Category Archives: Uncategorized

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.

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

Authors: Tombari Dubon, Hachimenum Nyebuchi Amadi, Onyebuchi Nelson Igbogidi, Richeal Chinaeche Ijeoma

Abstract: This study investigates the integration of a 5MW solar power system into the Elekahia Housing Estate grid to address challenges such as renewable energy intermittency, voltage instability, and transmission losses. A Particle Swarm Optimization technique was employed to optimally tune the Unified Power Flow Controller, while Flexible AC Transmission System devices were used to provide dynamic voltage regulation and impedance control. Energy storage systems were incorporated to mitigate renewable power fluctuations and support peak demand. Simulation results show that the inclusion of energy storage increases total grid output to a peak of 8.9MW, with storage contributing between 0.45MW and 1.8MW, thereby smoothing the demand curve and supporting peak loads between 18:00 and 21:00 hours. The State of Charge (SOC) analysis indicates effective battery management, with SOC rising to about 60% during off-peak hours and dropping to approximately 45% during high-demand periods. The integration of the 5MW solar generation further enhances system capacity, enabling the network to meet a demand of 7.9MW during evening peaks, compared to the original 4MW capacity. Voltage and current fluctuations observed in the absence of control devices were significantly reduced with the implementation of the optimized UPFC. The PSO-optimized UPFC demonstrated superior voltage regulation, reduced current peaks, and improved power flow stability compared to the conventional UPFC. Overall, the combined integration of renewable energy, energy storage, and advanced control technologies significantly enhances grid stability, operational efficiency, and reliability. The findings provide strong evidence that optimized FACTS control and energy storage systems can effectively support high-penetration solar power integration, reduce transmission losses, and improve voltage stability in urban distribution networks. The study recommends policy adoption and grid modernization strategies that incorporate PSO-optimized UPFC, energy storage systems, edge computing, and quantum-enhanced optimization to support sustainable and resilient renewable energy deployment.

DOI: https://doi.org/10.5281/zenodo.18950585

 

Published by: vikaspatanker

Authors: V. Latha Sivasankari, Pratheep Kumar V, Preethika G, Pravin B

Abstract: Online marketplaces increasingly suffer from deceptive product reviews that manipulate customer perception and distort purchasing decisions. Traditional rule-based and manual moderation approaches struggle to detect sophisticated opinion spam, especially as review volumes grow exponentially across e-commerce platforms. The proposed system, Fake Product Review Detection Using Machine Learning, introduces an automated text analytics pipeline for identifying deceptive reviews using supervised learning techniques. The system processes raw review text through data preprocessing stages including tokenization, stop-word removal, normalization, and stemming, followed by feature extraction using TF-IDF vectorization. Multiple classification algorithms such as Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) are evaluated to determine optimal performance. A trained model is integrated into a Flask-based web application that enables real-time review classification as Fake or Genuine. The system architecture ensures seamless interaction between preprocessing, feature engineering, model inference, and user interface components. Performance evaluation conducted on a labeled dataset demonstrates an accuracy of 85%, with balanced precision and recall values, confirming reliable detection capability. The modular Python-based implementation ensures scalability, maintainability, and ease of deployment on standard computing environments. This approach enhances trustworthiness in online review ecosystems by providing an efficient, intelligent, and automated fake review detection solution.

Published by: vikaspatanker