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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

Automated Student Attendance Using Computer Vision
Authors:-Reshi, Merinkanth, Yogeswari, Gayathri, HOD/IT 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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi

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

Published by: Kajal Tripathi