Daily Archives: May 24, 2025

Authors: Assistant Professor Sourabh Jain, Prachi Gupta, Manish Yadav, Pooja Srivastava

Abstract: In this day and age when we need to manage resources prudently, accurately predicting how much energy one would require is an extremely important task. In this abstract, we showcase how the application of advanced technologies – machine learning, data mining, and artificial intelligence techniques can be blended with energy management systems to enhance the efficiency of forecasting energy consumption rates. The sample we use in this research contains a wide array of data, including casual and seasonal weather data, time, building occupancy figures, as well as the figures attained for energy consumption during the various time slices. Some of the various approaches to solve the problem we are working on that we analyze include: linear regression, decision tree regression, random forest regression, and artificial neural networks. It is vital to accurately predict future power consumption considering factors like resource optimization and sustainable energy management. This work describes an approach that uses advanced methodologies in machine learning techniques for precise forecasting based on historical data alongside a variety of descriptive features to predict energy consumption within the foreseeable future. In this research, we use an extensive dataset containing weather data, timestamps, occupancy statistics, and previous energy consumption data. We apply many algorithms that include linear regression, decision trees, random forests, and neural networks to energy consumption prediction and analyze which model best performs the prediction task.

Published by: vikaspatanker

Authors: Assistant Professor Mr. Pramodh H K, Chandan M J, Mithun Gowda H C, Lokesh T S, Samskruthi A Y

Abstract: This article presents a comprehensive overview and proposes a system design that integrates wireless power transfer with an automated electric vehicle (EV) platform for real-time voltage monitoring and mobility. Utilizing inductive coupling technology, the system transmits power wirelessly from a stationary transmitter coil to a mobile receiver coil mounted on the EV prototype. A voltage sensor, in conjunction with an ESP8266 microcontroller, measures the received voltage, which is displayed on an LCD screen for user feedback. A motor driver and DC motors allow the vehicle to move, demonstrating the system’s ability to function while wirelessly charging. This approach aims to improve efficiency in EV charging infrastructure by minimizing manual intervention and enabling autonomous, wireless power reception. The article discusses both existing charging systems and the implementation of the proposed prototype.

DOI: 10.61137/ijsret.vol.11.issue3.115

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Authors: Viraj P. Tathavadekar

Abstract: This study investigates the use of generative AI and human-centered design for sustainable solutions to the ever-more pervasive problems within software development processes with a particular view to improving cross-functional collaboration. The modeling of modern Software Development Life Cycles (SDLCs) is further complicated by such things as vague requirements, continuous changes, and integration problems, all of which delay projects and increase their cost. The enormous integration gaps that the present study identifies are connecting AI-driven technologies with human-centered design practices, especially in creating collaboration among varied teams but ensuring technology sustainability. Research objectives consist of studying generative AI's role in enhancing requirements gathering, design processes, and further automated intelligent decision-making in testing, application deployment, and maintenance. It also aims at understanding the challenges key stakeholders confront across different SDLC phases. This is being done through a mixed-methods research approach combining quantitative data on AI tool effectiveness: reducing technical debt and increasing efficiency in teams, with correspondent qualitative insights from industry case studies. The major outcomes indicate that AI-driven tools do not just improve the efficiency of processes, but are also conducive to sustainable development practice as they reduce resource consumption, promote better collaboration. Implications are that generative AI and human-centered design can transform SDLC practices leading to higher-quality products and much lower maintenance costs as well as overall sustainability in software development projects.

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Authors: Goswami Jaygiri

Abstract: With the recent explosion of technology, cybersecurity is now a necessity to protect sensitive data, critical systems, and individual privacy. This review paper examines the existing state of cybersecurity, detailing some of the principal threats and countermeasures, related problems, and future paths. We particularly concentrate on AI, zero-trust architecture, blockchain, and quantum-resilient cryptography from a security viewpoint. We also touch on human factors, governance, and other cyber risk avoidance measures that deserve more research. This article provides a comprehensive overview of present and future directions in cybersecurity while assisting scholars, decision makers, and practitioners of cybersecurity.

DOI: 10.61137/ijsret.vol.11.issue3.116

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Authors: Dinnesh Gr, Manoj Jai Sudhan, Mrs. A. Jeyanthi, Mrs.G.Priyaa Sri

Abstract: Stroke is a major global health challenge, contributing significantly to mortality and disability, and placing a heavy burden on healthcare systems. Timely and accurate diagnosis is critical to mitigate long-term complications and improve patient outcomes. This study introduces a hybrid deep learning framework for automated stroke detection in brain CT images, integrating Vision Transformer (ViT), LASSO regression, and DenseNet121 to enhance diagnostic accuracy and efficiency. Utilizing a Kaggle dataset of 1900 CT images (950 stroke, 950 normal), the system employs preprocessing techniques, including resizing to 224×224 pixels, grayscale-to-RGB conversion, and data augmentation (flipping, rotation, blurring), to ensure model robustness and adaptability. The ViT model extracts high-level semantic features, capturing global dependencies through self-attention mechanisms, which are then refined using LASSO regression for feature selection to reduce dimensionality and prevent overfitting. The refined features are fed into DenseNet121, a convolutional neural network optimized for efficient parameter usage and gradient flow, for binary classification (stroke vs. normal). A Tkinter-based graphical user interface facilitates seamless interaction, allowing radiologists to upload images and receive real-time predictions, enhancing clinical workflows. The system is designed for scalability, local deployment, and integration with hospital systems like PACS, addressing challenges of diagnostic delays and inter-observer variability. Evaluation on the dataset demonstrates robust performance, with an accuracy of 92.69%, precision of 91.36%, recall of 94.03%, and F1-score of 92.68%. These metrics underscore the system’s reliability in minimizing false negatives, critical for clinical applications. This framework advances automated stroke diagnosis by combining transformer and convolutional architectures, offering a scalable, interpretable solution for emergency settings and laying the groundwork for future enhancements in multi-class stroke classification and real-time deployment.

 

 

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Authors: P.K.C.Ashara Rathnasinghe

Abstract: This document derives an analysis of factors influencing on digital banking adaptation by senior citizen in public license commercial banks in Colombo district and to know the dimensions driven by solutions for digital banking adaptation and the associated value proposition for senior citizen customers. The convenient function of digital banking has replaced interactions with physical money and reduced transaction time, better meeting the convenience needs in modern society life styles with the technological development. As digital banking concept plays an important role in day-to-day functions, understanding the factors which attracting consumers of senior citizen category by their age to use digital banking method will bring more opportunities for development, and further significantly improve the output in convenient way. This study discusses how to further influence the factors of digital banking adaptation by senior citizens who use public license commercial banks in Colombo district. This is based on the main theoretical framework of the selected 5 factors from several factors. In this study, data analysis is implemented by for the purpose of verifying the research model and hypotheses. The research results show that factors such as awareness of the service, lack of knowledge / training, cost of service, online security, perceived ease of use have selected as independent variables influence on senior citizens (age above 60 years) to adapt to use digital banking concept for their financial transactions. Three hundred and eighty-five number of samples will plan to select to the study and sample was consisted of random sampling technique. Statistical analysis and Regression analysis going to be used to confirm the impact of these five factors on digital bank adaptation.

Published by: vikaspatanker

Authors: P.K.C.Ashara Rathnasinghe

Abstract: This document derives an analysis of factors influencing on digital banking adaptation by senior citizen in public license commercial banks in Colombo district and to know the dimensions driven by solutions for digital banking adaptation and the associated value proposition for senior citizen customers. The convenient function of digital banking has replaced interactions with physical money and reduced transaction time, better meeting the convenience needs in modern society life styles with the technological development. As digital banking concept plays an important role in day-to-day functions, understanding the factors which attracting consumers of senior citizen category by their age to use digital banking method will bring more opportunities for development, and further significantly improve the output in convenient way. This study discusses how to further influence the factors of digital banking adaptation by senior citizens who use public license commercial banks in Colombo district. This is based on the main theoretical framework of the selected 5 factors from several factors. In this study, data analysis is implemented by for the purpose of verifying the research model and hypotheses. The research results show that factors such as awareness of the service, lack of knowledge / training, cost of service, online security, perceived ease of use have selected as independent variables influence on senior citizens (age above 60 years) to adapt to use digital banking concept for their financial transactions. Three hundred and eighty-five number of samples will plan to select to the study and sample was consisted of random sampling technique. Statistical analysis and Regression analysis going to be used to confirm the impact of these five factors on digital bank adaptation.

Published by: vikaspatanker

Authors: Jamila J, M. Sathya, R. Priyadharshini, J. Jenshya, V. Vinothini

Abstract: Urban areas face growing challenges in managing parking efficiently due to increased vehicle density. This paper proposes a real-time parking occupancy detection system using OpenCV, a powerful open- source computer vision library. By analyzing live video feeds from strategically positioned cameras, the system detects, classifies, and tracks vehicles using advanced object detection techniques, such as YOLO and SSD. This enables continuous monitoring of parking spaces and accurate assessment of their occupancy status. The system operates in three core stages: vehicle detection, classification of parked or moving vehicles, and real-time tracking using algorithms like Kalman filters or optical flow. Occupancy data is dynamically updated and shared via user-friendly interfaces such as mobile apps or digital displays, helping drivers find available spots efficiently. The system is developed using Python and OpenCV to ensure flexibility and ease of deployment across different parking environments. Performance evaluation was carried out using real-world datasets under various lighting and environmental conditions, demonstrating high accuracy and responsiveness. The proposed solution is scalable, adaptable to various camera setups, and suitable for deployment in street parking, garages, and smart city infrastructure. By improving parking space utilization and reducing the time spent searching for parking, this system contributes to easing traffic congestion, reducing fuel consumption, and enhancing the urban driving experience. With potential features such as safety compliance monitoring and modular architecture, the proposed system represents a significant step toward intelligent and efficient parking management in modern cities.

 

 

Published by: vikaspatanker