Authors: R. Saniya Paul, Livistone S P, Dr. S. Sheeja

Abstract: Drug discovery is inherently complex and expensive with many requisites in terms of precision with respect to interactions' molecular modelling, biological activity prediction and chemical compounds optimisation. Classical computing methods have contributed substantially to the progress of computational drug discovery via molecular simulations, machine learning models and high-throughput virtual screening. However, challenges emerge from the exponentiality of molecular configuration space and the low efficiency of classical algorithms. Quantum computing as a new computing paradigm that offers a novel approach to computation based on various phenomena such as superposition and entanglement and therefore offers a way to overcome the previous limitations. This work presents a comparison of classical and quantum computing approaches in drug discovery with emphasis on their strengths and weaknesses, current progress and future prospects in different aspects of pharmaceutical research.

DOI: https://doi.org/10.5281/zenodo.18979549

 

Published by: vikaspatanker

Authors: Pragadeeshwaran R, Mohanapriya D, Dr.S.Sheeja

Abstract: Conventional animal health management practices involve extensive manual observation and documentation, resulting in late disease detection and ineffective veterinary care, especially in rural areas. To fill this pressing need, this paper proposes a comprehensive AI-assisted web application for proactive animal health monitoring. The proposed system employs a strong three-tier architecture, combining a React.js front end, a Node.js API gateway, and Supabase for secure and real-time data management. The system is segmented into role-based portals for Farmers, Veterinarians, and Administrators, supporting bilingual functionality (English and Tamil) for broad grassroots reach. The key innovation here is the combination of two Artificial Intelligence components: a Convolutional Neural Network (CNN) for the quick diagnosis of dermatological and visible diseases from user-submitted images and a Natural Language Processing (NLP) engine that combines unrefined farmer observations into formatted clinical reports. By leveraging the digital recording of longitudinal vitality parameters such as temperature and food intake, along with AI-driven diagnoses, the proposed system enables precise remote veterinary diagnosis. This system greatly minimizes the time gap between disease manifestation and treatment, thus enhancing animal well-being, preventing economic losses for farmers, and optimizing the workflow of veterinary experts.

DOI: https://doi.org/10.5281/zenodo.18979262

Published by: vikaspatanker

Authors: Ms. Shristi Singh

Abstract: The increasing penetration of the internet and the widespread use of smartphones have transformed the way consumers obtain information and purchase products. In the modern digital environment, marketing activities have shifted from traditional methods to digital platforms such as social media, search engines, websites, and e-commerce portals. These platforms allow companies to communicate with customers quickly and effectively while promoting their products and services. With the expansion of digital connectivity in India, the influence of digital marketing is no longer limited to urban regions. Rural areas are also experiencing rapid digital adoption. Improved internet infrastructure and affordable smartphones have enabled rural consumers to access online information, compare products, and engage in digital transactions. The present study explores the satisfaction level of rural consumers toward digital marketing practices. The research relies on secondary data sources such as academic journals, books, research reports, and online publications related to rural marketing and digital consumer behaviour. The findings indicate that digital marketing enhances rural consumers’ access to product information, increases product availability, and offers convenient purchasing options. However, challenges such as limited digital literacy, weak network connectivity, and concerns about online security still influence consumer satisfaction in rural areas.

DOI: https://doi.org/10.5281/zenodo.18979257

 

Published by: vikaspatanker

Authors: Mr. Sahil Mahale, Mr. Shivam Gangurde, Mr. Pankaj Ahire, Mr. Harshal Kushare, Mr. A.S.Parkhe

Abstract: The increasing demand for electrical energy and the rapid depletion of conventional energy resources have made renewable energy sources an important alternative for sustainable power generation. This paper presents a hybrid renewable energy generation system that utilizes both solar energy and piezoelectric energy for electricity generation. Solar panels convert sunlight into electrical energy using photovoltaic cells, while piezoelectric sensors generate electrical energy when mechanical pressure such as human footsteps is applied. By combining these two energy sources, the efficiency and reliability of energy generation can be improved. In this system, solar energy is used as the primary source of power during daytime, while piezoelectric sensors generate electricity from mechanical pressure created by human movement in crowded areas. The electrical energy generated from both sources is stored in a rechargeable battery and can be used to power small electrical loads such as LED lighting systems, sensors, or low-power electronic devices. This hybrid renewable energy system can be implemented in public places such as railway stations, bus stands, shopping malls, and footpaths where human movement is frequent. The system helps in utilizing wasted mechanical energy and natural solar energy effectively. The proposed system contributes to energy conservation, reduces dependence on conventional power sources, and promotes the use of clean and sustainable energy technologies.

 

 

Published by: vikaspatanker

Authors: Sheeja S, Selvasurya S, G Surriya Vel

Abstract: The COVID-19 crisis reshaped healthcare systems across the world in ways never seen before. As hospitals struggled to manage rising infection rates, traditional face-to-face consultations quickly became risky. In response, healthcare providers rapidly turned to telemedicine as a safer and more practical alternative. What initially began as an emergency response soon demonstrated long-term value. Virtual healthcare services have since proven effective in expanding access, improving chronic disease management, reducing operational costs, and maintaining continuity of care. This paper examines how telemedicine evolved during the pandemic, the technologies that support it, the benefits and limitations it presents, and its growing importance in shaping the future of global healthcare delivery.

DOI: https://doi.org/10.5281/zenodo.19810045

Published by: vikaspatanker

Authors: Dr G Rama Subba Reddy, Vaddi Obulesu, Ajay Gujjari, pattupogula Lakshmikala, Vamshi Nalapalli

Abstract: Estimating and analyzing traffic patterns is essential for managing Quality of Service (QoS) metrics in cellular networks. Cellular network planners often employ various approaches to predict network traffic. However, existing algorithms rely on large datasets, leading to significant time complexity and resource demands. To address this issue, we introduce a novel algorithm, AML-CTP (Adaptive Machine Learning-based Cellular Traffic Prediction), which is trained on a small, accurate dataset to enhance prediction accuracy while reducing complexity. Our methodology includes data normalization using the Min-Max Scaler, feature selection via the Select-K-Best algorithm, and dimensionality reduction through PCA. We apply density-based clustering techniques (DBSCAN and Kernel Density) to identify high-similarity clusters for training. We evaluate several machine learning algorithms, including Support Vector Machine (SVM), Linear Regression, Decision Tree, Light Gradient Boosting, and XGBoost, using a Cellular LTE dataset from an Egyptian company. The results demonstrate that the Decision Tree algorithm achieved the highest R² score of 96%, followed by the extension XGBoost model, which reached a remarkable R² score of 98%, indicating its superior performance in cellular traffic prediction.

DOI:

 

 

Published by: vikaspatanker

Authors: Kaustubh Nitin Salunke, Vinayak Amol Shewale, Anurag Sanjay Shigwan, Omkar Vinod Tate

Abstract: The escalating demand for transparent, tamper-proof, and efficient electoral processes in educational institutions necessitates a modern digital alternative to conventional paper-based voting. This paper presents NEXTGEN: College Voting System, a secure, fully web-based election management platform designed specifically for college-level institutional elections. The system is architected on a three-tier client-server model employing Java Servlets and JavaServer Pages (JSP) for backend processing, HTML5/CSS3 with Bootstrap 5 for the frontend, MySQL 8.0+ as the relational database engine, Apache Tomcat 11 as the servlet container, and the Jakarta Mail API for OTP-based Two-Factor Authentication (2FA). The platform features two primary role-based modules: an Admin Module offering complete election lifecycle control including student registration management, candidate management, election activation/deactivation/reset, and real-time result monitoring; and a Student Module providing secure registration, OTP-verified login, position-wise vote casting, and OTP-based password recovery. Security is enforced through SHA-256 password hashing, session management, role-based access control, dual-layer duplicate vote prevention (application-layer logic and database UNIQUE constraints), and time-bound OTP verification (5-minute validity). Testing validated 100% vote-count accuracy, 100% duplicate vote rejection, and OTP delivery within 5–10 seconds. The system eliminates manual counting errors, drastically reduces administrative overhead, and enables instant, verifiable election results. Future directions include biometric authentication, blockchain-based vote immutability, SMS-OTP support, and cloud deployment.

 

 

Published by: vikaspatanker

Authors: Dr.S.Swetha, CH.Ramya

Abstract: Insects are among the most diverse and abundant organisms on Earth and play essential roles in ecosystem functioning. Due to their sensitivity to environmental changes, short life cycles, and wide ecological distribution, insects are increasingly recognized as effective bioindicators of environmental health. Changes in insect diversity, abundance, behavior, and community composition reflect alterations in habitat quality, pollution levels, climate change, and land-use practices. This review examines the role of insects as bioindicators, highlights major insect groups used in environmental monitoring, discusses methodologies and applications, and outlines current challenges and future perspectives in sustainable environmental assessment.

DOI: https://doi.org/10.5281/zenodo.18954970

Published by: vikaspatanker

Authors: Akkala Shivani Reddy, Janardhan Sreedharan, Veldi Karunakar, Erukali Shiva Kumar, Kommu Sony

Abstract: India's 600+ million social media users face unprecedented threats from sophisticated fake profiles and coordinated botnets that undermine platform integrity, spread disinformation, and influence elections. Traditional machine learning approaches relying on isolated account features fail to capture complex relational patterns and coordinated behaviors characteristic of modern botnets. This research proposes a novel Graph Neural Network (GNN) framework that models social networks as G=(V,E) graphs, where nodes represent user profiles with rich behavioral features and weighted edges capture interaction patterns. The architecture combines Graph Convolutional Networks (GCN) for neighborhood aggregation with Graph Attention Networks (GAT) for dynamic relationship weighting, enabling hierarchical feature learning across three GNN layers. Trained on combined TwiBot-22, Cresci-2015, and India-specific datasets, the model achieves state-of-the-art performance: 96.3% accuracy, 95.7% precision, 96.8% recall, and 96.2% F1-score, outperforming SVM (82.1%), Random Forest (85.3%), and other baselines by 11-18%. Key innovations include multi-scale graph embeddings capturing both individual account anomalies and bot cluster topologies, temporal interaction modeling, and real-time deployment as a scalable web application (<500ms inference/profile). Feature importance analysis reveals follower-following ratios, clustering coefficients, and posting variance as strongest discriminators. Successfully detecting a 47-account botnet with 95.7% recall, the framework addresses India's unique multilingual, high-density social ecosystem challenges. This GNN-based solution provides social media platforms with production-ready tools for maintaining authenticity, combating misinformation, and ensuring digital trust at national scale.

Published by: vikaspatanker

Authors: K.A.S.L.U. Maheswari, Gugulothu Mythili, Jitta Rithika Reddy, Kolipaka Vineeth Nihal

Abstract: This study introduces a Blockchain-Based Zero Trust Network Access (ZTNA) solution that is designed to solve security problems caused by the centralised design of cloud storage systems, like data leaks, unauthorised access, and reliance on third-party providers. It uses blockchain, specifically Ethereum, along with the Zero Trust approach of "never trust, always verify" to create a secure, transparent, and unchangeable access control system. Smart contracts written in Solidity automate authentication, permission checks, and access validation, while AES encryption ensures strong protection for sensitive information in the cloud. The system sorts files into public and private groups based on user roles, and all access requests, permission changes, and activity logs are permanently stored on the blockchain, making it easier to keep track of who did what and when. The system’s lack of central control reduces the risk of failures, increases dependability, and builds confidence among users. The system is meant to be scalable, work with mixed cloud setups, and could be linked to future security tools like advanced threat detection systems. In general, this solution offers a secure, checkable, and reliable platform for managing valuable digital assets in today’s environment.

Published by: vikaspatanker