Daily Archives: December 25, 2025

Authors: Dr. Rachna Rana, Er. Gundeep Kaur, Er. Manpreet Kaur, Mr. Sachin Sharma

Abstract: Artificial Intelligence (AI) is reshaping educational systems worldwide through personalized learning, predictive analytics, intelligent tutoring systems, automation, and institutional decision-support technologies. AI applications in education have transitioned from experimental prototypes to widely adopted tools used for assessment, student support, curriculum design, and governance. This paper presents a comprehensive analysis of the current landscape of AI in education, with emphasis on machine learning (ML) frameworks, learning analytics (LA), natural language processing (NLP), and predictive analytics used for monitoring academic quality assurance (QA). The paper synthesizes findings from recent empirical and conceptual studies, discusses the system-level implications of AI-enabled educational data mining, and identifies ethical, pedagogical, and institutional challenges that influence adoption. A section is dedicated to the integration of AI-driven predictive models into QA processes, including early warning systems, risk-prediction algorithms, and data-driven continuous-improvement frameworks. The paper concludes with recommendations for responsible AI deployment, future research trajectories, and policy considerations.

DOI: http://doi.org/10.5281/zenodo.18081433

Published by: vikaspatanker

Authors: Vaibhav Sinha, Abhishek Kumar Singh, Dr. Partap Singh

Abstract: The integration of 5G technology and Artificial Intelligence (AI) marks a transformative phase in digital communications and intelligent connectivity. As 5G networks offer unprecedented speed, ultra-low latency, and massive device connectivity, AI brings the intelligence required to optimize and automate 5G systems. This research paper critically examines how AI empowers 5G networks, explores key applications, discusses challenges, and highlights future prospects across industries. With supporting pictorial references, the paper presents a comprehensive, humanized view suitable for academic and professional audiences.

DOI: http://doi.org/10.5281/zenodo.18053277

Published by: vikaspatanker

Authors: Satyaki Adak

Abstract: Distributed Machine Learning (DML) enables efficient training over massive datasets by distributing computation across multiple nodes; however, it also increases vulnerability to data poisoning attacks, where adversaries inject malicious or mislabeled data to corrupt the learning process. Ensuring model integrity in such environments is a critical security challenge. This project classifies DML systems into basic-DML and semi-DML based on whether the central server participates in dataset training. For the basic-DML scenario, a novel cross-learning–based data poisoning detection scheme is proposed, where training results from distributed workers are compared through multiple training loops to identify anomalous behaviour. A mathematical model is developed to determine the optimal number of training loops that maximizes detection accuracy while minimizing overhead. For the semi-DML scenario, an enhanced poison detection mechanism is introduced by leveraging the central server’s computing resources, along with an optimal resource allocation strategy to reduce unnecessary computation. Experimental results demonstrate that the proposed schemes significantly improve model accuracy—up to 20% for Support Vector Machines and 60% for Logistic Regression in basic-DML—while reducing wasted resources by 20–100% in semi-DML. The proposed framework offers a general, efficient, and scalable defence against data poisoning attacks in distributed learning environments.

DOI: http://doi.org/10.5281/zenodo.18051593

Published by: vikaspatanker