Daily Archives: April 16, 2026

Authors: Kritika Kumari Ojha

 

Abstract: Phishing remains a critical cybersecurity threat, as traditional blacklist-based systems struggle against rapidly evolving domains and zero-day attacks. While machine learning (ML) has emerged as an adaptive solution for detection, the ultimate defense lies in re-engineering the authentication handshake itself. This paper explores the transition from "pass/fail" binary assertions toward a richer, contextual verification ecosystem powered by FIDO (Fast Identity Online) standards. We analyze how WebAuthn and CTAP2 shift the paradigm from possession-based secrets to high-assurance, phishing-resistant identity verification.

DOI:

 

Published by: vikaspatanker

Authors: A Banu, Associate Professor Dr. T. M. Hemalatha

Abstract: Social media marketing has emerged as a powerful tool in influencing consumer behavior and driving the success of e-commerce businesses. With the rapid growth of digital platforms such as Instagram, Facebook, YouTube, and WhatsApp, online retailers increasingly rely on social media to attract, engage, and retain customers. This study aims to analyze the role of social media marketing in shaping e-commerce success by examining consumer awareness, purchasing behavior, engagement levels, and perceived effectiveness of social media campaigns. Primary data were collected through a structured questionnaire from 200 respondents actively involved in online shopping. Statistical tools such as Percentage Analysis, Correlation, Chi-Square Test, and One-Way ANOVA were applied to analyze the data. The findings reveal a significant relationship between social media marketing strategies and e-commerce performance, indicating that social media plays a crucial role in enhancing brand visibility, customer trust, and sales growth.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Mohamed Issam S, Associate Professor Dr. T. M. Hemalatha

Abstract: Artificial Intelligence (AI) is a key driver of modern business transformation, especially in corporate environments where data-driven decision-making remains complex. AI-driven tools play a significant role in bridging this gap by providing scalable predictive analytics, automated reporting, and risk assessment to businesses. This study examines the role of Artificial Intelligence in enhancing business decision-making. The research focuses on understanding the accessibility, efficiency, and effectiveness of AI services in improving the operational and strategic outcomes of businesses. Using a descriptive research design, data was collected through a structured questionnaire and supported by secondary sources. The findings indicate that AI has contributed positively to decision-making by enhancing data processing speed, encouraging proactive strategies, supporting revenue-generating activities, and reducing dependence on manual heuristics. The study highlights the importance of strengthening AI integration practices to achieve sustainable business growth.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Rohit singh, Vikas pal, Minal suthar, Priti tangadi

Abstract: Agriculture forms the backbone of the Indian economy, yet smallholder farmers continue to face critical challenges including crop failure, rampant plant disease, unpredictable weather, and limited access to expert advisory services. This paper presents Smart-Kheti, a web-based AI-powered smart agriculture platform designed to democratize data-driven decision support for farmers. The proposed system integrates a personalized crop recommendation engine utilizing soil nutrient parameters (N, P, K), pH, temperature, humidity, and rainfall processed through an XGBoost-based multi-class classifier; an automated plant disease detection module employing a Convolutional Neural Network (CNN) trained on the PlantVillage dataset and deployed via TensorFlow Lite for server-side inference and TensorFlow.js for offline client-side inference; and a yield prediction module utilizing XGBoost regression on multi-year historical agricultural data. The platform employs a full- stack architecture with React.js and TypeScript on the frontend and Python FastAPI on the backend, containerized using Docker for scalable deployment. Additional features include a profit calculator, real-time market insights from government data APIs, offline support, and multilingual accessibility. Experimental evaluation demonstrates crop recommendation accuracy of 97.4%, disease detection accuracy of 93.7%, and yield prediction RZ of 0.87.

Published by: vikaspatanker

Authors: Mr. Albino Albert Raj Jenobe, Associate Professor Dr. T. M. Hemalatha

Abstract: Microfinance has become a significant financial instrument for fostering entrepreneurship within economically disadvantaged communities, especially among women. Women frequently encounter obstacles such as insufficient access to credit, limited financial knowledge, and societal constraints that hinder their full engagement in entrepreneurial endeavors. Microfinance institutions (MFIs) tackle these issues by offering small loans, savings options, insurance, and financial assistance to those who are marginalized by conventional banking systems. This study examines the role of microfinance in promoting entrepreneurship among women and its impact on economic empowerment and social status. The research adopts a descriptive research design and collects data through structured questionnaires from women beneficiaries of microfinance services. Secondary data sources such as journals, reports, and research publications are also used to support the study. The findings reveal that microfinance services have significantly contributed to the development of women entrepreneurs by enabling them to establish small businesses, improve income levels, and achieve financial independence. The study also highlights the challenges faced by women entrepreneurs and suggests measures to strengthen microfinance programs for sustainable economic development.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Mr. Deva Dorin DR, Associate Professor Dr. T. M. Hemalatha

Abstract: The Goods and Services Tax (GST) is one of the most significant tax reforms introduced in India to simplify the indirect taxation system. It was implemented on 1 July 2017 with the objective of replacing multiple indirect taxes such as Value Added Tax (VAT), Service Tax, Excise Duty, and others into a single unified tax structure. The main purpose of GST is to create a common national market, reduce tax cascading, and improve transparency in the taxation system. This study focuses on the implementation of GST and its impact on businesses, consumers, and the overall economy. The research examines the advantages, challenges, and effectiveness of GST after its implementation. It also analyzes how GST has simplified tax compliance, improved tax collection, and influenced the pricing of goods and services.The study is based on secondary data collected from journals, government reports, articles, and online sources. The findings indicate that GST has brought significant changes to the Indian tax structure by promoting transparency and reducing tax complexities, although certain challenges such as compliance issues and technical difficulties were faced during the initial stages of implementation. Overall, the implementation of GST has contributed to strengthening India's taxation framework and supporting economic growth by creating a more efficient and unified tax system.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Utsav R. Hirapra

Abstract: As the scope of software projects increases, it becomes increasingly difficult to ensure proper code quality and conduct efficient code reviews, not because the required tools lack, but because all relevant information tends to become buried under unnecessary noise. In an attempt to combat that issue, CodeFox is presented as a lightweight, modular solution for discovering valuable insights by leveraging modern AI algorithms. In terms of functionality, CodeFox consists of a developer-friendly user interface, various automated review tools, and a meaning- based code search module. The platform collects metadata such as commits, reviews, comments, as well as ownership information and uses semantic embeddings to index critical elements within the code. By leveraging vector search algorithm, CodeFox allows its users to easily discover similar code, discussion threads related to the code, as well as reviewers who were working on this code. The use of the platform at an early stage of development in our company allowed us to shorten the review cycle process as well as reveal the reasoning behind code changes more efficiently. In this paper, we discuss CodeFox architecture, key aspects of integration, namely webhooks, background workers, and persistent storage, as well as share our experience of implementing a convenient yet lightweight platform to facilitate further development. We plan to conduct a user study in order to evaluate efficiency in the context of the problem and implement more advanced data gathering features and intelligent suggestions in the future.

DOI: https://doi.org/10.5281/zenodo.19603725

Published by: vikaspatanker

Authors: Aayush Yogesh Sanklecha, Pranav Dattatray Gund, Aditya Yogesh Salunke, Samarth Pramod Koli, Prof Mr.H.B.Gadekar

Abstract: Data quality remains the critical bottleneck in enterprise machine learning pipelines. Unreliable, schema-broken, drifted, or regulatory non-compliant data causes downstream analytics failures with consequences ranging from inaccurate predictions to regulatory penalties. This paper presents ADAP (Automated Data Analytics Platform) (Data Intelligence Pipeline with Expert Verification), an end-to-end data intelligence platform that unifies multi-source data ingestion, NLP-augmented semantic schema classification, seven-dimensional parallel validation, regulatory compliance enforcement (AML, HIPAA, SOX, GDPR), AutoML with SHAP explainability, and a dual reinforcement learning (RL) adaptation engine — all within a single auditable medallion-architected system. ADAP (Automated Data Analytics Platform) achieves schema classification accuracy of 94.7% across 31 semantic types, anomaly detection AUROC of 0.961, calibrated confidence scoring with ECE 0.0225 and AUC 0.9784, and multivariate drift detection at 89.4% accuracy at moderate distributional shift. A PPO Actor-Critic agent pre-trained over 1,000 synthetic episodes and warm-started via Thompson Sampling adapts 8-axis pipeline execution strategies in real time. End-to-end pipeline latency is under 7.4 seconds for 100,000-row datasets. All six production models pass tightened v7 quality gates, with performance validated end-to-end on held-out enterprise datasets.

Published by: vikaspatanker

Authors: Atchaya K, Madhumitha T, Pasumarthini R, Siva Sandhiya M, Susmitha S

Abstract: The rapid growth of urbanization has resulted in increased vehicle density on roads, raising the demand for efficient and intelligent traffic monitoring systems. This paper presents a real-time vehicle detection, tracking, and recognition system using YOLOv26(ultralytics), the latest advancement in the You Only Look Once (YOLO) architecture. The proposed system leverages deep learning-based object detection to detect and classify vehicles from video streams captured by surveillance cameras. YOLOv26(ultralytics) offers improved accuracy and speed over its predecessors, making it highly suitable for real-time Intelligent Transportation System (ITS) applications. The system incorporates Deep SORT for robust multi-object tracking and supports recognition based on vehicle attributes including color, type, and license plate.

Published by: vikaspatanker