The Virtual Mouse Assistant is an innovative human-computer interaction system designed to provide an intuitive and hands-free alternative to traditional mouse devices. Leveraging advanced computer vision and gesture recognition technologies, the system enables users to control cursor movements, perform clicks, and execute complex commands using simple hand gestures. This approach enhances accessibility, particularly for individuals with physical disabilities, and offers a more ergonomic solution for prolonged computer use. The system integrates real-time image processing with machine learning algorithms to accurately detect and interpret hand movements, ensuring smooth and precise control. Additionally, it supports customizable gestures, allowing users to tailor commands according to their preferences and workflow requirements. Extensive testing demonstrates that the Virtual Mouse Assistant significantly reduces the dependency on physical input devices while maintaining high accuracy and responsiveness. Its applications extend beyond conventional computing, finding relevance in gaming, virtual reality, and remote operation environments. By bridging the gap between human intention and digital execution, the Virtual Mouse Assistant represents a step forward in creating more natural and efficient user interfaces. Future work focuses on enhancing gesture recognition in varying lighting conditions and expanding compatibility across multiple platforms.

Keywords : Virtual Mouse, Gesture Recognition, Human-Computer Interaction, Accessibility, Computer Vision

Authors : P J V G PRAKASA RAO

Title : Virtual Mouse Assistant

Volume/Issue : 2021;3(4 (August - September))

Page No : 22 - 25

The rapid and accurate detection of COVID-19 remains a crucial step in controlling its spread and providing timely medical care. This study proposes a Convolutional Neural Network (CNN)-based approach for the automatic detection of COVID-19 infection from chest X-ray (CXR) images. The model leverages deep learning techniques to extract discriminative features from CXR scans, distinguishing COVID-19 cases from normal and pneumonia-affected lungs. A preprocessed dataset consisting of labeled X-ray images was utilized for training, validation, and testing. Data augmentation techniques were applied to enhance model generalization and address class imbalance. The proposed CNN architecture achieved high accuracy, precision, and recall, demonstrating its reliability as a diagnostic support tool. The model’s performance was also compared with other deep learning architectures to evaluate its effectiveness. The results confirm that CNN-based methods can serve as a rapid, cost-effective, and non-invasive screening solution, assisting healthcare professionals in early COVID-19 diagnosis, particularly in resource-limited environments.

Keywords : Convolutional Neural Network (CNN), Chest X-ray, COVID-19 Detection, Deep Learning, Medical Imaging

Authors : Vinod Manikanta

Title : Analyze Chest X-Ray images to detect Covid-19 using CNN

Volume/Issue : 2021;3(4 (August - September))

Page No : 19 -21

With the rapid growth of online streaming platforms, providing personalized movie recommendations has become essential for enhancing user experience and engagement. This study focuses on developing a movie recommendation system using a collaborative filtering approach. The collaborative method relies on analyzing user preferences, viewing histories, and rating patterns to predict and suggest movies that align with a user’s interests. By leveraging user-item interaction matrices, the system identifies similarities among users or movies using techniques such as user-based and item-based collaborative filtering. The study also explores the integration of matrix factorization and similarity algorithms to improve accuracy and scalability. Experimental results demonstrate that the collaborative approach effectively captures user preferences and delivers relevant recommendations compared to traditional content-based methods. The proposed model contributes to improving recommendation accuracy, user satisfaction, and personalization in modern entertainment platforms.

Keywords : Collaborative filtering, Movie recommendation, Personalization, User similarity, Recommender systems

Authors : N P PATNAIK M

Title : Movie Recommendation through Collaborative approach

Volume/Issue : 2021;3(4 (August - September))

Page No : 15 - 18

Water is one of the most vital natural resources essential for agriculture, industry, and human survival. In India, where agriculture remains a primary livelihood source, efficient water conservation and irrigation management are critical for sustainable development. This study examines the current status of water resources, conservation methods, and irrigation facilities across different regions of the country. It highlights traditional and modern techniques such as rainwater harvesting, drip and sprinkler irrigation, watershed management, and the use of renewable energy in irrigation systems. The paper also discusses the challenges faced in water management, including uneven rainfall distribution, groundwater depletion, and inefficient irrigation practices. Government initiatives and policies aimed at improving water use efficiency and promoting sustainable irrigation are reviewed. The findings emphasize that integrated water resource management, technological advancements, and community participation are essential for ensuring long-term water security and agricultural productivity in India.

Keywords : Water conservation, Irrigation facilities, Sustainable agriculture, Water management, India

Authors : D. Vijay Kumar

Title : A Study on Water Conservation and Irrigation Facilities in India

Volume/Issue : 2021;3(4 (August - September))

Page No : 11 - 14

Wetlands are among the most productive and valuable ecosystems on Earth, providing a wide range of ecological, economic, and social benefits. They play a crucial role in maintaining biodiversity, regulating water cycles, controlling floods, improving groundwater recharge, and filtering pollutants from surface water. Despite their importance, wetlands are increasingly threatened by rapid urbanization, agricultural expansion, pollution, and climate change. The degradation and loss of wetlands have led to severe ecological imbalances and a decline in ecosystem services. This study focuses on the significance of wetlands, their types, ecological functions, and the urgent need for their conservation. Various conservation strategies, including sustainable management, restoration practices, community participation, and implementation of national and international policies such as the Ramsar Convention, are discussed. The research emphasizes that effective conservation of wetlands is vital for environmental sustainability, climate resilience, and the well-being of human societies.

Keywords : Wetlands, Biodiversity, Conservation, Ecosystem services, Sustainable management

Authors : D. Vijay Kumar

Title : Study on Significance and Conservation of the Wet Lands

Volume/Issue : 2021;3(4 (August - September))

Page No : 6 - 10

This paper presents the design and simulation of a 32-bit Parallel Self-Timed Adder (PASTA) architecture aimed at achieving high-speed and energy-efficient performance for Very Large Scale Integration (VLSI) circuits. The proposed architecture utilizes self-timed principles to eliminate the need for a global clock, thereby reducing power consumption and clock-skew issues common in synchronous systems. The 32-bit PASTA design incorporates modular carry propagation and parallel computation techniques to enhance speed while maintaining low latency and area efficiency. Simulation results, obtained using standard CMOS technology, demonstrate significant improvements in delay and power-delay product compared to conventional ripple carry and carry look-ahead adders. The architecture’s asynchronous operation also contributes to enhanced robustness under process, voltage, and temperature variations. The implementation shows that the 32-bit PASTA is a promising solution for high-performance arithmetic units in modern VLSI applications such as digital signal processing (DSP), microprocessors, and embedded systems.

Keywords : PASTA Architecture, Asynchronous Circuits, High-Speed VLSI, 32-Bit Adder, Low-Power Design

Authors : K. V. Ganesh

Title : Design and Simulation of a 32 Bit PASTA Architecture for High Speed VLSI Circuits

Volume/Issue : 2021;3(4 (August - September))

Page No : 1 - 5