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Daily Archives: May 23, 2026

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Exploratory And Visual Analytics Of Mtcars Dataset Using Tableau Tool

Authors: Cherukupalli Harshitha, Darapu Saivenkat, Muvvapati Koushik, Mrs.K.Sireesha

Abstract: The mtcars11 dataset provides complete data about vehicle performance and their corresponding features. The dataset includes essential features which measure fuel efficiency through miles per gallon and provide engine specifications and horsepower and vehicle weight and transmission type and driving conditions. The information assists in examining trends associated with vehicle effectiveness, performance, and operational conduct. Through the use of data visualisation methods on this dataset, we seek to comprehend how elements such as weight, engine power, and transmission type affect fuel efficiency and overall performance. It also aids in recognising patterns under various driving circumstances like traffic and weather. The knowledge acquired can enhance decision-making in automotive evaluation, vehicle development, and performance improvement.

DOI: http://doi.org/10.5281/zenodo.20355470

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A Study On Ai-Driven Consumer Segmentation And Social Marketing Strategies For Sustainable Water Purification Businesses In Coimbatore City

Authors: Ms. Revathi G, Mr. Ashwath R

Abstract: The study adopts a descriptive research design and is based on both primary and secondary data. Primary data was collected from 150 respondents using a structured questionnaire, while secondary data was gathered from journals, articles, and online sources. The research focuses on identifying consumer segments, analyzing the impact of AI in understanding customer preferences, and evaluating the effectiveness of social marketing strategies in influencing consumer awareness and purchasing behavior. The study adopts a descriptive research design and is based on both primary and secondary data. Primary data was collected from 150 respondents using a structured questionnaire, while secondary data was gathered from journals, articles, and online sources.

DOI: https://doi.org/10.5281/zenodo.20354287

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A Study On The Socio-Economic Impact Of The Tamil Pudhalvan Scheme On Low-Income Families

Authors: Ms. Dr.B. Geethpriya, Mr. M. Balakumar

Abstract: This study examines the socio-economic impact of the Tamil Pudhalvan Scheme a state-funded educational welfare initiative introduced by the Government of Tamil Nadu on low-income families residing in the Coimbatore district. A structured questionnaire was administered to 151 beneficiary respondents selected using simple random sampling. Data were analyzed using percentage analysis and one-way Analysis of Variance (ANOVA) to test whether statistically significant differences exist in perceived financial relief, educational motivation, and dropout-prevention effectiveness across educational level groups (school, diploma, undergraduate, and postgraduate). ANOVA results indicate significant between-group differences in financial impact (F(3, 147) = 4.82, p = .003) and educational motivation (F(3, 147) = 3.61, p = .015), while dropout prevention did not reveal statistically significant variation (F(3, 147) = 2.14, p = .097). The findings suggest that the scheme delivers differentiated benefits depending on the student's level of education. Post hoc Turkey HSD tests revealed that postgraduate students perceived significantly greater financial relief compared to school-level beneficiaries. Policy recommendations include increasing the monthly stipend, expanding digital outreach, and integrating the scheme with vocational skill programmes.

DOI: https://doi.org/10.5281/zenodo.20354253

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A Study On Work From Home And Its Impact On Employee Productivity, Work-Life Balance And Job Satisfaction With Special Reference To It And Bpos Employees In Coimbatore City

Authors: Dr. Kowsalya G, Ms. Kowsalya

Abstract: The COVID-19 pandemic catalysed the most rapid and large-scale transition to remote work in human history, transforming work from home from a marginal flexibility benefit into the dominant mode of employment for millions of knowledge workers globally and in India. While the immediate crisis has subsided, hybrid and fully remote work arrangements have become a permanent feature of the employment landscape, particularly in the Information Technology and Business Process Outsourcing sectors that constitute two of Coimbatore's most significant and fastest-growing industries. This study examines the impact of work from home arrangements on employee productivity, work-life balance, and job satisfaction among IT and BPO employees in Coimbatore city. Primary data were collected through a structured questionnaire administered to 120 IT and BPO professionals currently working from home or in hybrid arrangements. Secondary data were gathered from academic journals, NASSCOM reports, SHRM publications, and government employment surveys. Statistical tools including simple percentage analysis, weighted average method, and chi-square test were employed. Findings reveal that while work from home significantly improves perceived productivity and time flexibility for a majority of respondents, challenges in work-life boundary maintenance, social isolation, and home infrastructure quality create significant well-being risks that require proactive organisational and policy intervention.

DOI: https://doi.org/10.5281/zenodo.20354225

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A Study On The Impact Of Financial Literacy On Financial Decision-Making Among College Students With Special Reference To Coimbatore District

Authors: Ms. Nandhini R, Mr. Mohan Kumar

Abstract: Financial literacy plays a significant role in enabling individuals to make informed and effective financial decisions in their daily lives. In today’s rapidly changing financial environment, college students are increasingly required to manage personal finances, including budgeting, saving, investing, and controlling expenses. However, many students lack adequate financial knowledge and awareness, which may lead to poor financial behaviour and long-term financial instability. This study aims to assess the level of financial literacy among college students and examine its influence on their financial decision-making behaviour. The research also focuses on identifying the major sources of financial information used by students and analysing the relationship between financial literacy and saving habits. The study highlights the importance of financial education in developing responsible financial behaviour among young adults. By identifying gaps in financial awareness, the research provides useful insights and recommendations for improving financial literacy programs and promoting better financial management practices among students for a financially secure future.

DOI: https://doi.org/10.5281/zenodo.20354178

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A Proximal Adaptive Momentum Algorithm with Variance Reduction for Nonconvex Composite Optimization: Convergence Analysis and Complexity Bounds

Authors: Dr.K.Srinivasan, Dr. M. K. Vediappan

Abstract: We propose and analyze the Proximal Adaptive Momentum with Variance Reduction (PAMVR) algorithm, a novel first-order method for solving nonconvex composite optimization problems of the form min F(x) = f(x) + g(x), where f is a smooth nonconvex function and g is a proper convex, lower-semicontinuous regularizer. PAMVR integrates three complementary mechanisms: (i) a momentum-corrected gradient estimator with adaptive step sizes, (ii) a periodic variance-reduction snapshot strategy inspired by SVRG, and (iii) a proximal operator for handling the nonsmooth component. Under standard Lipschitz-gradient and bounded-variance assumptions, we establish global convergence to an epsilon-approximate stationary point with a sample complexity of O(n + n^{2/3}/epsilon^2) stochastic gradient evaluations, matching the best-known bounds for this problem class while requiring weaker algorithmic assumptions than existing momentum-based methods. We further prove almost-sure convergence of the iterate sequence under a Kurdyka-Lojasiewicz (KL) regularity condition, obtaining explicit convergence rates depending on the KL exponent. The theoretical findings are validated on benchmark nonconvex problems including sparse logistic regression, matrix completion, and neural network training, demonstrating consistent improvements of 15–32% in convergence speed over PROX-SVRG, ProxGD-M, and Spider-Boost baselines. These results establish PAMVR as both a theoretically sound and practically competitive method for large-scale nonconvex optimization.

DOI: https://doi.org/10.5281/zenodo.20354108

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Smart Classroom and Digital Learning

Authors: Lalita Sonawane

Abstract: Smart classrooms and digital learning are changing education with the help of technology. Tools like smart boards, projectors, online classes, artificial intelligence, and virtual classrooms help students learn in an easy and interesting way. During the COVID-19 pandemic, online learning became very important because schools and colleges were closed. This research paper explains the meaning, benefits, challenges, and future of smart classrooms and digital learning. The information for this paper was collected from books, journals, websites, and research articles. The study shows that smart classrooms improve communication between teachers and students, increase student participation, and provide flexible learning opportunities. Students can study anytime and anywhere through digital platforms. However, there are also some problems like poor internet connection, high technology cost, lack of digital skills, and cyber security risks. The paper concludes that smart classrooms and digital learning are important for the future of education and need proper support, training, and infrastructure.

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Hybrid Generative Artificial Intelligence and Quantum-Mechanical Screening for Accelerated Drug Lead Optimization

Authors: Prof. R. Raveendhra

Abstract: Artificial intelligence (AI) is transforming pharmaceutical research by enabling rapid molecular prediction, virtual screening, and biological data integration. However, many current AI systems lack energetic realism and mechanistic interpretability. This manuscript presents a conceptual framework termed Adaptive Quantum-Generative Optimization (AQGO), integrating generative AI, molecular transformers, quantum-mechanical screening, molecular docking, and expert pharmacological validation. The framework is designed to improve lead optimization by combining data-driven molecular generation with physics-based energetic evaluation. The article reviews current advances in AI-driven drug discovery, the role of quantum chemistry in molecular simulation, translational challenges, and future directions for hybrid AI–quantum systems. Emphasis is placed on explainability, reproducibility, ethical deployment, and scientific transparency. The proposed architecture highlights the potential of combining generative intelligence with quantum-mechanical validation to support more efficient and reliable pharmaceutical discovery pipelines.

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Incentive-Driven Social Media Usage Regulation System

Authors: Govardhan Jadhav, Anand Ahire, Hitesh Kalal, Rishi Mishra, Prof.S.R.Agrwal

Abstract: Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.

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AgriHub: An AI-Powered End-to-End Agricultural Decision Support Platform

Authors: Govardhan Jadhav, Anand Ahire, Hitesh Kalal, Rishi Mishra, Prof.S.R.Agrwal

Abstract: Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.

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