect of earthquake using ETABS. This project is designed as per IS 1893-PART 2:2002, IS 456-2000. Every structural engineer should design a building with most efficient planning and also be economical. They should ensure that is serviceable, habitable in healthy environmental for its occupants and have longer design period. Structurally robust and aesthetically pleasing building are beginning constructed by combining the best properties of any construction material and at the same time meeting a specific requirement like type of building and its loads, soil condition, time, flexibility and economy. The high-rise buildings are best suited solution. This Project discusses the analysis of a multistoried building depending up on the area prepare a plan based on the requirements. The plan area is 3500sqft of 15m height i,e G+4. And each floor consisting of 2 flats. Each flat with 3bhk software used to draw the plan is AutoCAD 2019.We have analysis and design of multistoried building using ETABS we have applied all the loads and its combination to the structure and it is safe.

DOI: 10.61137/ijsret.vol.11.issue2.309

Performance of Seismic Analysis of Rc Structure High Rise G+13 Multi-Storied Building by ETABS
Authors:-B. Sri Kalyan, B. Harish, M. S. Prasanth, K. S. S. A. N. Kishore Kumar, B. Sridhar, J. Teja

Abstract-Earthquake is a natural calamity that has taken toll of millions of lives throughout the ages. The earthquake ranks as one of the destructive events recorded so far in India in terms of death and damage to infrastructure. Due to the present environmental condition and behavior of tectonic plates, it has become utmost necessary for civil engineers to consider the effects of the earthquake during the designing of the building. Also, most parts of India are under the earthquake-prone zone, so it has become necessary to consider earthquake load while designing a structure to minimize the effects of the earthquake. In this project work, a (G+13) storey high rise building is analysed in seismic zone-V by both equivalent lateral force method and response spectrum method. After analysis, various response parameters like storey shear, storey displacement, storey drift, etc. are studied and the results are also compared. The analysis of building under construction is also done and the results of base shear at different storey level of construction phase are also compared.

DOI: 10.61137/ijsret.vol.11.issue2.310

Seismic Analysis and Design of Multistorey Residential Building by Using ETABS

Authors:-V Tanuja, D Anusha, Y Devi Pravallika, P Mohan Vinay, B John Bhaskar

Abstract-One of the most frightening and destructive phenomena of a nature is a severe earth quake and it’s terrible after effects. Earthquake strike suddenly, violently and without warning at any time of the day or night. If an earthquake occurs in a populated area, it may cause many deaths and injuries and extensive property damage. Although there are no guarantees of safety during an earthquake, identifying potential hazards ahead of time and advance planning to save lives and significantly reduce injuries and property damage. Hence it is mandatory to do the seismic analysis and design to structural against collapse. It is highly impossible to prevent an earthquake from occurring, but the damage to the buildings can be controlled through proper design and detailing. Designing a structure in such a way that reducing damage during an earthquake makes the structure quite uneconomical, as the earth quake might or might not occur in its life time and is a rare phenomenon. In order to compete in the ever-growing competent market it is very important for a structural engineer to save time. As a sequel to this an attempt is made to analyze and design a multistoried building by using a software package E-Tabs. For analyzing a multi storied building one has to consider all the possible loadings and see that the structure is safe against all possible loading conditions. There are several methods for analysis of different frames like kani’s method, cantilever method, portal method, and Matrix method. The present project deals with the seismic analysis and design of a multi storyed residential building of G+10 RCC Residential Building. The dead load &live loads are applied and the design for beams, columns, footing is obtained-Tabs with its new features surpassed its predecessors, and compotators with its data sharing capabilities with other major software like AutoCAD, and MS Excel. We conclude that E- Tabs is a very powerful tool which can save much time and is very accurate in Designs. Thus it is concluded that E-Tabs package is suitable for the design of a multistoried building.

DOI: 10.61137/ijsret.vol.11.issue2.311

Design and Analysis of Dormitory Building Structure by Using Structural Analysis Programme Software
Authors:-L. Praveen kumar, R. Sai, N. Preethi, A. J. Siva Prakesh, L. Lokesh

Abstract-In general, the building is designed as per codal provisions, which has various constraints while analyzing with dynamic loads. This analysis procedure takes a lot of time and is complex. Therefore, most of the Civil Engineering structures are designed taking the assumption of applied loading to be static. The process of neglecting the dynamic forces may lead to the collapse of the structure as a whole in case of a catastrophe such as an earthquake. Some recent earthquakes have shown the need for dynamic analysis. Nowadays, a lot of research is going on the field of performance-based design such that the structure can withstand earthquake-induced loads. This study confers the need to shift the design practice from force based to performance based for getting actual response. Three different analysis have been performed using empirical formulae and numerical modelling software to estimate the natural period of oscillation of building and the parameters are discussed on which it depends. Research and development in the field of earthquake resistant design has put emphasis on non-linear analysis methods to estimate seismic demands. Nonlinear time history and nonlinear static pushover analysis are the main methods. In this study, pushover analysis is carried out on multi-story reinforced residential concrete building in India.

DOI: 10.61137/ijsret.vol.11.issue2.312

Seismic Evaluation of Irregular Buildings Using E-TABS Software
Authors:-B Sri Kalyan, J. Mahalakshmi, G. Anjali, P. Durga prasad, M. Ashish

Abstract-Since the existence of lace in horizontal space has encouraged people to soar higher, there has been a consistent trend towards the creation of taller structures. However, pushing the vertical limit further increases the hazard factor. We must assess the building for a range of loads to ensure safety. An examination of a building is required to ascertain its seismic resistance because the behavior of a structure is critical during an earthquake. An earthquake may bring a high-rise structure to the ground, a risk that is difficult to predict. Therefore, it is necessary to do a seismic force assessment on different types of buildings. The seismic officious can use the seismic coefficient technique to analyses small and medium-sized buildings up to a height of forty meters. d of analysis that requires less manual computation. The overall shape, size, and geometry of a structure all have a significant role in defining its behavior. since asymmetry is because asymmetrical structures are more likely to exhibit critical behavior during an earthquake than symmetrical ones. of the characteristics of symmetrical, L-shaped, and T-shaped and build RC structures during earthquakes to better understand the variations in seismic loading and behavior that may arise due to form variances. The seismic ETABS software aids in the seismic coefficient study of a ten-story building with three-meter-tall levels and a varied plan shape, including symmetrical, L-shaped, and T-shaped configurations. al inspection of a ten-story structure takes a long time and increases the chance of errors. ETABS can simplify, improve efficiency, and increase the accuracy of a structure’s analysis. The analysis is conducting the analysis by adhering to IS 1893:2002 (Part 1). shaped RC structures have their own response, which includes, among other things, lateral pressures, base shear, storey drift, and storey shear. We use the reaction of the variously shaped buildings to compare the findings.

DOI: 10.61137/ijsret.vol.11.issue2.313

Classical Physics, Quantum Mechanics, and More
Authors:-Abhinav Tr

Abstract-To sum up current theories to come up with a universal understanding of the universe.

DOI: 10.61137/ijsret.vol.11.issue2.314

Implement YOLO(You Only Look Once)to Detect Objects in Image
Authors:-Dr. M.V. Subba Reddy, Yendluri.Suchitra, Shaike. Humera Bhanu, Thati. Ganga Maheswari, Pasam.Venkatesh

Abstract-Object recognition plays a crucial role in various real-world applications, including surveillance, autonomous vehicles, and robotics. This paper presents an efficient object detection and recognition system using the You Only Look Once (YOLO) deep learning model. YOLO is a state-of-the-art, real-time object detection framework that processes an entire image in a single forward pass, making it significantly faster than traditional region-based approaches. The proposed system is implemented using YOLOv5, leveraging a Convolutional Neural Network (CNN) for feature extraction and classification. The model is trained on large-scale datasets, such as COCO, to recognize multiple objects with high accuracy. We evaluate the system’s performance based on mean Average Precision (mAP), inference speed, and real-time detection capabilities. Experimental results demonstrate that YOLO achieves robust object recognition with minimal latency, making it suitable for real-time applications.

DOI: 10.61137/ijsret.vol.11.issue2.315

A Hybrid AI Framework for Climate Change Prediction and Mitigation Using Deep Learning and Reinforcement Learning

Authors:-Yashraj Misal, Omkar Ovhal, Aqdas Mirza, Mayuresh More4, Mrs. Anuja S. Phapale

Abstract-This paper proposes a hybrid artificial intelligence (AI) modular system with reinforcement learning and deep learning to update climate change forecasting and mitigation planning. The system learns from multimodal data in satellite imagery, IoT sensors, and historical data to make accurate, real- time forecasts of key climate variables. Convolutional neural networks (CNN) and long-short-term memory (LSTM) models are employed to learn spatial and temporal patterns, and a Re- inforcement Learning (RL) module provides adaptive mitigation recommendations. Prediction accuracy and emission reduction are enhanced compared to traditional models, as shown in our experimental results. This paper contributes significantly to scalable and intelligent solutions to climate change issues.

DOI: 10.61137/ijsret.vol.11.issue2.316

A Comprehensive Review of Large Language Models for Code Generation: Challenges and Future Directions

Authors:-Madhav Vyas, Dhruvi Dave, Khushaliba Gohil, Professor Mansi Gosai

Abstract-Large Language Models (LLMs) have significantly transformed the field of code generation by automating programming tasks, improving developer productivity, and enabling rapid prototyping. This review explores recent advancements in LLM-based code generation, examining both proprietary (closed-source) and open-source models. Proprietary models, such as GitHub Copilot, OpenAI Codex, and Amazon CodeWhisperer, offer high accuracy and seamless integration with development environments but limit user control. In contrast, open-source models like Code Llama, StarCoder, and PolyCoder provide transparency, customization, and self-hosting capabilities. Despite their progress, LLM-generated code faces challenges, including incorrect outputs, inefficiency, security risks, and difficulty in real-world software development. Benchmark datasets like HumanEval, MBPP, APPS, and CodeXGLUE have been developed to evaluate model performance based on correctness, efficiency, and robustness. Recent studies propose new methodologies, such as reinforcement learning and self-checking systems, to enhance accuracy and usability. Future research should focus on improving evaluation methods, contextual understanding, and security measures to ensure reliable and efficient LLM-generated code.

DOI: 10.61137/ijsret.vol.11.issue2.317

Prediction of Heart Disease Using Data Mining
Authors:- Kale Vivek Raju, Guide Prof. Shripad Bhide

Abstract- Heart disease has emerged as the foremost cause of mortality worldwide, impacting individuals of all ages and genders. Timely prediction of heart disease is imperative for initiating interventions and enhancing patient outcomes. This paper delves into the realm of data mining techniques, particularly focusing on supervised learning methods, to construct prediction models for heart disease survivability. By conducting a thorough examination of existing research and technological advancements in this domain, the study aims to furnish a comprehensive review and comparison of diverse supervised learning models. The comparison entails an assessment of the accuracy levels of these models as documented by various researchers. Furthermore, the paper extends its analysis to explore additional factors influencing the performance of supervised learning techniques in heart disease prediction, such as feature selection methods, model evaluation metrics, and data preprocessing techniques. The insights gleaned from this study not only shed light on the efficacy of supervised learning methods in forecasting heart disease but also pinpoint avenues for further research and development, including the exploration of ensemble methods, deep learning architectures, and the integration of multimodal data sources for enhanced predictive accuracy.

A Comprehensive Review of Large Language Models for Code Generation: Challenges and Future Directions
Authors:-Madhav Vyas, Dhruvi Dave, Khushaliba Gohil, Professor Mansi Gosai

DOI: 10.61137/ijsret.vol.11.issue2.318

Malware Detection Using Machine Learning
Authors:-Abhishek Dadhich, Viraj Shinde, Rashmit Pawar, Umesh Mohite

Abstract-The increasing sophistication of malware threats necessitates advanced detection techniques beyond traditional signature-based methods. This paper presents a machine learning–based approach for malware detection using the Random Forest classifier. Static analysis features are extracted from executable files, and feature selection is performed using the Extra Trees Classifier. The proposed system achieves high accuracy in distinguishing between legitimate and malicious files, demonstrating the potential of machine learning in cybersecurity. Furthermore, we discuss the advantages of using machine learning for malware detection, the limitations of static analysis, and the potential of hybrid approaches that incorporate dynamic analysis. Additionally, we evaluate the computational cost, scalability, and efficiency of the proposed method to ensure real-world applicability. The increasing sophistication of malware threats necessitates advanced detection techniques beyond traditional signature-based methods. Machine learning- based detection offers a promising approach by leveraging data-driven classification techniques. This paper explores the implementation of a Random Forest classifier for static analysis-based malware detection. We discuss feature extraction techniques, dataset preprocessing, and the benefits of automated detection systems. Additionally, we highlight the importance of hybrid approaches that integrate dynamic analysis to enhance accuracy and resilience against obfuscation techniques. Our results demonstrate significant improvements over traditional heuristic-based detection methods, emphasizing the potential of intelligent cybersecurity solutions.

Real-Time Imaging and Rapid Response System Using Autonomous Drones for Enhanced Military Operations
Authors:-Mahaan N Bhat, Zeeshaan, Sidharth S Shaikh, Aishwarya M Bhat

Abstract-The development of autonomous systems has revolutionized military operations, enabling enhanced battlefield support. This paper presents an autonomous Unmanned Aerial Vehicle (UAV) system designed to provide real-time imaging, rapid response, and intruder detection for military applications. The UAV performs critical functions such as delivering first aid to injured soldiers, conducting reconnaissance missions, and autonomously detecting intruders. Key technologies utilized include AI-driven decision-making, autonomous navigation, advanced imaging systems, and Remote Desktop Protocol (RDP) for remote control. This innovative approach aims to improve situational awareness, increase operational efficiency, and strengthen the overall effectiveness of military operations. The results showcase significant improvements in reconnaissance speed, response accuracy, and security measures [1], [2].

Automated Student Attendance Using Computer Vision
Authors:-Reshi, Merinkanth, Yogeswari, Gayathri, Dr.P.Sachidhanandam

Abstract-The implementation of an Automated Student Attendance System utilizing Computer Vision optimizes attendance management by leveraging facial recognition technology to automate the marking process. This approach minimizes manual intervention while enhancing accuracy and efficiency. A web-based interface facilitates seamless attendance tracking, record maintenance, and real-time monitoring. Furthermore, the system incorporates email notifications to provide timely updates and allows direct downloads of attendance logs for administrative convenience. Security measures are reinforced through the identification and image capture of unauthorized individuals, with automated email alerts dispatched to administrators for enhanced surveillance. By integrating artificial intelligence, this system ensures a robust, reliable, and autonomous attendance tracking solution, significantly improving record-keeping efficiency within educational institutions and organizations.

DOI: 10.61137/ijsret.vol.11.issue2.350

Prep Gate: Advanced GATE Exam Preparation Platform using android studio & Java
Authors:-Aman Patre, Abhishek Bawankar, Mohit Selokar, Pallavi Pandey, Professor Saroj A. Shambharkar

Abstract-Prep Gate is a comprehensive mobile application designed to aid students in preparing for the Graduate Aptitude Test in Engineering (GATE), an essential examination for aspiring engineers in India. This Android-based platform offers a wide array of tools and resources that cater to the diverse needs of GATE aspirants, providing them with easy access to study materials, practice tests, progress tracking, and more, all in a user-friendly environment. Developed using Android Studio, Prep Gate incorporates interactive features such as quizzes, mock tests, and time-based assessments, allowing users to simulate real-time exam scenarios and evaluate their performance. The app also provides essential study resources, including subject-wise notes, video tutorials, and reference materials, which can be accessed at any time, facilitating flexible learning. To enhance user engagement, Prep Gate leverages Firebase Authentication for secure user login and personalized experience, allowing users to track their progress over time, set study goals, and receive timely reminders and notifications. The platform also includes a leaderboard feature, fostering a sense of competition and motivation among users.

DOI: 10.61137/ijsret.vol.11.issue2.319

Optimization of Print Speed for FDM Process Using Minitab
Authors:-Mrs. B. Siva Naga Ramya, Kommukuri Praveen, Bandarlanka Satya Sai, Peddapati Sai Naredra, Boddapati Madhusudhan

Abstract-The Fused Deposition Modeling (FDM) process is widely used in additive manufacturing due to its cost-effectiveness and design flexibility. However, optimizing the print speed without compromising material consumption and surface quality remains a critical challenge. This study focuses on optimizing print speed, infill pattern, and wall count using the Design of Experiments (DOE) methodology in Minitab to achieve an optimal balance between printing time, material usage, and surface finish. The research evaluates the impact of these parameters on printing efficiency through a structured Taguchi experimental design approach, allowing for the identification of parameter settings that minimize print time while ensuring adequate part strength and surface smoothness. Various infill patterns (rectilinear, honeycomb, gyroid, and concentric), wall counts (single, double, and multiple shells), and print speeds were tested to determine their influence on material deposition rate, layer bonding, and overall print quality.

DOI: 10.61137/ijsret.vol.11.issue2.320

Multi Objective Optimization of Print Settings For Nominal Print Time Using Frontier Analyse Method
Authors:-Mrs. S. Hemani, Vasamsetti Mallika, Ayithireddy Bhavani Raja, Annamdevula Naga Venkata Ramana, Pujari Rupesh

Abstract-In Fused Deposition Modeling (FDM), optimizing print settings is crucial to balance print time, material consumption, and part quality. Achieving an optimal combination of parameters ensures efficient production without compromising mechanical integrity. This study employs the Frontier Analysis Method for multi-objective optimization of infill pattern, wall count, and print speed to achieve a nominal print time while minimizing material consumption and maintaining print quality .The study investigates how different infill patterns (grid, gyroid, honeycomb, and line), wall counts (single, double, and multiple), and print speeds affect the total print time, material usage, and surface finish. The Frontier Analysis Method, a data-driven optimization approach, is implemented to determine the most efficient print settings that provide the best trade-off among speed, strength, and material efficiency. The results indicate that higher print speeds reduce print time but may lead to defects such as layer misalignment and poor adhesion. Increasing wall count improves strength but leads to higher material consumption and longer print times. Similarly, infill pattern selection significantly impacts part strength and material usage, with honeycomb and gyroid infills showing better strength-to-material ratios compared to grid-based structures. Through multi-objective optimization, the study identifies optimal print settings that reduce excess material use and printing time while maintaining dimensional accuracy and mechanical properties. The findings help improve FDM printing efficiency, providing a systematic approach for selecting ideal print parameters based on specific manufacturing needs.

DOI: 10.61137/ijsret.vol.11.issue2.321

Implementation of Text Encryption and Decryption Using Caesar Cipher in Python
Authors:-Veerkant Bhaskar

Abstract-This essay investigates the use of Python’s Caesar Cipher algorithm for text encryption and decryption. One of the oldest and most basic encryption methods is the Caesar Cipher, which involves moving characters in the textto a predetermined number of places. It functions as an introduction cryptographic technique for instructional purposes even though it is unsuitable for contemporary secure communication. The theoretical underpinnings, Python implementation, and efficacy assessment for simple encryption tasks are described in this article.

Osteoarthritis Severity Level Detection Using X-ray Images
Authors:-Mukesh G, Mohit Chandran, Karthick P, Shreeman MM

Abstract-Osteoarthritis (OA) is a chronic degenerative joint disease affecting millions worldwide, leading to joint pain, stiffness, and limited mobility. Early diagnosis is critical for effective treatment and disease management. Traditional OA diagnosis relies on radiologists interpreting X-ray images, which is a time-consuming and subjective process. The integration of artificial intelligence (AI) in medical imaging offers a promising solution to automate and enhance the diagnostic process. In this study, we propose an AI-driven approach for osteoarthritis severity level detection using X-ray images. We utilize a fine-tuned Efficient Net model, trained on the Kaggle Osteoarthritis dataset. The model employs convolutional neural networks (CNNs) with dropout layers to improve generalization and prevent overfitting. Preprocessing techniques such as normalization, augmentation, and resizing enhance the robustness of the model. The AI model is optimized using the Adam optimizer and evaluated using binary cross-entropy loss. Experimental results demonstrate that our approach achieves high classification accuracy, outperforming traditional methods. Performance metrics such as precision, recall, and F1-score validate the effectiveness of the model. By automating osteoarthritis severity detection, this research aims to provide healthcare professionals with an objective, efficient, and scalable diagnostic tool.

Design and Fluent Simulation of Draft Tube to Increase Exist Pressure
Authors:-Mr. Ch. Sai Mohan Reddy, Punnam Yesu, Yedla Tejaswi, Adabala Srirama Surya Prakash, Mangam Ajay Kumar

Abstract-The draft tube is a critical component of hydropower plants, especially for reaction and mixed-flow turbines, as it plays a vital role in ensuring efficient energy conversion and system stability. Its design significantly impacts the overall performance of the turbine by reducing velocity at the outlet, converting kinetic energy into pressure energy, and minimizing energy losses. However, the design of an efficient draft tube comes with numerous challenges, including addressing problems such as cavitation, backflow, surging, swirl flow, and erosion of metal components due to high-velocity water flow. A well-designed draft tube should effectively mitigate these issues while maintaining optimal performance. The primary objective of this project was to design a draft tube and analyze its performance under real working conditions using advanced computational tools. The draft tube design was created using a CAD software tool, providing an accurate and detailed 3D model for further analysis. Simulation of the design was then performed using ANSYS software, with Computational Fluid Dynamics (CFD) analysis carried out in ANSYS Fluent. The CFD analysis included defining key parameters such as inlet velocity, flow patterns, outlet pressure, velocity distribution, and turbulence behaviour to replicate realistic operating conditions.

DOI: 10.61137/ijsret.vol.11.issue2.322

Optimization of Support Patterns to Reduce Material Wastage Using Doe Method
Authors:-Mr. K. Simon Rupas1, Dondapati Vijaya Sukruthi, Chevvakula Sai Vamsi, Appana Mahesh Aditya, Bondapalli Abhiram

Abstract-In Fused Deposition Modeling (FDM), support structures are essential for printing overhanging and complex geometries. However, excessive support material increases printing costs, material wastage, and post-processing time. This study focuses on optimizing support patterns and part orientation using Design of Experiments (DOE) methods to minimize material usage while maintaining structural stability during printing. The study evaluates two commonly used support structures: normal (grid-based) and tree-like supports, along with different part orientations to determine their impact on material consumption and overhang stability. The Taguchi DOE method is implemented to systematically analyze the influence of orientation and support type on key parameters such as printing time, support volume, and ease of removal.

DOI: 10.61137/ijsret.vol.11.issue2.323

Orientation Optimization for Reducing The Support Material Wastage in Material Extrusion Process
Authors:-Mr. M. Rambabu, Akula Hemanth Raja, Bobboli Vijay Durga Rao, Namu Karthik, Bandi Ajay Shankar

Abstract-Additive Manufacturing (AM) using material extrusion processes, such as Fused Deposition Modeling (FDM), often requires support structures to ensure the stability of overhanging or complex geometries during fabrication. However, the use of support material increases material consumption, printing time, and post-processing effort, ultimately raising production costs. This project focuses on optimizing the orientation of parts during the manufacturing process to minimize the use of support material while maintaining the structural integrity and quality of the final product .Fusion 360 was used to simulate the manufacturing process by evaluating various part orientations. By analyzing the overhang angles, build directions, and contact areas requiring supports, optimal orientations were identified. The optimization process considered parameters such as material usage, print time, and surface finish quality. Additionally, simulations were conducted to evaluate the impact of orientation changes on part strength and dimensional accuracy. The study revealed that strategic orientation adjustments could significantly reduce support material wastage by minimizing the number and size of overhangs. For instance, aligning the part’s geometry with the build platform or leveraging self-supporting angles helped achieve material efficiency. The outcomes demonstrated that reducing support material by optimizing orientation not only improves material utilization but also enhances sustainability in AM processes. This research highlights the importance of orientation optimization in material extrusion processes and showcases the potential of simulation tools like Fusion 360 to refine the manufacturing process, enabling cost-effective and resource-efficient production.

DOI: 10.61137/ijsret.vol.11.issue2.324

Enhancing The Discretization Method by Implementing The DOE Method to Optimize The Discretization Value Using Workbench and Ansys
Authors:-Mr. M. Vinil, Meddisetti Ramesh, Polisetti Chaitanya Anil, Bharthala Subrhamanyaswamy, Kantareddy Siva Sai

Abstract-Discretization, commonly known as meshing, plays a pivotal role in finite element analysis (FEA) as it divides a component into smaller elements for numerical simulations. The quality and size of the mesh significantly influence the accuracy, convergence, and computational cost of the simulation. This project focuses on enhancing the discretization process by implementing the Design of Experiments (DOE) method to optimize the mesh size for achieving an optimal balance between computational efficiency and result accuracy. ANSYS was utilized to perform FEA simulations on a selected component with varying mesh sizes to observe their influence on key output parameters such as stress, strain, deformation, and factor of safety. Coarser meshes lead to faster computation but may compromise accuracy, while finer meshes provide more precise results but at a higher computational expense. The DOE method was applied using Minitab software to design a set of systematic experiments, enabling the identification of the most influential factors and their interactions affecting the output.

DOI: 10.61137/ijsret.vol.11.issue2.325

AI-Powered Fraud Detection: Secure Online Transaction Monitoring Using Machine Learning
Authors:-G.Jashwitha, T.Sai Srinath, G.Naga Kastusi, V.Anshitha, G.Janitha Sree, Mrs.G.Tejasri Devi

Abstract-Fraud detection remains one of the most critical challenges in financial transactions, driving on going research and the adoption of advanced technologies such as machine learning. Financial transaction fraud detection aims to explore and compare various machine learning approaches to assess their effectiveness, challenges, and potential future developments comprehensively. This paper begins by highlighting the importance of fraud detection in financial transactions, emphasizing the widespread impact of fraudulent activities on individuals, businesses, and the overall economy. While traditional fraud detection methods have been valuable, they often struggle to counter increasingly sophisticated and evolving fraudulent schemes. As a result, more advanced techniques are required to enhance detection accuracy. Machine learning-based approaches have emerged as a promising solution, enabling algorithms to analyse vast amounts of transactional data and identify patterns indicative of potential fraud. In particular, supervised learning techniques—such as logistic regression, decision trees, and support vector machines—have gained significant popularity in fraud detection due to their ability to classify transactions as legitimate or fraudulent based on historical data.

DOI: 10.61137/ijsret.vol.11.issue2.326

Explainable AI for Event and Anomaly Detection and Classification in Healthcare Monitoring Systems
Authors:-J.Sree Varenya, D.Sahithi, S.Sri Divya, A.Chakri, S.Asritha, Mrs.L.Yamuna

DOI: 10.61137/ijsret.vol.11.issue2.327

Evaluating Machine Learning Efficiency: Simpler Models Outperform Deep Learning in Motor Fault Detection
Authors:-Mrs. L. Yamuna., G. Abhisekhar., K. Prasanna Lahari., K. Vivek., S. Hemanth.

Abstract- In motor condition monitoring, deep learning techniques have been explored by utilizing two-dimensional plots as datasets instead of traditional time-series signals. For instance, Convolutional Neural Networks (CNNs) have been trained using recurrence and frequency-occurrence plots. While previous studies have shown promising results with CNNs, the indistinct differences in these plots often make the model’s decision-making process appear as a black box. This study evaluates and compares ten traditional machine learning (ML) techniques with recent deep learning (DL) approaches for motor fault diagnosis using the same dataset. The dataset consists of 3,750 synthetically generated motor current signal samples, categorized into five classes—one representing healthy conditions and four representing faulty motor conditions—each tested under five loading levels (0%, 25%, 50%, 75%, and 100%). Following similar training and testing phases, the Light Gradient Boosting Machine (LightGBM) achieved the highest classification accuracy of 93.20%, outperforming three CNN-based models by at least 10.4%, whose accuracy ranged between 74.80% and 82.80%. LightGBM also demonstrated superior performance in other key evaluation metrics, including F1 score, precision, and recall. Notably, five out of ten traditional ML models surpassed the CNN-based models. These findings emphasize the importance of carefully selecting deep learning architectures, as they are computationally expensive and memory-intensive, yet do not always guarantee improved performance over traditional ML models, especially for relatively straightforward tasks like motor fault classification using current signals.

DOI: 10.61137/ijsret.vol.11.issue2.328

AI-Driven Time Series Forecasting for Financial Markets: Leveraging Machine Learning for Smarter Predictions
Authors:-S.Likhita, S.Venkata Basavayya, Y.S.Santosh Kumar, P.Bhanu Divyasri, V.Sandeep, Mrs.V.Anantha Lakshmi

Abstract- Financial markets, including stock prices, exchange rates, and commodity prices, are inherently volatile and influenced by numerous factors, making their prediction a challenging yet essential task. Accurate forecasting of market trends is crucial for investors, financial analysts, and policymakers, as it helps in making informed decisions and mitigating risks. In this study, we explore the use of Support Vector Machine (SVM), a powerful machine learning algorithm, for time series forecasting of financial market trends. Traditional forecasting methods often struggle with financial data due to its non-linear and dynamic nature. However, SVM is well-known for its ability to handle high-dimensional data and capture complex patterns, making it a suitable choice for financial market prediction. Our approach leverages historical price and volume data to train the SVM model, enabling it to recognize patterns and predict future market movements. The study evaluates how effectively SVM adapts to changing market conditions, demonstrating its ability to model non-linear relationships within financial time series. Additionally, we consider external economic factors that may influence market behavior, further validating the robustness of the model. The findings highlight the potential of SVM in financial forecasting, offering a reliable alternative to traditional methods. Future work may involve integrating hybrid models combining SVM with deep learning techniques or incorporating macro-economic indicators to further enhance prediction accuracy. This research contributes to the growing field of AI-driven financial analysis, paving the way for more sophisticated and data-driven investment strategies.

DOI: 10.61137/ijsret.vol.11.issue2.329

Knowledge, Attitude and Practices towards Antimicrobial Stewardship among Master of Public Health (Mph) Students in Ahmadu Bello University, Zaria
Authors:-Olalekan Adekunle Muideen, Ejiohuo Precious Tombari, Amodu David Adejo, Kwande Bilikisu, Angara Amina Aliyu, Professor Umar Ibrahim

Abstract- Antimicrobial resistance (AMR) remains a significant global health challenge, with antimicrobial stewardship (AMS) critical to controlling its spread. However, research on AMS practices, particularly within specific regional contexts such as Zaria, Nigeria, is limited. This study assessed the knowledge, attitudes, and practices (KAP) towards AMS among Master of Public Health (MPH) students at Ahmadu Bello University, Zaria, aiming to address these gaps. A cross-sectional, mixed-methods research design was employed, involving a sample of 200 MPH students selected through stratified random sampling. Data collection was conducted using structured questionnaires, with quantitative data analyzed using SPSS version 26 and qualitative data examined through thematic analysis. The results revealed that while 86.5% of respondents were aware of AMS, only 58.0% rated their AMS knowledge as “Good.” In terms of attitudes, a majority (94.0%) supported the restriction of antibiotic prescriptions to necessary cases, though significant gaps persisted in practical adherence to AMS guidelines. For instance, 47.0% reported following national/WHO guidelines “Always” or “Often,” while 45.5% followed them “Sometimes.” Furthermore, 42.0% of respondents acknowledged prescribing antibiotics without laboratory confirmation, highlighting critical gaps between knowledge and practice. The study also identified key barriers, including “Lack of Training” (51.5%) and “Lack of Awareness” (46.0%), which hinder effective AMS implementation. In conclusion, addressing these identified gaps is essential to enhance AMS practices among MPH students and ensure the responsible use of antibiotics. The study recommends targeted training programs, increased resource availability, and strengthened multidisciplinary collaboration to support AMS efforts.

Material Optimization of Bracket For Maximum Stiffness Conditon to Withstand Higher Loading Conditions
Authors:-Mrs. K. Tulasi, Gundu Jaswanth Narayana, Karumanchi Karthik, Anusuri Taraka Sai Surya Venkata Siva, Maddi Chandra Sekhar

Abstract- Topology optimisation has been essential for the design of lightweight mechanical components since their inception in the aerospace industry. Its potential and thorough research yielded computationally viable methods, resulting in its incorporation into numerous computer-aided design applications. Concurrent with the advancement of topology optimisation, additive manufacturing technologies have significantly improved, leading to dependable additive manufacturing processes. The integration of these two technologies enables the fabrication of optimised components more efficiently than conventional production methods. This study offers a concise comparison between topology optimization and other forms of structural optimization. Furthermore, it succinctly contrasts additive production with other production techniques. Solidworks uses the SIMP approach to address topology optimisation issues, and this article presents an overview of this approach, along with additional theoretical considerations of the subject. e primary objective of the research is to illustrate the topology optimization of a jet engine bracket via a SolidWorks simulation. We achieved this by following a structured methodology and meticulously documenting each phase of the optimisation process. We then exported the generated topology in a graphical body format and used it to reconstruct the bracket. The rebuilt bracket is 50% lighter than the original, and it worked well enough in finite element analysis simulations under all the different loading conditions that were given. This article also discusses the collected data, focusing on identifying the sources of mistakes in the research and evaluating their impact on the performance of the optimized bracket. Finally, the research gives a full assessment of possible production methods based on how they affect the finished bracket’s mechanical properties.

DOI: 10.61137/ijsret.vol.11.issue2.330

Addressing IOT Security Challenges through AI Solutions
Authors:-Gopi Parmar, Hasti Makwana, Professor Mansi Gosai

Abstract- The Internet of Things (IoT) is a transformative technology that significantly impacts various sectors, including connectivity, healthcare, work, and the economy. By enabling automation, enhancing efficiency, and reducing operational stress, IoT has the potential to improve daily life in diverse environments, from smart cities to educational institutions. However, the growing prevalence of cyberattacks presents considerable challenges to the security of intelligent IoT applications. Traditional security measures are increasingly insufficient due to evolving threats and vulnerabilities. To address these challenges, future IoT systems must integrate AI-powered machine learning and deep learning techniques, which offer dynamic and adaptive security mechanisms. This review paper explores IoT security intelligence from multiple perspectives, proposing innovative approaches that leverage machine learning and deep learning to analyze raw data, detect attack patterns in unstructured data, and safeguard IoT devices against a broad spectrum of cyber threats.

Bianchi Type-III Cosmological Model with f(R, T) Gravity Based on Lyra Geometry
Authors:-L. S. Ladke, B.V.Bansole, V. P. Tripade

Abstract- This paper is devoted to the study of Bianchi type-III cosmological model with gravity in the presence of perfect fluid based on Lyra geometry. We formalize the gravity equations based on Lyra geometry. To solve the field equations, obtained by considering Bianchi type-III space-time, we used physical condition that the shear scalar σ2 is proportional to scalar expansion . The behavior of the model has been discussed by studying the physical and kinematical properties of the model.

DOI: 10.61137/ijsret.vol.11.issue2.331

Artificial Intelligence in Healthcare
Authors:-Kushal Shah, Jainam Shah, Riya Doshi Prof Mansi Gosai

Abstract- Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment planning, and patient care. Yet, the implementation of AI-based solutions in clinical environments is hindered by a major challenge: the absence of transparency and interpretability. Explainable AI (XAI) has become an essential area focused on making AI decisions more comprehensible, reliable, and accountable. This paper provides an extensive review of XAI in healthcare, emphasizing its significance, methodologies, and applications. XAI methods such as rule-based models, feature importance methods, and interpretable deep learning architecture allow clinicians to understand AI-predicted diagnoses. Such approaches also mitigate fears surrounding bias, fairness, and ethical decision-making in medical AI systems. Involving XAI in the healthcare sector enables us to achieve trust among clinicians, better regulate, and optimize patient safety.

AI-Powered Smart Traffic Management System for Urban Congestion Reduction
Authors:- Aditya Wairagade, Krushna Ugale, Rohit Waghmare

Abstract- Urban traffic congestion causes delays, increased fuel consumption, and environmental pollution. This research proposes an AI-powered smart traffic management system that dynamically adjusts traffic signals based on real-time data, reducing congestion and optimizing vehicle flow. AI techniques, including machine learning and computer vision, analyze live traffic data to predict congestion patterns. IoT sensors and cameras collect real-time traffic data, while machine learning algorithms process this data to dynamically adjust signal dura- tions. This system significantly reduces congestion, improves fuel efficiency, and lowers emissions, while also enhancing road safety and ensuring smoother movement for emergency vehicles. Unlike fixed-timer systems, this AI-driven approach continuously adapts in real-time, making traffic control more efficient.

The Role of Plasma Gasification in Achieving Zero Waste and Circular Economy Goals
Authors:- Sarthak Pandit, Sumit Panda, Shivam Patil

Abstract-Plasma gasification can correct the inefficiencies of the conventional waste management systems such as landfilling and incineration. Greater pollution, greenhouse gas emissions, and depletion of resources will aggravate the global waste crisis further and further. The present study examines the prospects of plasma gasification for converting municipal solid waste into syngas and vitrified slag without or with minimal significant environmental damage. The performance of the process, syngas quality, and utilization of byproducts have been investigated by thermodynamic simulation and pilot-scale tests through opti- mization of certain operational parameters. Plasma gasification achieved a syngas yield of up to 72%, while it significantly reduced toxic emission and thus high operational sustainability, producing inert vitrified slag which can be used for construction. The technology has the potential to foster decentralized waste-to- energy projects and decrease reliance on fossil fuel and landfilling. In this study, the scale-up, economic viability, and environmen- tal benefits of plasma gasification are evaluated, presenting a vision of how plasma gasification can be integrated into current landfilling practice.

A Comprehensive Analysis of Emerging Cyber Threats and Mitigation Strategies in the Digital Era
Authors:- Dr.Vikas Mahandule, Dr. Priti bharambe, Tejas Watekar , Shubham Wadekar, Sanket Roundhal

Abstract- The proliferation of digital technologies has led to a corresponding rise in sophisticated cyber threats, impacting individuals, businesses, and national infrastructure. This study presents a comprehensive review of prominent emerging cyber threats, including ransomware, phishing, IoT vulnerabilities, and advanced persistent threats (APTs). It also proposes mitigation techniques and discusses future implications. By analyzing real-world cases and examining current prevention mechanisms, this research contributes to the understanding of evolving cybersecurity landscapes. The findings emphasize the need for adaptive defense systems, cybersecurity awareness, and global cooperation.

A Hybrid AI Framework for Climate Change Prediction and Mitigation Using Deep Learning and Reinforcement Learning
Authors:- Yashraj Misal, Omkar Ovhal, Aqdas Mirza, Mayuresh More, Mrs. Anuja S. Phaphale

Abstract- This paper proposes a hybrid artificial intelligence (AI) modular system with reinforcement learning and deep learning to update climate change forecasting and mitigation planning. The system learns from multimodal data in satellite imagery, IoT sensors, and historical data to make accurate, real- time forecasts of key climate variables. Convolutional neural networks (CNN) and long-short-term memory (LSTM) models are employed to learn spatial and temporal patterns, and a Re- inforcement Learning (RL) module provides adaptive mitigation recommendations. Prediction accuracy and emission reduction are enhanced compared to traditional models, as shown in our experimental results. This paper contributes significantly to scalable and intelligent solutions to climate change issues.

Application of First Order Linear Ordinary Differential Equations in Mechanics and Thermodynamics
Authors:- Jyotika Sa, Tejaswini Pradhan

Abstract- This comprehensive study explores the profound applications of first-order linear ordinary differential equations (ODEs) in the domains of classical mechanics and thermodynamics. These mathematical tools serve as vital instruments in modeling and analyzing real-world physical phenomena. In particular, this research focuses on Newton’s Second Law of Motion and Newton’s Law of Cooling, both of which are quintessential examples of how first-order linear ODEs can effectively describe dynamic systems. The paper provides an in-depth explanation of the formulation, derivation, and solution of these equations, supported by descriptive illustrations and analytical interpretations. Emphasis is placed on demonstrating the solution techniques such as the integrating factor method, and the separation of variables method, while linking their mathematical elegance to practical engineering, environmental, and forensic applications. The ultimate objective is to illuminate how first-order linear ODEs not only simplify complex physical laws but also enable predictions that are essential in technological and scientific advancements.

DOI: 10.61137/ijsret.vol.11.issue2.332

Beyond Screens: Impact of Artificial Intelligence on Cybersecurity: Opportunities and Challenges
Authors:- Hussain Shaikh

Abstract- In the digital era, Artificial Intelligence (AI) has emerged as a transformative force across various sectors, including cybersecurity. As cyber threats grow in complexity and scale, traditional defense mechanisms are proving insufficient. AI offers a proactive approach to detecting, preventing, and responding to cyber threats in real-time. However, the integration of AI into cybersecurity introduces new challenges, such as adversariattacks, data privacy concerns, and ethical dilemmas. This paper explores the dual role of AI in cybersecurity – its potential to strengthen defenses and the risks it poses. Through a comprehensive literature review, analysis of current applications, and examination of real-world case studies, this research identifies key opportunities, challenges, and future directions. The paper aims to provide a balanced perspective on leveraging AI responsibly and effectively to secure digital infrastructures.

Engineering Scalable Microservices: A Comparative Study of Serverless Vs. Kubernetes-Based Architectures
Authors:- Sreenivasulu Navulipuri

Abstract- This study compares serverless architectures and Kubernetes-based orchestration systems in the context of cloud-native microservices. It examines key factors such as latency, scalability, cost efficiency, and operational complexity. AWS Lambda and other serverless platforms scale automatically but experience delays during cold starts while Kubernetes provides detailed control at the cost of increased operational expenses. The paper also examines AI-driven resource optimization and hybrid models, such as Knative and OpenFaaS, which combine the advantages of both paradigms. Performance benchmarks and case studies guide architects in selecting the most suitable deployment model. A hybrid solution appears to provide the optimal combination of scalability, cost management and operational efficiency.

DOI: 10.61137/ijsret.vol.11.issue2.333

Smart Railway Safety and Track Monitoring System Using ESP32
Authors:- Assistant Professor Ambika Annavarapu, Vanja Chaitanya Aswith, Tadiboina Baji, Sattu Naga Likhith, Purushothapatanam Bhanu Sai Ram

Abstract- Railway transportation is one of the most widely used and efficient means of transport worldwide. However, railway accidents due to track failures pose a significant threat to human lives and infrastructure. Cracks and irregularities in railway tracks can cause derailments, leading to catastrophic accidents. To address this problem, we propose a Smart Railway Safety and Track Monitoring System that detects track anomalies using ultrasonic sensors. And ultrasonic sensors are connected to 180° servo motor. The system is designed to continuously monitor the track condition, and if the distance between the track and the ultrasonic sensor exceeds 5 cm, it Indicates a crack or damage. Upon detection of an anomaly, the system triggers a series of safety measures: it sends an alert to the nearest railway station via the Blynk IoT app, transmits a text message with GPS coordinates using a GSM module, and initiates an automated phone call to the concerned authority. Additionally, signal lights positioned beside the track serve as a visual warning, turning from green to red upon detection of a crack. The entire process is controlled by an ESP32 microcontroller, ensuring real-time monitoring and efficient safety measures.

DOI: 10.61137/ijsret.vol.11.issue2.334

Precision Agriculture with IoT: A Review
Authors:- Ms. Arpita Newaskar, Ms. Anuja Gadade

Abstract- Nowadays, IoT is increasingly found in various sectors and domains, agricultural production is one of these. In agriculture, the internet of things (IoT) is transforming traditional farming practices by improving productivity, efficiency, and sustainability. Precision farming uses smart technology called the Internet of Things (IoT) to make farming better and more eco-friendly. IoT is a global network of things that is physical and virtual devices that have an independent identity each one, which can be connected via a vast network to share information and process. IoT allows farmers to get connected to their farm from anywhere at any time In the agriculture sector, there is a growing demand for the use of technology to improve production, reduce labor, efficiently utilize resources, and decrease the environmental impacts on crops. Several technology solutions have been deployed to address the agriculture-specific technical problems.

A Study on Impact of Digital Marketing in Customer Purchase Behavior Special Reference to Ramanathapuram District
Authors:-R. Asraf Sithika

Abstract- Digital marketing platforms have grown in popularity in recent years as a means for companies to advertise their goods and services. This study intends to investigate the elements—such as perceived value, brand trust, and ad credibility—that affect consumers’ purchase intentions with regard to digital marketing. The sample size is limited to 100 and will be chosen using the convenience sample technique. Both primary and secondary data served as the foundation for this investigation. Primary data will be gathered using the questionnaire method. The secondary data was gathered from a variety of offline and online sources. Marketers can gain a better understanding of how to develop social media campaigns that connect with consumers and eventually influence their purchasing decisions by examining these elements.

Development and Modification of Waste Paper Recycling Machinese
Authors:-Mo Mohseen

Abstract- Paper is used daily with learning institutions such as universities and schools being the main consumers. Due to its single usage it ends up being disposed hence most of the paper waste remains idle and unutilized although it is a valuable resource. Therefore, this paper explores the design of a cheap and efficient manually operated paper recycling machine. The design used integration of acquired knowledge on the recycling technology, existing manually operated and available paper recycling machines to form a cheap but efficient paper recycling machine. The benefits of the machine are not only centered on the merits of recycling paper but by the in-cooperation of the manually driving system which will also curb the high unemployment rates in developing countries. Due to the design being not 100% efficient due to the gear box, belt and chain transmission, the estimated efficiency is equal to 90% but using the 90% for design, the design power input is 450 watts and since an average person can produce 100 Watts constantly therefore 2 people are necessary to drive the machine.

DOI: 10.61137/ijsret.vol.11.issue2.336

Performance Evaluation of a Solar PV–Hydrogen Fuel Cell Hybrid System under Summer Load Conditions
Authors:-Research Scholar Amol Barve, Professor Anurag S D Rai

Abstract- This paper presents a comprehensive performance analysis of a hybrid energy system integrating Solar Photovoltaic (PV) panels and Hydrogen Fuel Cells (HFC) under a summer session load profile. The study aims to evaluate the feasibility, reliability, and efficiency of such a system in meeting varying energy demands during high-temperature conditions, which typically cause fluctuations in solar irradiance and load requirements. The system configuration includes a solar PV array for primary energy generation, an electrolyzer for hydrogen production during excess generation, a hydrogen storage unit, and a fuel cell for supplementary power during periods of low solar availability. Simulation and modeling are carried out using MATLAB/Simulink to assess key performance parameters, including energy efficiency, hydrogen consumption, system autonomy, and environmental impact. Results demonstrate that the hybrid system can effectively balance energy supply and demand, maintain stable output, and reduce dependency on conventional grid power. The integration of hydrogen fuel cells significantly enhances energy storage capabilities, making the system more resilient during extended cloudy periods or nighttime usage. This analysis underscores the potential of solar-hydrogen hybrid systems in supporting sustainable and uninterrupted power supply during summer seasons, particularly in regions with high solar insolation and variable load demands.

Designing Explainable Large Language Models for Critical Decision-Making in Healthcare: A Review-Based XAI Perspective
Authors:-Roshan Nikam, Parv Shah, Anish Shinde, Chaitanyaa Kashid/strong

Abstract- Large Language Models (LLMs) are changing the world of healthcare by simplifying clinical documentation, diagnosing, and making medical decisions. Despite their promise, the black-box-like behavior of LLMs poses severe challenges in a healthcare scenario where trust, interpretability, and accountability are of utmost importance. This paper investigates the LLM interpretability techniques in the medical domain based on the salient insights obtained from an extensive survey of Explainable AI (XAI) literature. It looks into post-hoc explanation techniques (SHAP, LIME), collaborative human-AI decision-making frameworks, and interpretable approaches such as neurosymbolic systems. Highlighted are the main issues: algorithmic bias, hallucinations, healthcare compliance, and algorithmic inefficiencies. It is posited that structured prompting, especially Chain-of-Thought (CoT) reasoning enhanced by diagnostic logic, would ensure that LLM actions, in terms of outputs and explanations, are much more in sync with clinical reasoning, thus enhancing transparency while preserving performance. It is concluded that, drawing on the insights gained from XAI, the more interpretable LLMs promote clinicians’ trust in AI systems’ conduct and consequently promote ethical and effective integration into the healthcare setting. The way forward is to focus on some direction to maintain the balance between model accuracy and interpretability and cater to evolving regulatory requirements.

DOI: 10.61137/ijsret.vol.11.issue2.337

Hand Gesture Recognition in Low-Light Environments Using Deep Learning
Authors:-Sujal Suraywanshi, Ganesh Waje, Diya Jain, Prajyot Jagtap/strong>

Abstract- Hand gesture recognition has attracted enormous interest because of its extensive application in human-computer interaction, sign language understanding, and augmented reality. Recognizing hand gestures in low light conditions is a difficult task to achieve because the visibility is low, there are noises, and feature details get lost. Here, we propose a detailed survey of recent techniques in hand gesture recognition for low-light conditions by employing deep learning methods. We present the difficulties involved with low-light environments, such as illumination changes and background noise. In addition, we discuss different deep learning-based methods for hand detection and gesture classification and present their effectiveness in improving recognition accuracy under difficult lighting conditions. We conclude by offering a comparative assessment of these methods based on primary performance indicators like accuracy, processing time, and resistance to low-light conditions.

DOI: 10.61137/ijsret.vol.11.issue2.338

AI-Driven Business Intelligence and Decision Making: Turning Data into Actionable Insights
Authors:-Aditya Kokate

Abstract- The integration of Artificial Intelligence (AI) into Business Intelligence (BI) is revolutionizing how organizations make decisions by automating data analysis and enhancing predictive capabilities. Traditional BI systems rely on static reporting and retrospective data interpretation, limiting their responsiveness in dynamic environments. In contrast, AI-powered BI systems leverage Machine Learning (ML), Natural Language Processing (NLP), and cognitive computing to transform raw data into actionable insights. This paper explores the architecture, implementation, and impact of AI-driven BI systems, emphasizing real-time analytics, predictive modeling, and explainable AI. The study demonstrates how AI enhances operational efficiency, data accuracy, and strategic foresight, offering a competitive advantage to modern enterprises.

DOI: 10.61137/ijsret.vol.11.issue2.339

AI-Powered Ransomware Defence: Cutting-Edge Machine Learning Techniques for Threat Detection
Authors:-Mrs.P.Satyavathi, M.Naga Sai Ganesh, N.V.Gowtham Kumar,V.S.V.Satya Yaswanth, G.Satya Nandini, V.Giri Sathvika

Abstract- The increasing frequency and sophistication of ransomware attacks, there is a growing need for dynamic and effective detection and mitigation strategies. Traditional signature-based approaches often fall short in identifying new and evolving ransomware variants. This paper explores the application of machine learning techniques for ransomware detection, aiming to enhance the accuracy and adaptability of detection mechanisms. It provides a comprehensive analysis of various machine learning methods and algorithms, evaluating their effectiveness in identifying ransomware patterns. The findings offer valuable insights into the advancement of cybersecurity solutions, emphasizing resilience and proactive defense against the ever-evolving ransomware threat landscape.

DOI: 10.61137/ijsret.vol.11.issue2.339

IoT-Based Smart temperature controlled Fan for Energy-Efficient Cooling
Authors:-Megha Narwade, Diksha Patil, Akanksha Patil, Chetan Aher

Abstract- This paper gives a new and creative IoT-based smart fan system for cooling with energy efficiency in various applications – residential, commercial as well as industrial surroundings. Traditional cooling systems usually depend on static control mechanisms. These cannot adjust to actual environmental conditions and result in extra energy consumption and reduced efficiency too. In order to tackle these challenges, our proposed system integrates an advanced temperature-controlled fan that makes use of a dynamic Pulse Width Modulation (PWM) algorithm that lets us adjust fan speed based on real-time temperature variations around the fan. The system consists of – an NTC thermistor for precise temperature measurement, an Arduino Uno microcontroller for data processing, and an IoT-enabled mobile interface that automates user interactions, and reducing manual adjustments. Strict testing results show that the system successfully achieves a temperature stability of ±0.2°C and reduces power consumption by 27.1% as compared to traditional methods. Also, the modular design supports both DC as well as AC fans, increasing scalability and adaptability. This work bridges the gap between academic research and practical applications and sets a new standard for smart, future focused, thermal management solutions.

DOI: 10.61137/ijsret.vol.11.issue2.340

Click to View MORE PAPERS: Volume 11 Issue 2 March-April 2025

Artificial Intelligence in Everyday Life: Opportunities and Challenges
Authors:-Research Scholar Dhaamunuuri Mahesh, Associate Professor Dr.V.Harsha Shastri

Abstract- Artificial Intelligence (AI) has Power-full integrated into our daily life’s ,influencing industries, homes, and personal experiences. This paper explores the opportunities and challenges associated with AI’s pervasive presence.It examines AI’s impact on various sectors, including healthcare, education, business, and transportation. Additionally, it addresses ethical concerns,privacy issues, and potential risks posed by AI-driven systems. The study aims to provide a balanced perspective on AI’s benefits and limitations.While AI enhances efficiency and decision-making, challenges remain in its responsible implementation. This paper highlights solutions for sustainable AI development, ensuring ethical and secure AI applications.

Multimodal Emotion Classification Using Machine Learning and Deep Learning
Authors:-Professor Mr. V. K. Sabari Rajan, B. Mukesh, C. Narendra, M. Shivanand, B. Ajay Kumar

Abstract- In the rapidly evolving field of artificial intelligence, emotion recognition has emerged as a pivotal area of research, with significant applications in human-computer interaction, mental health analysis, and social robotics. This project focuses on the development of a multimodal emotion recognition system capable of classifying emotions from text, audio, images, and live video. The system employs advanced machine learning algorithms tailored to each modality: BERT for text, CNN and LSTM for audio, and CNN for both images and live video frames. Each modality is designed to recognize a set of core emotions, with slight variations to account for the unique characteristics of each data type. The text module identifies emotions such as anger, fear, joy, love, surprise, and sadness, while the audio, image, and live video modules detect emotions including angry, disgust, fear, happy, neutral, and surprise. The system architecture encompasses dataset creation and preprocessing, model training, and emotion classification. User interaction is facilitated through a web interface, allowing users to input text, audio, images, or live video and receive real-time emotion classification results. This multimodal approach enhances the accuracy and robustness of emotion detection, providing a comprehensive tool for analyzing human emotions across different communication mediums.

DOI: 10.61137/ijsret.vol.11.issue2.341

Mall Customer Segmentation System for Retail Analytics and Personalized Marketing
Authors:-Dr.Prabakaran, S.M.Rafi Saddam, T.Narendra, B.Venkateswarlu, T.Venkatesu

Abstract- This paper presents a comprehensive customer seg- mentation system for retail businesses, specifically designed for shopping mall environments. Using advanced clustering tech- niques and RFM (Recency, Frequency, Monetary) analysis, we develop a robust framework for identifying distinct customer segments with similar purchasing behaviors. The system pro- cesses transactional data to create meaningful customer profiles, enabling businesses to implement targeted marketing strategies and improve customer relationship management. Our approach integrates data preprocessing, feature engineering, clustering algorithms, and interactive visualization to provide actionable insights. The implemented dashboard facilitates segment com- parison, geographical distribution analysis, and automated per- sonalized email campaigns tailored to each segment’s prefer- ences. Experimental results demonstrate the effectiveness of this approach in identifying five key customer segments with distinct behavioral patterns. The system’s practical application is validated through its ability to generate segment-specific market- ing recommendations and predict customer preferences, leading to more efficient resource allocation and potentially increased customer engagement. This research contributes to both the theoretical understanding of customer behavior modeling and provides a practical tool for retail analytics in real-world business environments.

DOI: 10.61137/ijsret.vol.11.issue2.342

AI-Powered Early Disease Prediction Using Machine Learning and Deep Learning Models
Authors:-Aryan Gampawar

Abstract- In today’s world, many people suffer from serious health problems like diabetes, heart disease, cancer, and COVID-
19. Detecting these diseases early is very important because it can help save lives and make treatment more effective. However, traditional diagnosis methods can be slow and may not always catch the early signs. To solve this problem, Artificial Intelligence (AI) is being used in healthcare. This paper discusses how AI techniques like Machine Learning (ML) and Deep Learning (DL) can help detect diseases at an early stage. These models learn from existing medical data, such as patient reports and X-ray images, and then use that knowledge to predict whether a person might have a disease. For example, a Random Forest model was able to predict diabetes with 85 We used real-world datasets and applied models like Logistic Regression, CNN, and LSTM. The models were tested and evaluated using measures like accuracy, precision, recall, and F1-score. We also used tools like SHAP and LIME to explain how the models make their predictions. In conclusion, AI can support doctors in making faster and more accurate decisions. Although there are challenges such as data privacy and understanding how the models work, AI has a bright future in healthcare and can play a big role in saving lives.

Student Council Election Portal
Authors:-Professor Bharati Bisane, Vaishnavi Rajendra Borse, Resham Sanjay Umale, Sharddha Bhagwan Borate

Abstract- Traditional paper-based voting has been used for many years, but it comes with several challenges, such as security risks, lack of transparency, human errors, and privacy concerns. To solve these issues, we propose a blockchain-based voting system for college elections. Blockchain technology is in high demand because it offers security, transparency, and decentralization. Our goal is to use blockchain to create a secure and tamper-proof election system at the college level. This system will also help developers build and deploy smart contracts, which ensure accuracy and provide quick voting results.
Smart contracts automate the voting process, making vote counting secure and preventing fraud. Blockchain stores all transactions in blocks within a decentralized network, ensuring that no single person can manipulate the results. Overall, th is system will make the voting process faster, more secure, and more reliable. It will also reduce costs since there is no need to print ballots.

DOI: 10.61137/ijsret.vol.11.issue2.343

Complaint Management System for Migrant Workers in Tamil Nadu
Authors:-MS.B.Manjubashini, Ranjithkumar S, Sevvelkaran Palani Vetrivel, Srikanth S, Jahan V

Abstract- The proposed Complaint Management System for Migrant Workers in Tamil Nadu is a digital platform designed to empower migrant laborers by providing a streamlined, accessible, and multilingual interface for registering and tracking workplace grievances. Recognizing the diverse linguistic backgrounds and socio- economic challenges faced by migrant workers, the system incorporates features such as real- time complaint tracking and automatic translation of the app interface and complaint content into the worker’s native language (e.g., Hindi, Bengali, Odia, Telugu) This user-centric platform allows workers to easily lodge complaints related to: wage disputes, unsafe working conditions, harassment, poor living standards, and other labor rights violations. Each complaint is assigned a unique tracking ID, enabling real-time status updates through a simple dashboard. The translation feature ensures inclusivity, bridging the communication gap between workers, government authorities, and employers.

DOI: 10.61137/ijsret.vol.11.issue2.362

The Impact of Personalization on E-commerce Conversion Rates: An Empirical Analysis of 100 Respondents
Authors:-Hitesh Ramdasani

Abstract- This study investigates the impact of personalization strategies on e-commerce conversion rates, examining the perceptions of 100 online shoppers. Through a simulated survey approach, the research explores the relationship between the level of perceived personalization experienced by consumers and their likelihood of making a purchase. The findings from the simulated data analysis suggest a positive correlation between higher levels of perceived personalization and increased conversion likelihood. These results underscore the importance of implementing effective personalization techniques for e-commerce businesses seeking to enhance customer engagement and drive sales.

DOI: 10.61137/ijsret.vol.11.issue2.344

Nebula Cloud: Revolutionizing Cloud Computing with AI-Driven Solutions
Authors:-Abhiraj Bondre

Abstract- That is why NebulaCloud is the new state of the art within cloud computing performance, utility and new frontiers in AI- centric workflows. optimized for AI-enabled cloud services, and scalable, intelligent, and elastic for the cloud. As the world becomes more and more AI-driven, NebulaCloud is identifying as the key to the future by offering the high-performant infrastructure serving service tailored for AI workloads that allow organizations to embrace AI, machine learning (ML), and deep learning (DL) to improve their operations, enhance decision-making and accelerate innovation This paper discusses NebulaCloud’s technology architecture, core features, competitive advantage, and transformative use cases in fields like healthcare, finance, and manufacturing. This research also emphasizes NebulaCloud’s significant contributions to the evolution of AI in cloud computing, its role in accelerating AI adoption across enterprises, and future prospects through the analysis of its architecture and market impact.

Waste-to-Energy: Innovations and Approaches in Sustainable Waste Management
Authors:-Nandini Kalange, Vaishnavi Patil, Rucha Wandhekar

Abstract- The increase in global waste generation has resulted to severe environmental and economic challenges,including land pollution, diminishing landfill space and greenhouse gas emissions. Waste-to-energy (WTE) technologies convert waste into useful energy, addressing both waste disposal and energy demand challenges. Various WTE methods, which includes incineration, anaerobic digestion, gasification, pyrolysis, and hydrothermal carbonization, offer unique advantages and drawbacks. This paper reviews existing WtE technologies in terms of economic feasibility, efficiency and environmental impact. By integrating WtE into a circular economy framework, these systems contribute to sustainable waste management, energy security, and decreases environmental degradation. However, many challenges such as high initial investment, emissions control, and public perception need to be addressed. The results demonstrate how WtE may play a significant role in waste management and sustainable urban development plans with the correct technology and legislative support . The paper highlights the importance of improved waste segregation, legal motivations, and technological improvements to promote Waste to Energy adoption globally.

AI-Inegrated Wearable Device for Mental Health Monitoring and Early Illness Detection
Authors:-Pranav Pawar, Parth Shinde

Abstract- Mental health disorders are often underdiagnosed due to lack of continuous monitoring and real-time assessments tools. Develop a wearable device integrated with AI to monitor mental health by analyzing physiological and behavioral data. This device will help to detect early signs of stress, anxiety, and depression. Our findings demonstrate that continuous monitoring using AI-driven analytics can identify subtle patterns associ- ated with mental health conditions. By detecting deviations in physiological parameters, the system effectively predicts early symptoms of stress and anxiety.The wearable device will collect real-time data using embedded sensors. AI algorithms will analyze these data points and compare them with ideal mental health indicators. A web-based interface will display results. The proposed system can diagnose mental health problems providing continuous monitoring. Reduce the burden on mental health professionals and improve patient outcomes by timely mental health assessments that rely on subjective reporting , this wearable device introduces on AI based data driven approach.

Security Challenges in Quantum Communication
Authors:-Sanyog Swami

Abstract- Rapidly evolving technology continues to reap new increases even in the world’s most remote areas. Classical physics has been at the forefront of advances in medical, energy, wireless communications, computing, and artificial intelligence technology. Traditional communication systems using classical bits are reaching their limits. Quantum technology, especially quantum bits (qubits), is a game-changing alternative with advantages in security and efficiency. This study examines the basics of quantum communication on the underlying principles, purposes, and ways to process information. It introduces a model for quantum communication systems for which examples of applications are given and challenges encountered for implementation into practical use is indicated. The future of enhanced security and efficiency of digital networks and communications will derive from quantum advances.

Embedded Intelligence: Power-up the Future of Robotics
Authors:-Saurabh Pawar, Tanvi Sonawane, Shriya Malode

Abstract-Embedded system is an important aspect of robotic development through the capabilities to facilitate AI machine learning and real-time control in robotics. The aim is to identify how embedded systems enhance robotics performance and autonomy. From the review of the existing research, it is observed that technology such as microcontrollers allows seamless sensor incorporation, low latency control, and AI decision-making. The strategy centers on how the embedded system enables robots to operate independently and effectively. An Embedded system is committed to computing systems that manage different robot parts such as sensors, actuators, and processors. Real-world applications involve autonomous robots in sectors such as healthcare, agriculture, and manufacturing where embedded systems improve accuracy and efficiency. This research emphasizes the role of embedded systems in defining the future of robotics, providing cost-effective and scalable solutions for real-world issues.

Empowering the Capability of Solar Panels using Nanotechnology
Authors:-Manasi Nitin Owhal

Abstract-Solar energy is among all the renewable energy sources, the most promising one, but due to conventional silicon- based solar panels, its Capability is limited. Reflectance, Ab- sorption,and Recombination losses are the 3 main factors that highly affect the Capability of solar panels. Therefore, there is a requirement to enhance the Capability of solar panels by exploring new technologies. Develop improved light absorption and charge carrier collection nanostructured solar cells, also investigate and evaluate the effects and potential of nanostruc- turing on the properties of solar panels, such as optical and elec- trical. Nanostructuring techniques include nanolithography and nanoimprint lithography. Software packages such as COMSOL and Sentauru can be utilized to simulate nanostructured solar cell properties like optical and electrical. Results in higher quantum efficiencies, optical properties like reflectance and transmittance, were significantly improved. By enhancing the Capability of commercial solar panels, nanostructuring of solar cells can be utilized. This can lead to a reduction in the cost, making it more competitive with fossil fuels. Effects of nanostructuring on the optical and electrical properties of solar cells, which outcomes in new insights of the mechanisms, focusing on the enhanced Capability of nanostructured solar cells.

Dynamic Wireless Charging Using Inductive Power Transfer System for Electric Vehicle
Authors:-Yash Sonawane, Hrishikesh Saindane, Rudrakash Chaudhari

Abstract-The rapid global transition towards sustainable transportation has intensified the demand for efficient and convenient charging solutions for electric vehicles (EVs). Wireless charging, particularly through Inductive Power Transfer (IPT), has emerged as a revolutionary alternative to conventional plug-in systems, eliminating physical connectors and enhancing user convenience. IPT operates on the principle of electromagnetic induction, enabling seamless energy transfer between a stationary charging pad and a vehicle-mounted receiver coil. This technology not only mitigates range anxiety by enabling dynamic and stationary charging but also enhances system longevity by reducing mechanical wear and tear. This paper explores the fundamental principles, system architecture, and advancements in high-efficiency resonant inductive coupling for EV wireless charging. It further examines the challenges associated with misalignment, energy transfer efficiency, electromagnetic interference, and standardization. Additionally, integration with smart grids, renewable energy sources, and bidirectional power flow for vehicle-to-grid (V2G) applications is discussed to highlight the future potential of IPT in creating a sustainable EV ecosystem. Through a comprehensive analysis of recent advancements and real-world implementations, this study aims to provide valuable insights into the feasibility, performance optimization, and scalability of wireless charging for the next generation of electric mobility.

Scheduling the Complexities in Semiconductor Manufacturing with Solution Techniques and Challenges in Future
Authors:-Onkar Sapkal, Bhavesh Pokale, Samrudhi Samarth

Abstract-In semiconductor manufacturing, errors in schedul- ing can cause delayed productions, extra costs, and low through- put. In semiconductor manufacturing, scheduling operations is very complex, dynamic, and needs to meet a variety of quality requirements. The present paper is aimed at discussing the problems, investigating advanced solution techniques, as well as highlighting the challenges in the scheduling manufacturing oper- ations of semiconductor in the future. The main focus contains the delays in production stages, impact of downtime for equipment, as well as the need for real-time adaptability. Promising options contains AI-based algorithms, predictive analytics, and hybrid scheduling frameworks. A comprehensive review of literature as well as case studies is conducted, highlighting the existed methodologies, their effectiveness, and gaps in current research. Such insights from this work would guide manufacturing compa- nies in optimizing the scheduling process to increase operational efficiency and maintain quality. By systematic review of the scheduling arena in semiconductor manufacturing, this study opens doors for futuristic solutions and research.

Real-Time Health Monitoring using Wearable IoT Devices
Authors:-Prathamesh Thakre, Mayur Dhumal, Krishna Thakre

Abstract-The rising prevalence of chronic conditions such as cardiovascular disorders and respiratory diseases has under- scored the need for continuous health monitoring. This research aims to develop and evaluate a real-time health monitoring system using a wearable IoT device. The proposed system integrates biomedical sensors, a microcontroller ESP32 ,MAX30100 Pulse Sensor,DS18B20 Temperature Sensor and a wireless communica- tion module to transmit real-time health data to a cloud-based platform. The system can provide early alerts in emergencies, as- sist healthcare professionals, and support further advancements in wearable technology. By ensuring accurate health tracking and efficient data handling, this approach represents a step forward in next-generation digital healthcare solutions.

IOT Based Baby Cradle Monitoring System
Authors:-Mrunal Pawar, Aman Pawar

Abstract-Traditional baby cradles require constant supervision making it difficulty for parents to balance child care daily task existing monetary solution provide limited automation and real time feedback this research propose on IOT based cradle system that integrate sensor to monitor babies movement crying temperature and humidity the purpose of the story is to development creditor that monitor keep parameter such as temperature humidity motion and crying better than using IO tea sensor this system automatically at least parents we are mobile application and provides real time monitoring and data analysis the methodology in in hall designing of prototype integrate with sensor a microcontroller and wireless communications like balancing or Atlas notification and transmits information to a cloud base platform for remote monitoring experiment finding demonstrate the systems effectiveness in detecting impact district regulating cradle motion and maintaining optimal environmental condition practical implementation shows that the system can significantly reduce parental workload while improving in fact safety and comfort this research contribute original value by proposing and IOT enable solution for intelligent cradle monitoring integrated automation and remote accessibility.

Autonomous Weed Cutter Using Raspberry PI and Image Processing
Authors:-Rajesh Sonavane, Praneet Parbhane, Swanand Sarvadnya

Abstract-Traditional method of weed removal requires a lot of human efforts or sometimes requires a lot of chemical pesticides which may decrease the fertility of soil and this reduces crop productivity. The purpose of this method is to use advanced technologies like machine learning, image processing and cutting mechanism to remove weed or unused plants without damaging the main crop. By suing process like Raspberry Pi over other controllers, we can reduce time delay for image processing and this removing weed in less time. The Raspberry pi camera captures the images of field and then by using image processing we can distinguish a crop and a weed/unused plant. this provides precise and exact coordinates of the weed thus by using navigation or GPS module we can navigate our robot to the precise location of weed and we can execute further cutting process of weed. This method will be used to reduce manual labor, increasing crop yield and health also it prevents accidental crop damage, unlike mechanical weeders. This technology offers precise weed removal without using human efforts and without impacting the soil fertility.

Performance Analysis of STATCOM with Fuzzy-PI Controller in Power System Network
Authors:-Indrajeet kumar, Professor Ashish Kumar Rai

Abstract-This paper presents the design and implementation of a hybrid fuzzy logic and Proportional-Integral (PI) control scheme for a Static Synchronous Compensator (STATCOM) aimed at enhancing power system stability. A power frequency model of the STATCOM is proposed, and Fuzzy-PI controllers are developed for both the main and supplementary control loops. The primary Fuzzy-PI controller ensures constant voltage regulation, while the supplementary controller effectively damps inter-area oscillations. The integrated STATCOM model, including the hybrid controllers, is implemented within a conventional transient stability simulation framework. Simulation studies on a four-generator power system demonstrate that the Fuzzy-PI-controlled STATCOM delivers performance comparable to a well-designed conventional controller, confirming its effectiveness and robustness in stabilizing the system

A Machine Learning Approach to Heart Disease Prediction: 5-Fold Cross Validation and Hyperparameter Optimization
Authors:-Dr.N.Chandrasekhar

Abstract-The primary objective of this research is to develop an effective predictive model for heart disease using various Machine Learning (ML) algorithms. In this study, four different ML models—Gradient Boosting (GB), Random Forest (RF), LightGBM (LGBM), and AdaBoost (AB)—were implemented and evaluated for their prediction accuracy. To ensure the reliability and generalization of the models, 5-fold cross-validation was applied along with Grid Search Cross Validation (Grid Search CV) for hyperparameter tuning. This technique helped in identifying the optimal parameters for each algorithm, thereby improving their performance. Among all the models, Gradient Boosting achieved the highest accuracy of 95.08%, followed by Random Forest and LightGBM, both with 91.80%, and AdaBoost with 90.16%. These results highlight the effectiveness of ensemble-based ML models, particularly Gradient Boosting, in accurately predicting the risk of heart disease.

DOI: 10.61137/ijsret.vol.11.issue2.345

Evaluating the Impact of ABS on Road Safety with a Data-Driven Approach to Reducing Accidents
Authors:-Abhijit Mohite, Ganesh Matole, Ketan Patil

Abstract-Road accidents are a major global concern, often resulting from the loss of control during emergency braking. While ABS (Anti-lock Braking System) enhances control, some studies suggest it may not significantly reduce overall crash rates. This research aims to evaluate the impact of ABS on accident reduction by statistically analyzing crash data. The objective is to determine whether ABS-equipped vehicles experience fewer accidents, particularly under adverse conditions, and to assess its role in enhancing road safety.

AI in Finance: Challenges, Techniques, and Opportunities
Authors:-Piyush Takalkar, Sujit Sherkhar, Chirag Shrigod, Umar Shaikh, Punashri Patil

Abstract-The use of Artificial Intelligence (AI) in banking has significantly grown in the recent past, transforming some of the most important processes in banking, investment, insurance, and regulation. As financial information becomes more complex and gigantic, AI methods provide solutions to issues like fraud detec- tion, credit scoring, algorithmic trading, and risk management. This blending, nonetheless, comes with emerging issues of trans- parency, equity, interpretability, data privacy, and regulatory harmonization. This book provides an extensive overview of AI in finance, considering traditional and contemporary strategies, and identifies new research areas and trends with a focus on domain-specific, ethics-oriented, and explainable AI models of top concern.

Next-Gen Health Solutions
Authors:-Assistant Professor Priti Bharambe, Vikas Mahandule, Shraddha Phulsundar, Priti Aivale, Shivanjali Shinde

Abstract-The integration of Information Technology (IT) in healthcare has transformed patient care, data management, and operational efficiency. This paper explores the role of IT solutions in enhancing healthcare delivery, focusing on electronic health records (EHRs), telemedicine, artificial intelligence (AI), and blockchain technology. IT innovations facilitate real-time data sharing, improve diagnostic accuracy, and enable remote patient monitoring, thereby increasing accessibility and reducing costs. Despite significant advancements, challenges such as data safety, compatibility, and ethical concerns persist. This study examines both the advantages and limitations of IT-driven healthcare, proposing strategies to optimize its implementation. By leveraging technology effectively, healthcare systems can enhance patient results and overall efficiency, clearing the path for a more connected and intelligent healthcare ecosystem.

DOI: 10.61137/ijsret.vol.11.issue2.346

Ai-Driven Facial Health Analysis Using Deep Learning and Gemini Llm
Authors:-Shivansh Rajbhar, ManuKumar Yadav, Raj Padval, Professor Manisha Hatkar

Abstract-A promising non-invasive method for evaluating dermatological disorders and determining general wellness is facial health analysis. This study introduces a new AI- powered facial health analysis system that combines the Gemini large language model (LLM) for individualized health interpretation with deep convolutional neural networks (CNNs) for image-based categorization. Real-time or uploaded face picture capturing is done by the system, which then uses OpenCV and dlib to identify landmarks before feeding the data into a transfer learning pipeline that uses the ResNet and VGG architectures. Classification results are converted into natural language health reports using the Gemini API. Real-time health insights are made possible by the solution’s deployment using Flask and Streamlit. Tests conducted on a carefully selected dataset showed that the system could detect common facial health issues with up to 92.1% accuracy, such as acne, dryness, redness, or pigmentation. This multidisciplinary approach has promise for tracking personal wellness, telemedicine, and preventative healthcare.

Disease Prediction Model Using Multi-Modal Data Fusion
Authors:-Shruti Deokule, Dipti Kause, Suhani Korde, Suhani Korde

Abstract- With recent developments in machine learning and healthcare informatics Strong disease prediction models have been made possible . In order to improve the accuracy and dependability of early disease diagnosis, we present a multi-modal data fusion system in this paper. Advanced fusion techniques that can lessen the drawbacks of single-modality models are used to integrate heterogeneous data sources, such as wearable sensor readings, genomic data, medical images, and electronic health records (EHR). Our method integrates crucial information from multiple datasets by combining feature selection, preprocessing, and ensemble learning. In comparison to the traditional models, we find that the experimental results produce 15% higher prediction accuracy and lower error rates—down to 2.3% for cases of chronic disease.

href=”https://doi.org/10.61137/ijsret.vol.11.issue2.351″>10.61137/ijsret.vol.11.issue2.351

Company Inventory Management System Using Appian
Authors:-Balaji S, Dr. Krithika. D. R

Abstract- The product in every company decides the availability of resources according to the user needs. Each product must be useful to the user in certain ways to decide as per the demands. This paper speaks about how the products are handled by different departments from storage team to user by choosing the control of each product for the supply and flow in a company. This paper also conveys that this will tell all the activities happens in a single company for deciding how the storage team is very important in storing the products, each team decides the product supply to make it useful for users. When a product gets requested by user it must be decided by the team to inform the availability. The communication mechanism in this application is very useful in understanding the entire system by each team very easily. So, every activity in this application completely named as Inventory to explain about the management of this application.

DOI: 10.61137/ijsret.vol.11.issue2.352

The Comparative Analysis on Old Lanour Laws with New Labour Codes
Authors:-Majitha. A

Abstract-The Labour regulations in India has experienced a vital transformation with the introduction of New labour codes which aimed at simplifying the complexity and inadequate to function in this digital India. Empowering the worker’s life is essential for building a autonomy, thriving, and empowered India. It has been over 73 years since we got independence, approximately 90% of the workers working under the unorganised sector, they all are lacked access to basic social security benefits. The Government of India has done extraordinary job by enacting the four labour codes by consolidating the existing labour laws in central level. This research paper focuses on the significant changes which have been initiatives by the new codes this can be analyied by doing comparative analysis on old labour laws with new labour codes. This paper focus on impacted party such as unorganised sector, gig workers, platform workers. This paper uses the doctrinal methodology to analyse and collect data. And this paper overview the background of labour laws in India and need for labour reforms and transformation of four labour codes and short summary of four new codes and suggestion.

AI in Cloud Computing: Enhancing Efficiency, Scalability, and Intelligence in Modern Infrastructure
Authors:-Tushar Gajbhiye, Dr. Meenakshi Thalor

Abstract-This study delves into the incorporation of Artificial Intelligence (AI) in cloud computing infrastructure, investigat- ing its revolutionary effect on scalability, performance tuning, cost savings, and security. A comparative quasi-experimental approach was adopted, comparing two groups of users—AI- optimized cloud systems and conventional cloud systems—on parameters like processing latency, cost savings, and user ex- perience. Findings show a 30% boost in processing efficiency and outstanding improvements in dynamic resource management using AI-based orchestration. These findings emphasize AI’s role in transforming static cloud infrastructures into intelligent, self- optimizing ecosystems suitable for diverse application domains.

AI in Finance: Challenges, Techniques, and Opportunities
Authors:-Piyush Takalkar, Sujit Sherkhar, Chirag Shrigod, Umar Shaikh, Punashri Patil

An Artificial Intelligence-Focused Smart Infrastructure Management Framework for Urban Cities with the Integration of Computer Vision and Predictive Analytics
Authors:-Shriya Naphade, Piyush Panchmukhe, Assistant Professor Mrs. Anuja Phapale

Abstract-With the rapid growth of cities, there is a growing need for smart and effective infrastructure management systems. This paper presents an all-encompassing AI-based solution for solving the complex issues of urban infrastructure management in smart cities. Relying on the strengths of computer vision, predictive analytics, and the Internet of Things (IoT), the system offers real-time monitoring and proactive decision-making. Through the examination of live video feeds and real-time streams of sensor data, the system can effectively determine the structural health of main urban infrastructure like roads, bridges, and public utilities. It automatically identifies visual anomalies in the form of potholes, surface cracks, traffic con- gestion, and material degradation through Convolutional Neural Networks (CNNs). Concurrently, Long Short-Term Memory (LSTM) models examine temporal patterns in sensor readings to predict future wear and calculate maintenance needs with high accuracy.

DOI: 10.61137/ijsret.vol.11.issue2.347

System to Check the Healthiness of the Earthing System and Alert Staff in Case of Malfunction
Authors:-Deepak Pal, Arun Kumar Singh, Md. Tahir, Mohd. Zaid, Assistant Professor Kishan Kumar

Abstract-This paper presents an IoT-based system for detecting faults in underground cable earthing using an ESP32 microcontroller unit and a soil moisture sensor. The system monitors soil moisture levels around the earthing rod to ensure proper conductivity. Variations in soil moisture can lead to earthing inefficiency, posing safety risks. Earth faults are not only by far the most frequent of all faults, but the fault currents may be limited in magnitude by the neutral earthing impedance, or by the earth contact resistance which makes detection challenging for conventional protection schemes. Currently, normal earth fault protection together with sensitive earth fault protection has been employed in both distribution networks to detect and clear earth faults. There have been incidences where earth fault detection has been extremely challenging as fault values drop significantly and the protective device does not have sensitivity to detect and isolate the faulty equipment. The earth resistance is detect by moisture of earth using moisture sensor Real-time data acquisition, wireless transmission, and alerts help in preventive maintenance of earthing systems, particularly in rural or inaccessible areas. Experimental results show that this system is reliable, low- cost, and scalable for smart grid applications.

Harnessing Computer Vision for Precision Agriculture: Advancements in Crop Monitoring, Yield Prediction, and Disease Identification
Authors:-Swaraj Kawade, Anurag Kawade, Rohit Kashid, Aditya Jedhe, Rajdeep Jagtap

Abstract-The use of modern technologies such as computer vision and artificial intelligence individually or in tandem are modifying farming methods. These technologies are assisting in agricultural practices as the world population is expected to peak at 9.7 billion by 2050. The objective of this paper is to analyze major advancements within the years 2020 to 2024 regarding the role of computer vision in agriculture, specifically in crop health monitoring, yield prediction, and early detection of plant diseases. Some impressive progress includes: Autonomous weed management systems with 96% accuracy (Praveenraj et al., 2024). Near 99% accuracy in crop yield predictions from machine learning models (Sharma et al., 2023). Deep learning algorithms correctly identifying plant diseases at a rate of 99.35% (Li et al., 2021). Additionally, the development of autonomous vehicles has contributed to safety within agricultural fields, while AI image generation contributes to predicting potential yield imagery, along with real-time field monitoring robotic systems. These developments are positive strides towards sustainable agriculture. On the other hand, these systems always carry limitations like dealing with dynamic field conditions, insufficient amounts of data, and the need for high-end processing units. This paper evaluates the advancement to date, what it means in terms of practical agriculture, and how we can further progress:

DOI: 10.61137/ijsret.vol.11.issue2.348

AI-Powered SQL Assistant: Transforming Natural Language into Optimized SQL Queries
Authors:-Atharv Deshmukh, Manali Gawade, Ronit Fulari

Abstract-Databases have a steep learning curve, littered with schema design, SQL di- alects, and performance optimizations. Formulating efficient SQL queries is a chal- lenging process to tinker with, and this is one of the reasons many developers and analysts are blocked. Here we present an AI-based SQL Assistant that utilizes cutting-edge AI models to transform natural language requests into fast SQL code. It lives on top of a variety of SQL dialects, including Spark SQL, PostgreSQL, and MySQL, and provides schema suggestions and smart executor queries. It can use a feedback loop with machine learning methods to improve performance after the sys- tem is deployed based on users adapting the system to query patterns. We provide experimental evidence to show that the proposed solution not only improves query performance and execution time but also accuracy so that the database interactions are smooth for non-experts.

DOI: 10.61137/ijsret.vol.11.issue2.349

Optical Mark Recognition (OMR) MCQ Automated Grading
Authors:-Rushikesh Patil, Parwez Nadaf, Assistant Professor Dr. Zalte S.S.

Abstract-This paper presents the development and implementation of an Optical Mark Recognition (OMR) system designed to automate the grading of Multiple-Choice Questions (MCQs) using a laptop’s built-in webcam. The purpose of this research is to address the inefficiencies and inaccuracies associated with traditional manual grading methods by using advanced image processing techniques when conducting physical examinations. The system utilizes the powerful OpenCV and NumPy libraries for image processing and mathematical operations, ensuring precise and efficient analysis of MCQ answer sheets. The methodology involves capturing images of MCQ sheets through either a live webcam feed or static image files, which are then resized to a standard resolution. Key pre-processing steps include grayscale conversion, Gaussian blurring, and Canny edge detection to identify and analyse the marked answers. Advanced contour analysis is employed to detect the boundaries of the MCQ and answer boxes, followed by perspective transformation to flatten the image for detailed examination. The extracted answers are then compared against a predefined answer key to calculate scores, which are visually represented on the marked image. The findings demonstrate the system’s accuracy and efficiency in grading MCQs, significantly reducing the time and effort required for manual grading. The system provides immediate feedback, highlighting correct answers in green and incorrect answers in red, along with the total score. The results are documented and saved, facilitating performance tracking and feedback. In conclusion, this OMR system offers a cost-effective and accessible solution for educational institutions, enhancing the grading process by providing immediate and reliable results. This research contributes to modernizing educational assessments, promoting a more interactive and engaging learning environment.

Wind Turbine Technology for Environmental Sustainability
Authors:-Mohit Patil, Mohit Patil, Sujal Makote

Abstract-The increasing request for renewable power has brought focus on the efficiency and sustainability of wind turbine technology. One of the biggest problems in the acceptance of wind power is grid, integration and variation of power which causes voltage instability, frequency variations and challenges in synchronizing wind energy with current Electrical grids further, the study purpose regarding grid integration to enhance efficiency, stabilize power output and decrease ecological footprint of wind energy systems. AI predicted a power conditioning system as major progress in enhancing grid integration and mitigating power fluctuations. The course of action of the studies includes an exam grid, integration and power fluctuations using data analysis, case studies and simulations, accessing smart grades, energy storage, and power control for announce stability for policy Makers and engineers or energy firms. There is a challenge for investment in intelligent grades, enhance power regulation, methods of wind forms for maximum stability and efficiency. The outcomes are secure Wind energy, stable and efficient, enhanced synchronization, storage and forecasting, improves grid resilience and speed up renewable uptake.

Enhancing User Comfort in Virtual Reality: A Biofeedback-Driven Adaptive Rendering Framework
Authors:-Raunak Khandare, Aniket Satpute, Arsh Shaikh

Abstract-Virtual Reality (VR) systems have shown immense potential across various fields, including healthcare and educa- tion. However, user discomfort caused by latency and motion sickness remains a significant barrier to widespread adoption in prolonged use. This study introduces a novel framework that dynamically adapts to user physiological responses, thereby minimizing discomfort and improving the overall VR experience. A user study involving 50 participants was conducted to evaluate the effectiveness of the proposed system compared to traditional systems. Findings indicate a significant reduction in user-reported discomfort and a 40% improvement in latency performance, demonstrating the effectiveness of biofeedback-driven adaptive rendering. The methodology involves the design and imple- mentation of a biofeedback-enabled VR system that monitors user physiological signals, such as heart rate and galvanic skin response, in real time. These signals are used to adjust rendering parameters, such as frame rate and field of view, to mitigate motion sickness and latency. The practical implementation of this research is far-reaching, as it enables more user-friendly VR systems suitable for prolonged use in therapy and training. The originality of this work lies in its unique combination of biofeedback and adaptive rendering, offering a novel solution to a long-standing problem in VR technology and enhancing the user’s experience.

Smart Dustbin Using Machine Learning
Authors:-Awej Fardin, Yusuf Amravatiwala, Faiz Mallick, Hamza Bartanwala, Assistant Professor Qudsiya Naaz

Abstract-Smart Dustbin Using Machine Learning for Waste Segregation and effective waste management. The Smart Dustbin uses machine learning (ML) to automatically classify and segregate waste into three categories: recyclable, biodegradable, and non- biodegradable. The system integrates sensors and image recognition technology to analyze waste materials in real-time, ensuring accurate categorization. The smart dustbin employs a trained ML model that processes data from sensors and cameras to identify the type of waste.

The Comparative Analysis on Old Lanour Laws with New Labour Codes
Authors:-Majitha. A

Thermal Analysis of Engine Fins with Different Geometries and Materials
Authors:-Rajat Yadav, Assistant Professor S.N. Dubey

Abstract- In order to make the engine cylinder fins design simpler, CFD (Computational Fluid Dynamics) is used to analyze the thermal and mechanical behavior of the engine cylinder fins. In CFD, the heat transfer and pressure drop characteristics of the engine cylinder fins can be accurately predicted. This helps in understanding the performance of the engine cylinder fins and helps in making necessary modifications to improve the heat dissipation rate. Additionally, CFD can also be used to analyze the airflow characteristics and pressure distribution inside the engine cylinder. This helps in designing the fins properly to reduce the aerodynamic drag and improve the efficiency of the engine. The purpose of this article is to analyze the thermal properties of cylinder fins with different geometries using Ansys Workbench. The geometries were 3D modeled using SOLIDWORKS 2016 and their thermal properties were evaluated using Ansys Workbench R 2016. The change in temperature over time is an important factor in many applications, such as refrigeration, and accurate thermal modeling can help. You determine the key design parameters to improve performance. , The cylinder fin body material is AA 6061 aluminum alloy with a thermal conductivity of 160-170 W/mK. This material is used for current analysis of cylinder ribs.

Genetic Algorithm for Optimum Result Using Chromosom in Data Mining
Authors:-Dr. Divya Sakhuja

Abstract-Information or data are considered as elementary variable facts. Knowledge is considered as a set of instructions, which describes how these facts can be interpreted and use[1]. The proposed algorithm provides the result in quick period and only adds the data in result which are important data set. The proposed algorithm reduces that data set those have very less support and confidence. They produce the result graph in very attractive way. The algorithm has two parts, the first part perform the data mining algorithm and reduces the unwanted data sets and then perform the genetic operation from obtained results and then finally conclude the common properties of attributes. The genetic algorithm takes attributes 10th overall percentage, Distinction of two subjects on 10th results, area, income and parents’ occupations. They execute the genetic operation and find out the common features from them and conclude the results. An advanced learning method using a combination of perception and motion has been introduced. Emergent, self-organizing, reflective, and interactive (among human beings, environment, and artificial intelligence) knowledge processing is considered by using soft computing and by borrowing ideas from bio-information processing [2].

AI-Based Crime Scene Simulation through 3D Image Processing and Semantic Segmentation
Authors:-Om Hajare, Dr Meenakshi Thalor

Abstract-Crime scene simulation (CSS) technology has reached efficiency to need more innovation and great breakthroughs, but also the need for precision. AI-based approaches provide promise by coupling 3D image processing with semantic segmentation in order to support forensic analysis. Existing research has challenges such as, poor generalization computational inefficiency, and dataset diversity. We suggest a new framework using state-of-the-art AI algorithms to bridge these gaps. Our method integrates semantic segmentation for object classification and 3D imaging to produce high-resolution spatial Simulations. In contrast to previous research, our approach emphasizes real-time processing and flexibility across various crime scenes. This paper describes the methodology, compares existing works, and identifies future potential.

Startup Valuation Methods in the Gig Economy Era: Effectiveness and Challenges
Authors:-Om Hajare, Dr Meenakshi Thalor

Abstract-The gig economy has transformed traditional business models, introducing complexities in startup valuation. Unlike conventional businesses, gig startups rely on digital platforms, network effects, and scalable yet volatile revenue streams. This study critically examines traditional valuation methods—Discounted Cash Flow (DCF), Comparable Company Analysis (CCA), and the Venture Capital (VC) method—and their effectiveness in assessing gig startups. Challenges such as regulatory uncertainties, high user acquisition costs, and fluctuating profitability are analyzed. The paper proposes adjustments to valuation frameworks, incorporating factors like platform dependency, user engagement, and AI-driven predictive analytics. Understanding these challenges is crucial for investors and policymakers to enhance financial decision-making in this evolving landscape.

Sentiment Analysis on Social Media
Authors:-Mr.Shahbaz Ahmad, Assistant Professor Ms.Noorishta Hashmi, Assistant Professor Mr.Ehteshaam Hussain

Abstract-The rise of social media platforms has revolutionized communication, enabling individuals to share opinions, emotions, and experiences in real-time. With billions of users generating vast amounts of unstructured data daily, social media has become a rich resource for understanding public sentiment and social behavior. Sentiment analysis, a subfield of natural language processing (NLP), offers a computational approach to identifying and categorizing sentiments expressed in text data. This research focuses on the development and application of sentiment analysis techniques to analyze user-generated content on platforms such as Twitter, Facebook, and Reddit. By utilizing machine learning, lexicon-based methods, and deep learning approaches, this study aims to assess the effectiveness of various sentiment classification models. Techniques including Support Vector Machines (SVM), Naïve Bayes, Long Short- Term Memory (LSTM) networks, and BERT are evaluated using benchmark datasets. The paper also addresses the challenges inherent in sentiment analysis, such as sarcasm, slang, multilingual content, and data imbalance. The results demonstrate that context-aware models like BERT significantly outperform traditional approaches in detecting nuanced sentiments. The findings of this research have applications in fields such as brand monitoring, political analysis, customer feedback evaluation, and disaster response. Furthermore, the study emphasizes the ethical implications of mining and analyzing social media data, advocating for transparency, consent, and responsible data handling. Sentiment analysis, also known as opinion mining, has emerged as a critical tool in natural language processing (NLP) for extracting subjective information from social media platforms. The exponential growth of user-generated content on platforms such as Twitter, Facebook, and Instagram has made sentiment analysis indispensable for businesses, governments, and researchers seeking to understand public opinion, brand perception, and emerging trends. This paper provides a comprehensive review of sentiment analysis techniques, challenges, and applications in the context of social media, while also discussing future research directions to enhance accuracy and scalability.

DOI: 10.61137/ijsret.vol.11.issue2.353

Predictive Modelling of Stock Market Prices Using Machine Learning Web App
Authors:-Akanksha Bhagwan Bangar, Dr. Santosh Jagtap

Abstract-The stock market is a dynamic environment influenced by numerous factors, making the prediction of stock prices a challenging yet critical task. Traditional methods often fall short due to the complex and volatile nature of financial markets. This project focuses on developing a machine learning-based web application for predicting stock prices, leveraging advanced algorithms to identify hidden patterns within historical data. The core of the application is built on the Long Short-Term Memory (LSTM) network, a specialized form of Recurrent Neural Network (RNN) designed for time series forecasting. LSTM networks excel in capturing long-term dependencies in sequential data, making them highly effective for financial predictions where past trends influence future movements. The model processes historical stock price data, analyzing trends, fluctuations, and patterns to predict future prices with a higher degree of accuracy. By maintaining an internal state, the LSTM can retain valuable information over time, providing robust forecasting capabilities. The web application offers an interactive interface where users can input stock symbols and view predicted price trends alongside real-time data. This feature enhances user engagement and decision-making processes, aiding investors in strategic planning. The project not only demonstrates the potential of machine learning in finance but also highlights the integration of predictive models into practical applications. The successful implementation of this system could contribute to more informed investment decisions, potentially yielding significant profits.

DOI: 10.61137/ijsret.vol.11.issue2.354

Consumer Behavior Analysis on Sales Process Model Using Process Discovery Algorithm for the Omnichannel Distribution System
Authors:-Professor Mr. S.Naresh Kumar Reddy, K.Karthik, K.Namratha Daisy, S.Divya Sree, S.Anshu

Abstract-Currently, OMNI channel distribution services are experiencing very rapid development around the world. In the OMNI channel distribution services, each existing sales channel will be connected to each other through integration capabilities.This is able to provide the best experience for consumers when shopping both online through mobile devices, laptops, and in physical stores. On the one hand, it facilitates the marketing process, but on the other hand, business people have difficulty reading the behavior of consumers who use OMNI channel distribution services.In this project, an experiment was carried out using the sales event log dataset generated from the OMNI channel distribution service system. Service channels used are Marketplace, Web Store, Social Media, and Social Media Shop. Sales process modelling is generated using the Inductive Miner Algorithm, Heuristic Algorithm, Alpha Miner Algorithm and Fuzzy Miner AlgorithmThen the next step is to measure the process model obtained by Conformance Checking. The purpose of process modeling and measurement is to obtain a sales process model that can predict consumer behavior patterns well.

DOI: 10.61137/ijsret.vol.11.issue2.355

AI Based Chatbot for Collating and Dissemination of Information on Groundwater
Authors:-Salandhri Shivani Yadav, Pantham Tharun Kumar

Abstract-This project’s objective is to develop an AI-based chatbot that can collect and deliver comprehensive groundwater information to users, including government officials, researchers, farmers, and the general public. In addition to being necessary for domestic, commercial, and agricultural operations, groundwater is also necessary for sustaining life. Despite its importance, obtaining groundwater-related data remains challenging and scattered, especially for non-technical users. This work presents the development of an AI-based chatbot system that simplifies the dissemination of groundwater information using an interactive, natural language interface. Users may get location-specific information about hydrogeological conditions, water quality indicators, water level scenarios, and available technical reports through the chatbot, which was created with a Flask-based backend and a React frontend. By utilizing fuzzy string matching and structured JSON data to handle imprecise searches, the system enhances accessibility and usability. It also makes it easier to create comprehensive groundwater extraction regulations, report downloads, and summaries. By offering a faster and more convenient method of obtaining data than is currently feasible, the chatbot aims to bridge the knowledge gap between users and publicly available groundwater data. Preliminary testing shows that the system is accurate, responsive, and reliable, indicating that it has a lot of potential for usage in administrative and instructional settings pertaining to water resource management.

DOI: 10.61137/ijsret.vol.11.issue2.356

Home Loan Prediction Using AI and Machine Learning
Authors:-Professor Mrunali Jadhav, Professor Shubhkirti Bodkhe, Mohit Niwant

Abstract-The home loan industry is crucial for the economy as it enables people to buy homes and invest in property. However, lenders often find it challenging to assess the trustworthiness of loan applicants, which can lead to financial losses if borrowers default. This research paper explores how artificial intelligence (AI) and machine learning (ML) can improve the prediction of home loan approvals. We analyze a dataset containing applicant information, such as income, credit score, and employment status. We evaluate several machine learning models, including Logistic Regression, Decision Trees, and Random Forest, to determine which is most effective for predicting loan approval.

AI-Driven Disaster Response
Authors:-Shrija Salian, Ayush Jha, Ramesh Kumawat, Manisha Phulpagare, Ms. Shradha Chaudhari, Dr. Nandini C. Nag

Abstract-Natural disasters such as floods, earthquakes, and wildfires cause significant loss of life and property. Timely detection and response are crucial for effective disaster management. This paper explores the use of artificial intelligence AI) and machine learning ML) techniques, specifically the VGG16 convolutional neural network CNN, for disaster detection and classification. By leveraging image classification capabilities, the proposed system aims to enhance disaster management workflows. Experimental results demonstrate that VGG16 achieves high accuracy in classifying disaster-related images, outperforming other models such as ResNet50 and InceptionV3. This study highlights the potential of AI-driven solutions in improving disaster preparedness and response strategies.

IoT-Based Electricity Theft Detection System
Authors:-Mohammad Gulrez Zaidi, Deepanshu Punj, Moseen Khan, Ms. Jyoshita Narang

Abstract-Innovative solutions for various industries have been developed as a result of the proliferation of Internet of Things (IoT) devices. IoT has the potential to completely change how electricity is produced, transmitted, and used in the electricity sector. The use of IoT for detecting and preventing electricity theft is one such application. Meter tampering, also known as electricity theft, is a significant problem that affects the revenue and profitability of electricity boards. It entails circumventing meters in an unlawful manner in order to use electricity without paying for it. This not only costs government’s money, but also puts consumers and the electricity grid in danger of injury or damages. In this project, we propose creating an IoT-based system to track down and stop electricity theft. Smart meters with sensors and communication capabilities make up the system, along with a central server for data processing and analysis. Electricity consumption patterns are continuously monitored by smart meters, which also send data to a central cloud-based database. The database values are utilized by the authorities when it discovers anomalies or suspicious activity upon close monitoring of the data stored in real-time. The proposed system could significantly lower the number of instances of electricity theft, increasing revenue and profitability for the electricity providers while enhancing consumer safety. By offering real-time information on electricity consumption and billing, it can also assist utilities in streamlining their operations and enhancing customer service.

DOI: 10.61137/ijsret.vol.11.issue2.357

A Decentralized Social Media Platform with Sentiment Analysis Using Blockchain
Authors:-Nitish Jha, Abhishek Chaudhari, Piyush Pandey

Abstract-This research proposes a decentralized social media platform built on blockchain technology with integrated sentiment analysis using Natural Language Processing (NLP). Traditional social media platforms face issues such as data privacy breaches, central control, and lack of transparency. The proposed system utilizes Ethereum smart contracts and a decentralized architecture to enhance trust, ownership, and user privacy. Sentiment analysis is applied to user-generated content to gain insights and improve user interaction. The system is implemented using the Remix IDE, MetaMask wallet, Sanity database, and ReactJS frontend. This approach provides a transparent, secure, and scalable solution for the next generation of social networking applications.

Welfare Services in Emergency Scenario Management
Authors:-Darshini.S, Dr. Poongodi.A

Abstract-This Application helps the user to gain the services that are needed for the daily emergency situations. This application provides the services like Petrol, Tow, Hospital, Ambulance and Pharmacy in a Single Module. This application is free for everyone. My goal is to reduce the cost, by providing the maximum service even in any emergency situations to the people needs in a friendly approaching interface. We added braille support so blindly and visually impaired people can also use this application. We added more reliable support system to guide you anytime at anywhere. This application is all in one friendly daily emergency services for people’s welfare.

DOI: 10.61137/ijsret.vol.11.issue2.358

CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
Authors:-Assistant Professor Mrs .Shandhini, Roopashree.M, S.Fasiha

Abstract-CIFAKE is a computer program that can detect fake images created by artificial intelligence. It to identifies fake images and provide explanations for its decisions. CIFAKE can help prevent the spread of misinformation and fake news by identifying fake images. This tool can be useful for individuals, organizations, and social media platforms to ensure the authenticity of online images. We introduce the CIFAKE dataset, which consists of 120,000 images (60,000 real images from the CIFAR-10 dataset and 60,000 synthetic images generated using latent diffusion models). Convolutional Neural Networks (CNNs) for binary classification (real vs. fake) and it utilizes Gradient Class Activation Mapping (Grad-CAM) for explainable AI (XAI) to interpret the model’s decisions. The results demonstrate that the given approach achieves a classification accuracy of 92.98%, for detecting AI-generated images.

Solar and Wind Power Electric Vehicle
Authors:-Assistant Professor N. E. K. Chandra., Assistant Professor P.T. Krishna Sai., J. Prudhvi Ganesh.3, Y. Venkata Sai Mohan Krishna., B. Rohit Chandra Sekhar., Md. Saleha.

Abstract-Energy crisis and pollution caused by vehicle emissions are one of the most important issues in the present society. Due to the charging time of battery of electric vehicle, requirement of charging on board is explored as option. This paper deals with the design of a hybrid model of a solar and wind, which uses the battery as its storage system. This system allows the two sources to supply the load separately or simultaneously depending on the availability of the energy sources. The power generated from the wind and solar is fluctuating in nature. The system obtains maximum solar energy during day time and maximum wind energy during the night because the wind blows more at night compared to day time. Therefore, battery of the vehicle can be charged by using hybrid energy system.

A Study on the Challenges in Income Tax Compliance among the Salaried Employees
Authors:-Lavanya S, Assistant Professor Praveen S V

Abstract-This study aims to explore the challenges faced by salaried employees in complying with income tax regulations. Income tax compliance is a significant aspect of the tax system, ensuring the effective functioning of a nation’s economy. However, salaried employees often face various barriers that hinder full adherence to tax laws. These challenges include insufficient knowledge of tax rules, the complexity of tax filing processes, lack of awareness about available exemptions and deductions, and limited access to professional tax advisory services. Additionally, the study examines the impact of digital platforms, such as online filing systems, in facilitating or complicating the compliance process. Data was collected through surveys and interviews with salaried employees from different sectors to identify common issues and concerns. The findings suggest that while digital platforms have made tax filing more accessible, many employees still struggle with understanding tax calculations, deadlines, and documentation. The study also highlights the role of employer assistance in simplifying the compliance process. Recommendations are provided to improve tax literacy, streamline filing procedures, and offer better support systems to enhance overall compliance among salaried employees. This research contributes to understanding the existing barriers in tax compliance and proposes potential solutions to foster greater tax adherence in the salaried workforce.

DOI: 10.61137/ijsret.vol.11.issue2.359

E-Commerce Price Comparison System
Authors:-Prof.Vimmi Malhotra, Kanchan Panwar,Jaya

Abstract-In recent years, mobile apps have become increasingly useful for everyday use. The objective of this project is to provide users with a convenient method to compare product availability and prices across different e-commerce platforms. By inputting the product details into the program, users can effortlessly compare prices from various sources. To compare the product details discovered on multiple websites simultaneously, the application’s databases are searched. To guarantee that they never overlook a fantastic deal, customers can also receive push notifications when items become available or go on sale.

DOI: 10.61137/ijsret.vol.11.issue2.360

Smart Monitoring of Water Absorption in Roads Using Sensor Technology – A Review
Authors:- Mr. Jeshurun Wakdey, MS Amarja Pawar, Mr. Sahil Dolas

Abstract-Roads are lifeline of our country as it is the best suitable method for all type of vehicles and most used by common people. The development of any country largely depends on the efficiency of its transportation system, because the transportation of a chain of activities related to economic development Human wants are satisfied by the production of good and its distribution. The road is ordinary type i.e. concrete road, WBM road or bituminous road. If we replace these roads by water absorbing road (WAR) we can save large quantity of water. Ordinary road constructed in cities majorly face the problem of flooding of road and because its top layer is impervious. In urban areas larger amount of rainwater ends up falling on impervious surfaces such as parking lots, driveways, sidewalks, and streets rather than soaking into the soil and becomes storm water.

DOI: 10.61137/ijsret.vol.11.issue2.361

Air Quality Prediction Using Regression Techniques
Authors:- Arpit Kumar, Aishwarya Prasoon, Asst.Prof. Dr. Madhumitha K

Abstract-Air pollution has become a critical environmental concern, necessitating accurate and reliable predictive models for monitoring air quality. This study presents a machine learning-based approach to predict the Air Quality Index (AQI) using various pollutants and meteorological parameters. The dataset underwent preprocessing and exploratory data analysis to identify key contributors to air pollution. Correlation analysis revealed strong dependencies between particulate matter (PM2.5, PM10) and gaseous pollutants (CO, NO2, SO2) in influencing AQI levels. Multiple machine learning models, including Decision Tree, Random Forest, and Linear Regression, were implemented to forecast AQI values. Performance evaluation indicated that ensemble learning models, particularly Random Forest, outperformed others in capturing complex relationships within the data. Additionally, wind speed and direction showed weak correlations with pollutant concentrations, highlighting the need for incorporating additional meteorological variables for enhanced predictive accuracy. This paper provides a data-driven framework for AQI forecasting, which can assist policymakers and environmental agencies in taking proactive measures to mitigate air pollution. Future improvements can focus on deep learning integration and real-time data streams to enhance prediction reliability and responsiveness.

Global Warming from Fossil Fuels: Causes, Impacts, and Pathways to Sustainability
Authors:- Addhish Kumar, Dr. Reshma Umair

Abstract-Global warming, the persistent rise in Earth’s average surface temperature, is primarily driven by human activities, with the burning of fossil fuels—coal, oil, and natural gas—serving as the leading cause. These fuels release significant quantities of greenhouse gases, such as carbon dioxide (CO₂) and methane (CH₄), which trap heat in the atmosphere, intensifying the greenhouse effect and triggering widespread climate disruptions. This paper explores the mechanisms through which fossil fuels contribute to global warming, detailing the chemical and physical processes behind greenhouse gas emissions. It examines the profound environmental, economic, and social consequences of these changes and proposes practical, forward-thinking solutions to address the crisis. By advocating for a swift transition to renewable energy, improved energy efficiency, robust climate policies, and global cooperation, this study highlights the urgent need for collective action to mitigate this pressing challenge and secure a sustainable future. The combustion of fossil fuels involves extracting and processing coal, oil, and natural gas, which, when burned, release CO₂ as a primary byproduct. Coal, rich in carbon, generates substantial CO₂ emissions per unit of energy. Oil, widely used in transportation and industry, emits CO₂ alongside pollutants like sulfur dioxide, which further harm the environment. Natural gas, often considered a cleaner option, emits less CO₂ but releases methane during extraction and transport—a gas with a warming potential roughly 25 times greater than CO₂ over a century. These emissions accumulate, absorbing and re-emitting infrared radiation, which warms the planet and disrupts its delicate balance.

Multiplayer Shooting Game (3D Game for PC using Unreal Engine 5)
Authors:- Anay Tripathy, Sanskar Agrawal, Rishabh Mukherjee, Chirayu.S.Zope, Professor Monika Vishwakarma

Abstract-This project documents the collaborative endeavor of four individuals in the development of a third-person shooter (TPS) game, utilizing Unreal Engine, C++, and Visual Studio Code. Through a synthesis of technical prowess and creative vision, the team embarks on a journey to conceptualize, design, and execute a captivating gaming experience. The project unfolds as a testament to the intricacies of game development, exploring the synergy between programming, artistry, and design. By leveraging modern game development tools and technologies, the team navigates the complexities of game mechanics, level design, multiplayer integration, and post-launch support. The culmination of their efforts is a polished TPS game that exemplifies their collective dedication, innovation, and expertise. Through this project, the team presents a comprehensive narrative of their collaborative journey, offering insights into the challenges, triumphs, and lessons learned in the pursuit of gaming excellence.

Breathewell: Design and Implementation of a Low- Cost Smart Ventilator with IoT-Based Monitoring
Authors:- Sanika Vagal, Abhijeet Gudekar, Sahil Jadhav, Professor Jyoti Gurav

Abstract-The COVID-19 pandemic exposed critical shortages in respiratory support systems, especially in under-resourced areas. In response, this study introduces “Breathewell” — an economical, Arduino-powered ventilator with integrated IoT functionality for real-time patient monitoring. The system automates AMBU bag compression using a servo-based mechanical actuator and offers customizable settings for tidal volume and pressure. It tracks essential health parameters like blood oxygen levels and body temperature using onboard sensors and wirelessly transmits the data via an ESP8266 module. The paper details the design, hardware setup, software integration, and performance evaluation of the prototype, highlighting its potential as an emergency-use ventilator for low-resource settings.

Approach to Design for Smart Rides
Authors:- Anjali Dhunde, Srushti Anturkar, Vaishnavi Bhelkar, Vaishnavi Gudadhe, Assistant Professor Dr. Jayant Hande

Abstract-Smart rides, an emerging innovation in the automotive industry, integrate advanced technologies to enhance driving safety, convenience, and overall user experience. Key features include obstacle avoidance systems, voice control interfaces, automatic rain wipers, and Bluetooth connectivity. Obstacle avoidance technology uses sensors, and cameras, to detect and prevent collisions, ensuring safer navigation through various environments. Voice control allows drivers to interact with the vehicle without taking their hands off the wheel, enabling seamless control of functions such as navigation, entertainment, and climate settings. The automatic rain wiper system detects rainfall and adjusts the wiper speed accordingly, enhancing visibility and safety during adverse weather conditions. Bluetooth control provides the driver with wireless access to connect devices, manage calls, and play media, all while maintaining focus on the road. These intelligent systems work together to create a more efficient, safer, and more enjoyable driving experience, marking a significant leap toward the future of autonomous and connected vehicles.

Evaluating the Impact of Low-Code and No-Code Platforms on Modern Software Engineering Practices
Authors:- Nandini Manvar, Gayatri Zagade, Shreyash Trimale

Abstract-Low-code and no-code (LCNC) development platforms are altering software architecture. These systems let users design applications with either very little or no coding. They employ ready-made components, drag-and-drop tools, and visual interfaces. This facilitates fast and simple application development. LCNC tools are usable by non-developers as well as developers. This cuts the demand for big development teams, boosts productivity, and saves time. LCNC systems are also helping to solve the worldwide shortfall of qualified software programmers. These days, several sectors make use of these platforms. They are applied in healthcare to create systems of patient tracking. In the classroom, they advocate e-learning environments. In finance, they enable automated procedures and reports. These instruments have certain restrictions even if they are quite helpful in many spheres. Apps needing to expand or manage more users raise issues regarding scalability. Another problem is security, particularly in apps handling private or delicate information. Customizing can be challenging since consumers might not have complete influence over the backend or code of the app. Notwithstanding these problems, LCNC platforms keep getting better. The situation of LCNC tools now is investigated in this work. It looks over their benefits and drawbacks. It also looks at currently used popular platforms and their practical applications. The paper addresses how conventional coding might be used alongside LCNC tools. One can get speed and adaptability from a hybrid development method. LCNC tools for basic parts and conventional code for challenging projects let developers handle both. Better performance and simpler maintenance are thus possible. Recommendations for applying LCNC tools in several environments round out the paper. These cover companies, software teams, and the educational fields. LCNC systems might become increasingly important component of contemporary software engineering as technology develops. They provide a fresh approach with less technical obstacles to create apps quicker and more effectively.

Yoga and Ayurveda: Importance in Present Era
Authors:-Assistant Professor Dr. Praveen Kumar

Abstract-Yoga and Ayurveda is the most ancient medical sciences. are the knowledge of life gifted by sages of ancient time. Yoga supports the first aim of Ayurveda. Yoga and Ayurveda plays a potential role in improving host immunity and reduces the severity of the infection but also helps in rehabilitation after getting treated. During the pandemic, COVID 19 Yoga and Ayurveda played a major role in healing of patients physically, mentally and spiritually. Yoga and Ayurveda both act as preventive measures with lifestyle modifications.

Leveraging NLP for Real-Time Multilingual Translation and Content Moderation: The UniVox Framework
Authors:-Aditya Ajagekar, Balachandran Thandan, Devansh Ashar, Srinjan Chatterjee, Professor Omkar Ghag

Abstract-UniVox is a cutting-edge platform that enables communication across diverse languages, ensuring user safety and respectful interactions. It uses advanced natural language processing (NLP) technology to analyze and understand human language, providing real-time translation across multiple languages. The platform also employs sophisticated algorithms for profanity detection and content moderation, scanning conversations for harmful words and phrases before they reach the user. This proactive approach maintains a positive and respectful communication environment, making it an ideal choice for personal chats, professional meetings, and travel situations. UniVox breaks down language barriers and fosters understanding and respect among users. Its user-friendly interface makes it accessible to casual users and professionals. With its innovative features and commitment to safe communication, UniVox is paving the way for a more inclusive global community where everyone can share ideas and experiences without fear of misunderstanding or disrespect.

Cloud-Native Security: A Review of Modern Approaches
Authors:-Avantika Satish Pawar

Abstract-Cloud-native technologies like containers, micro services, and Kubernetes have transformed how applications are built and run. While they offer great speed, flexibility, and scalability, they also introduce new security challenges that traditional tools can’t fully address. This review paper explores the latest approaches in cloud-native security, including container security, service mesh, runtime protection, and DevSecOps practices. It highlights how modern security tools are being designed to work within dynamic and fast-changing cloud environments. The paper also discusses common threats, real-world examples, and current best practices used by organizations to secure their cloud-native applications. Overall, this review aims to provide a clear and updated understanding of how cloud-native systems are protected today and where future improvements are needed. More and more companies are moving to cloud-native systems, which means they use modern technologies like containers and microservices to build and manage their applications. While this makes things faster and more flexible, it also introduces new security challenges. This paper looks at the latest ways to keep cloud-native environments safe, including strategies like verifying identities (zero-trust security), controlling access, protecting running applications, securing containers, detecting threats automatically, and following security rules. It also examines the unique risks that come with cloud-native setups and highlights the newest security techniques and best practices to deal with them. The goal is to show how security is adapting to fight cyber threats while still allowing businesses to take advantage of cloud-native technology’s speed and efficiency.

Rural Road Pavement Design Using Construction Brick Waste
Authors:-Sainath Harish Mahale, Keshraj Shankar Bhadane, Assistant Professor Surekha S Thorat, Vishakha Yuvraj Pagare, Vijay Dnyaneshwar Ranpise

Abstract-As India’s population continues to rise, the need for reliable road infrastructure, particularly in rural regions, becomes increasingly important for the country’s development. Subgrade layers of roads are usually built using locally sourced soils, which may not possess adequate strength. To address this, mechanical stabilization is often employed as an economical and eco-friendly solution. This study explores the improvement of local soil strength by incorporating crushed brick waste in varying proportions (10%, 20%, 30%, and 40%). A series of laboratory tests—such as liquid limit, plastic limit, grain size distribution, compaction, and California Bearing Ratio (CBR)—were conducted to assess the impact of brick waste on the soil’s performance. The findings reveal that brick waste serves effectively as a filler material, enhancing the soil’s strength properties. This suggests its viability as a sustainable option for road construction. Moreover, when used in rural road subgrade design, this material contributes to a reduction in the total pavement thickness compared to traditional methods.

A Comprehensive Study on Extensions and Comparative Aspects of Fixed Point Theorems in Metric Space
Authors:-Abhinav Agrawal, Prince Kumar Namdev

Abstract-Fixed point theory is a central component of nonlinear analysis, with profound implications across numerous mathematical and applied fields, including differential equations, optimization, and computational modeling. Classical results, such as Banach’s and Kannan’s contraction principles, provide powerful tools for establishing the existence and uniqueness of fixed points. However, their restrictive assumptions, particularly regarding contractiveness and continuity, often limit their applicability in complex or real-world systems. This paper introduces a new fixed point theorem that unifies and generalizes the Banach and Kannan contraction conditions through a hybrid contraction framework. By relaxing traditional constraints, the proposed theorem significantly broadens the scope of mappings for which fixed point results can be obtained in complete metric spaces. The theoretical advancement is accompanied by a detailed comparative analysis with classical theorems, highlighting the improved generality and flexibility of our approach. To validate its practical relevance, the theorem is applied to a series of illustrative examples, along with discussions on its utility in areas such as economic modeling, machine learning algorithms, control theory, and numerical simulation. The study concludes by outlining directions for future research, including potential extensions to multivalued and generalized metric spaces. Overall, this work contributes to the expanding landscape of fixed point theory and its applications in modern mathematics.

A Novel IDS Framework Combining PCA and Random Forest
Authors:-Roshan Kumar S, Mageshwaran S D, Krithik S R, Dr. U. Surendar

Abstract-The aim of this project is to develop an application capable of identifying the type of attack on a system and detecting intruders using an intrusion detection system. [1] [6] Various machine learning techniques have been previously applied to IDS to enhance intrusion detection accuracy. This research introduces a novel approach by integrating principal component analysis and the random forest classification algorithm to build a more efficient IDS. PCA helps refine the dataset by reducing its dimensionality, simplifying data processing, while the random forest algorithm ensures precise classification of network activities. Experimental results indicate that this method outperforms traditional techniques such as support vector machine, naïve bayes and decision tree, delivering high accuracy. IDS functions as a network security measure, identifying threats, malicious activities, and attack types. A key limitation in conventional IDS is that if the primary detector fails, intrusions remain undetected. To address this, the proposed system implements multiple detection mechanisms, ensuring that if one detector fails, other continue monitoring and identifying threats, thereby, strengthening system security.

DOI: 10.61137/ijsret.vol.11.issue2.363

Fitness Club
Authors:-Nirjala Nandekar, Manasi Patil, Vinayak Patil, Chirag Patil, Professor Dr. Prachi Gadhire

Abstract-Being fit physically and mentally is every human being’s ultimate desire. People are always seeking to have a healthy body fitness and they are somehow engaged in day-to-day life. So, we believe that our application can solve this problem in android device users, the apps can be great relief to people who do not have time to visit fitness centre, through help users can manage the healthy life system. Many people who have realized the importance of these apps in their daily life have started making use of such apps. The Current Landscape of the Fitness Apps Market In 2023, the worldwide market for fitness apps clocked in at a solid 1.54 billion USD. Looking ahead, expect this sector to flex its muscles with an impressive compound annual growth rate of 17.7% from 2024 all the way through to 2030. It counts 87.4 million users in the US only. The fitness apps market has evolved dramatically, influenced by changing user preferences and technological advancements. It’s a domain where app users seek personalized experiences, driving the growth of both workout apps and nutrition and diet apps. User Engagement in Mobile Fitness Apps Understanding user engagement in mobile fitness app is crucial. This includes analysing patterns in the Google Play Store and Apple App Store, and how app features cater to diverse user needs. Monetization Strategies: From Paid Apps to In- App Purchases Monetization in the fitness app market varies from paid apps to dynamic in-app purchases, shaping the way developers create revenue streams.

E-Commerce Price Comparison System
Authors:-Assistant Professor Vimmi Malhotra, Kanchan Panwar, Jaya

DOI: 10.61137/ijsret.vol.11.issue2.364

AI Chatbot
Authors:-Vipashyna Arun Sable, Professor Suresh Mestry

Abstract-AI chatbots can assist right away by answering questions, providing explanations, and pointing to more resources. Software applications like chatbots can aid teachers in many assignments and become excellent digital teaching assistants.Chatbots are software applications that respond to inputs in natural language. We can see that chatbots are now a part of our daily life. We use them every day to book a movie, reach the closest restaurant, or find an open ATMThese are chatbot software applications that have made life easy. But their uses are not restricted to this. They also entertain users who are bored, play a massive role in home automation projects, give business strategy suggestions, and aid in many other ways.The system was tested on various user inputs and usage scenarios and satisfactory results were observed for usability, accuracy, and responsiveness. The project brings highly evolved chatbot framework technologies like Rasa and chit-chat bots of the likes of facebook and wit.ai to beginner level with a responsive conversational chatbot which could be deployed with minimal coding efforts.The project shows how advanced AI could be used effectively with simple web technologies, and opens up the possibility of further development of using voice, multilingual support, and AI-powered customization.

DOI: 10.61137/ijsret.vol.11.issue2.365

AI Chatbot
Authors:-Vipashyna Arun Sable, Professor Suresh Mestry

Lip-Interpretation Using Deep Learning and Cnn
Authors:-Asmita Chorge, Siddhi Dalvi, Sharvani Mahadik, Ashwini Pawar, Professor Manisha Hatkar

Abstract-Lip interpretation, also known as visual speech recognition, is a challenging task in the field of artificial intelligence (AI) and computer vision. This research explores how deep learning techniques, particularly Convolutional Neural Networks (CNNs), can enhance lip-reading accuracy. By analyzing different architectures, datasets, and methodologies, we present a comprehensive study on various models used for lip interpretation. The findings suggest that deep learning-based approaches significantly improve the accuracy of speech recognition without relying on audio data, thereby opening doors for applications in security, healthcare, and human-computer interaction.

AI-Driven Real-Time Threat Detection System for Women’s Safety Using Deep Learning and Gesture Analytics
Authors:-Associate Professor Dr.S. Mohana, Preethi S, Sujay Charan P, Parthiban S, Sanjai Krishnan A, Pradiksha R J

Abstract-This paper presents a comprehensive AI-driven real-time threat detection framework designed to enhance women’s safety in urban environments. The system integrates advanced computer vision techniques including YOLOv8- based person detection, ResNet-50 gender classification, and LSTM-based gesture recognition to analyze live surveillance feeds. Through the execution of a multi-modal threat analysis algorithm, the system can recognize vital scenarios including SOS gestures (with 92.3% accuracy), isolated female detection in night hours (with 89.7% accuracy), and probable mob scenario situations. The system is integrated with a distributed architecture supporting low- connectivity location-based edge computing, real-time alert generation based on Twilio/Vonage APIs, and compatibility with police systems through an exclusive web dashboard. Experimental outcomes prove 86.4% threat detection accuracy at an average latency of 1.2 seconds on NVIDIA Jetson devices.

DOI: 10.61137/ijsret.vol.11.issue2.366

Neuroplasticity in the Reels Era: Cognitive Consequences of Ultra-Short Form Video Consumption
Authors:-Era Mane, Aryan Patel

Abstract-Rapidly evolving technology continues to reap new increases even in the world’s most remote areas. Classical physics has been at the forefront of advances in medical, energy, wireless communications, computing, and artificial intelligence technol- ogy. Traditional communication systems using classical bits are reaching their limits. Quantum technology, especially quantum bits (qubits), is a game-changing alternative with advantages in security and efficiency. This study examines the basics of quantum communication on the underlying principles, purposes, and ways to process information. It introduces a model for quan- tum communication systems for which examples of applications are given and challenges encountered for implementation into practical use is indicated. The future of enhanced security and efficiency of digital networks and communications will derive from quantum advances.

Blood Cell Cancer Detection Using CNN Algorithm
Authors:-Ghanshyam Sharma, Harsh Mishra, Poovammal E.

Abstract-Blood cell cancer, particularly Acute Lymphoblastic Leukemia (ALL), requires timely diagnosis to improve patient outcomes. This study proposes a deep learning framework leveraging Convolutional Neural Networks (CNNs) to classify blood cell images into malignant and benign categories. Utilizing a dataset of 3,312 labeled blood cell images, we preprocess the data using augmentation techniques and train a CNN model with optimized hyperparameters. In this model achieves robust performance in distinguishing cancer subtypes, demonstrating the potential of AI-driven tools in hematological diagnostics. The methodology emphasizes reproducibility, with code structured in a Jupyter Notebook, and addresses challenges in medical image analysis.

An Analysis of the Relationship between Service Quality and Customer Satisfaction: Evidence from DUWASA in Bahi District, Tanzania
Authors:-Renalda M. Kapulula, Dr Castor Mfugale

Abstract-Reliable water service delivery is a cornerstone of customer satisfaction, particularly in developing regions where inconsistencies remain a persistent challenge. This study investigates the impact of water service reliability on customer satisfaction among beneficiaries of the Dodoma Urban Water Supply and Sanitation Authority (DUWASA) in Bahi District, Tanzania. Adopting a quantitative research design, data were collected from 129 respondents using a structured survey instrument. The study focused on key dimensions of service reliability, dependability, accuracy, consistency, and timeliness—and their influence on customer expectations, service experiences, and perceived value for money. Data analysis, conducted through simple linear regression, revealed a strong and statistically significant positive relationship between service reliability and customer satisfaction, with a correlation coefficient of 0.68. The coefficient of determination (R²) of 0.59 suggests that 59% of the variation in customer satisfaction is explained by the reliability of water services.

Preparation of Tradescantia Pallida Mediated Calcium Carbonate Nanoparticles and Their Activity against Hela Cell Lines
Authors:-Akshita Verma

Abstract-Purpose: To make calcium carbonate nanoparticles (CaCO₃NPs) and test their fluorescent and cytotoxic properties utilising Tradescantia pallida (Commelinaceae). Method: T. pallida aqueous leaf extract is used to make CaCO₃ NPs according to a straightforward technique (TPALE). Determine their crystalline nature and functional groups, Fourier transform infrared spectroscopy (FTIR) investigations are used. Photoluminescence characteristics of CaCO₃ Nanoparticles are evaluated using fluorescence spectroscopy. Following confirmation of CaCO₃ NP synthesis, the 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay is used to assess cytotoxicity against HeLa cell line. Conclusion: On the basis of TPALE, a simple method for synthesising CaCO₃ NPs has been successfully established. The generated CaCO₃ NPs have good luminescent characteristics and are cytotoxic to a cancer cell line.

Copyright in the Digital Age: Addressing Issues on Online Piracy and Streaming Services
Authors:-Soumya Mishra, Dr. Taru Mishra

Abstract-Copyright laws are being developed along with technology. Today, digital advances such as the Internet and PC offer both opportunities and challenges for creative work stakeholders. The balance of these interests is complicated but reflects the continued adaptation of copyright to new developments. The advent of the Internet and the widespread adoption of HR computers have begun an era of unprecedented connectivity and accessibility for creative work. However, in addition to these transformative developments, there are various challenges faced by stakeholders in the production, distribution, and consumption of copyrighted content. This analysis examines various digital copyrights in the Internet age and uses literature review methods that address the complex interactions of technological advancements and legal frameworks. The rise of digital copyright infringement, the spread of user-generated content platforms, and the development of new distribution models provide traditional ideas for copyright tracking and intellectual property protection. Furthermore, the global features of the digital economy complicate regulatory efforts as the terms of the legal framework make it difficult to meet the infinite characteristics of online transactions and consumption of digital content. This review examines development strategies used by political decision-makers, industrial interest groups, and law to control these challenges, from legislative reforms to innovation in content management and digital rights management (DRM) systems. Through a comprehensive analysis of existing literature, this overview uncovers ongoing dialogue related to digital copyrights on the Internet, providing insight into the complexity of copyright in the digital age, debate, and future trajectories.

DOI: 10.61137/ijsret.vol.11.issue2.367

AI-Driven Fraud Detection in Banking: A Comprehensive Approach Using Machine Learning and Data Science
Authors:-Mrs. Punashri Patil, Nidhi Warishe, Neha Sonar, Shantanu Wagh

Abstract-This paper presents a comprehensive exploration of artificial intelligence (AI)-driven methods for detecting fraud in the banking sector. As financial fraud grows in complexity and volume due to the rise in digital transactions, traditional rule-based systems have become inadequate. The study investigates various machine learning (ML) and deep learning (DL) algorithms for fraud detection, including supervised, unsupervised, and hybrid models. A hybrid system combining multiple models is proposed to enhance detection accuracy and adaptability. Our experiments demonstrate that neural networks achieve up to 96.1% accuracy, significantly outperforming traditional models. We also explore deployment strategies such as real-time analytics and explainable AI, offering actionable recommendations for banking institutions.

Intelligent Fault Management in Electric Power Transmission Lines Using Artificial Neural Networks
Authors:-Vikramsingh R. Parihar, Pearl H. Wardani, Aakansha P. Tiwari, Shreya R. Gangane, Soni N. Wasrani, Sushmita A. Meshram

Abstract-This paper focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

Bail Reckoner
Authors:-Kutala Pravalika, Korimi Praveen

Abstract-The subjectivity and complexity of bail determinations in criminal justice systems have for a long time presented difficulties to fairness and consistency. In this paper, we suggest a software solution named Bail Reckoner that helps assess bail eligibility with the use of machine learning methodologies. Through processing of historical court data and determining useful features such as offense category, previous offenses, and socio-economic factors, the system produces a probabilistic bail outcome suggestion. Our method is designed to assist judges and legal practitioners by increasing transparency and minimizing bias. The system is tested using simulated legal datasets and shows encouraging accuracy in matching with real judicial decisions.

DOI: 10.61137/ijsret.vol.11.issue2.368

Greenhouse Monitoring and Controlling Using Iot and Machine Learning
Authors:-Shreya Patwadkar, Harshada Shete, Nirmala Shelke, Snehal Sabale

Abstract-This study presents an ESP8266-based greenhouse monitoring and controlling system that effectively regulates environmental parameters essential for plant growth. The system utilizes sensors such as the DHT11 for temperature and humidity, YL69 for soil moisture, and an LDR for light intensity. These sensors provide real-time data to the ESP8266 microcontroller, which not only processes the inputs but also enables wireless connectivity for remote monitoring and control. Through relay modules, the system controls devices such as water pumps, artificial lighting, and ventilation fans to maintain optimal growing conditions. A display panel can be included for on-site visualization, while data transmission via Wi-Fi facilitates integration with cloud platforms for data logging and analytics. This IoT-enabled approach supports automation, enhances sustainability, and minimizes manual intervention, making it a robust solution for modern greenhouse management.

DOI: 10.61137/ijsret.vol.11.issue2.369

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