Category Archives: Uncategorized

Authors: Prof. Shyam Prasad H R, Rakshitha V, Rohan K M, Naveen S, Supriya R S

Abstract: The increasing development of urban areas and architectural requirements often lead to the construction of multi- storey reinforced concrete (RC) structures that do not comply with the symmetric and uniform structure that was assumed in classical seismic design theory.Multi-storey reinforced concrete (RC) structures are commonly built with an eccentric and non- uniform structure due to urban developments and architectural requirements, which do not match the symmetric and uniform structure used in classic seismic design theory. In this study, the behaviour of a G+7 RC building with an L-shaped plan irregularity and soft-storey vertical irregularity is studied under three structural configurations: Bare Frame, Infill Wall Frame and Shear Wall Frame and modeled in ETABS. The response spectrum analysis has been done according to IS 1893 (Part 1):2016, gravity loads as per IS 875 (Parts 1-3):1987, RC design as per IS 456:2000, ductile detailing as per IS 13920:2016. These values of storey drift ratio, lateral stiffness, storey shear and storey displacement were extracted and compared. The Shear Wall structure reduced the roof level lateral displacement by ~99.9% and storey drift by ~99.8–99.9% compared to the Bare Frame, and provided around five times the stiffness. The displacement reduction for the Infill Wall model ranged from intermediate (62–78%). A clear performance hierarchy was generated: Shear Wall > Infill Wall > Bare Frame, with shear wall being the critical member in an irregular building in high seismic zone construction, to ensure safety.

DOI: http://doi.org/10.5281/zenodo.20375845

Published by: vikaspatanker

Authors: Prof. Dr H R Divakar, Kavana S, K P Renuka Prasad, Manoj Kumar S R, Lipika K

Abstract: The traditional attendance system used in educational institutions is often time-consuming, error-prone, and vulnerable to proxy attendance. Existing hardware-based attendance solutions such as RFID systems require additional infrastructure, maintenance cost, and dedicated devices, making them less flexible and expensive for large-scale deployment. To overcome these limitations, this paper presents a Biometric Smart Attendance System that combines biometric verification, AI-based face verification, and GPS location validation to ensure secure, accurate, and reliable attendance management without using dedicated hardware components. The proposed system uses multi-factor authentication to verify student identity before marking attendance. Face verification technology identifies and authenticates students, while biometric authentication provides an additional layer of security. GPS-based location verification ensures that attendance can only be marked when the student is physically present within the authorized classroom premises. The system is developed using React, TypeScript, FastAPI, Python, DeepFace, OpenCV, and Supabase. Test results show face verification accuracy of 96% under normal lighting and overall attendance accuracy of 95%.

DOI: https://doi.org/10.5281/zenodo.20374000

Published by: vikaspatanker

Authors: Dr. M. C. Padma, Bhoomika M, Faika Mehvish, Praveen Kumar R

Abstract: The rapid proliferation of deepfake videos—synthesised using Generative Adversarial Networks (GANs) and allied deep-learning techniques—poses grave risks to societal trust, democratic processes, and personal privacy. Existing detection approaches predominantly rely on frame-level spatial analysis and consequently fail to capture temporal inconsistencies that arise in manipulated sequences. This paper presents Deep Shield, a hybrid deep-learning framework that couples a ResNeXt convolutional neural network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) recurrent network for temporal sequence modelling. Each video frame is first preprocessed via face detection and alignment, after which ResNeXt encodes per-frame spatial embeddings that are subsequently fed into the LSTM to capture inter-frame inconsistencies. A fully connected classifier then labels the video as Real or Fake alongside a confidence score. The system is validated on three benchmark datasets—FaceForensics++, DFDC, and Celeb-DF—achieving detection accuracy exceeding 99 % together with precision, recall, and F1-score values above 99 %. The framework is wrapped in a Django-based web interface that allows nontechnical users to upload videos and obtain results in near real time. Robustness testing under compression artefacts, low-light conditions, and adversarial inputs confirms the generalisability of the approach.

DOI: http://doi.org/10.5281/zenodo.20375755

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

Published by: vikaspatanker