Daily Archives: March 11, 2026

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:

 

 

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

Authors: Bandi Sai Sathwik, Dhanvanth Rahul Nayak, Masham Sanjay, Pagadala Anurag Kubera

Abstract: The use of digital tools in education exams has brought up new issues like keeping exams secure, fair, and honest. Traditional systems where everything is controlled from one place are easy targets for problems like leaking exam papers, letting in the wrong people, fake identities, and changing results. This paper introduces a new platform for exams that uses blockchain, deep learning, and biometric methods to solve these problems. Blockchain helps keep exam papers safe from changes, manages exam data without a single point of failure, and makes the exam process open and clear through smart contracts. The system also uses deep learning to create exam papers, watch over exams, and grade them. Biometric checks are used to stop people from pretending to be someone else or getting in without permission. Testing shows this system works well in removing single points of failure and reducing the need for human help. It is a secure and reliable way to handle digital exams in education.

 

 

Published by: vikaspatanker

Authors: N.Akshaya Reddy, G.Bala Ruthik Raja Reddy, K.Nithya Sri, Shaik Inthiyaz

Abstract: This research introduces a Blockchain-Based Water Management System designed to boost transparency, efficiency, and trust in how water is distributed and monitored. The system uses IoT-based water sensors to gather real-time information on how much water is used, how much is flowing, and whether there are leaks. This data is securely stored on a blockchain network. Smart contracts are used to automatically track water use, handle billing, and control access, making sure the data can't be changed or tampered with. A decentralized ledger means we don’t rely on a single authority, which stops people from altering data—this ensures a fair share of water for everyone involved. A web-based dashboard gives authorities and consumers real-time data, helping them make better decisions and save water. Testing shows data is sent securely, transactions are validated reliably, and there's more transparency than traditional systems. This system has strong potential for managing water resources sustainably in smart cities and rural areas.

Published by: vikaspatanker

Authors: Ganga Sruthi Sai, V. James Prabhakar, Leela Venkat Sai, M. Prasad

Abstract: The quick increase in schools and big exams has made grading papers by hand more difficult. Old ways of grading depend a lot on people, which makes the process slow, not always fair, and can be affected by things like tiredness or personal opinions. While machines work well for multiple-choice questions, grading longer, written answers is still hard because understanding language isn't easy for computers. This paper suggests a smart, automated system that uses AI, OCR, NLP, and machine learning. It turns handwritten or printed tests into text that computers can read, checks multiple-choice answers by matching them to the correct answers, and evaluates written responses by looking at how similar they are to the right answers using machine learning. The system also uses explainable AI to make sure the grading is clear and fair. Tests show that this system saves time, makes grading more consistent, and is as accurate as humans. It offers a better, fairer, and more efficient way to grade exams for the future.

DOI:

 

Published by: vikaspatanker

Authors: Madhu Rani

Abstract: The rapid increase in global energy demand caused by population growth, industrialization, and technological advancement has intensified the exploitation of fossil fuel resources such as coal, oil, and natural gas. These conventional energy sources contribute significantly to environmental degradation, including air pollution and climate change. Consequently, renewable energy sources have gained considerable attention as sustainable alternatives. Wind power is one of the most widely adopted renewable energy technologies due to its ability to generate electricity without emitting greenhouse gases during operation. However, despite its environmental advantages, wind energy development also presents certain environmental and social challenges. This research paper examines the environmental benefits of wind power, explores its ecological impacts, and analyzes its social implications. The study highlights both the positive and negative aspects of wind energy and emphasizes the importance of careful planning, environmental assessments, and community engagement to ensure sustainable wind energy development.

 

 

Published by: vikaspatanker