Authors: P. Meiyazhagan, R. Harish Muthu, M.V. Kowshika, S. Mohammed Kaif

Abstract: The rapid proliferation of fake profiles across heterogeneous social media platforms presents a significant challenge to online security, misinformation control, and digital trust. Traditional machine learning models for fake profile detection often operate as black-box systems, making it difficult to interpret their decisions. To address this, we propose a novel Explainable AI (XAI)-driven framework that enhances transparency and accountability in fake profile identification. Our approach integrates ensemble machine learning models (Random Forest, XGBoost, and Support Vector Machines) with SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to provide interpretable feature importance insights. By analyzing user metadata, behavioral patterns, and social network interactions, our system detects fake profiles while justifying its predictions in a human-understandable manner. Furthermore, an interactive XAI dashboard enables users and platform moderators to visualize decision factors, improving trust and ethical AI adoption. Experimental results demonstrate high detection accuracy and explainability, making this framework a promising solution for combating fake identities across diverse social media ecosystems.

DOI:

 

Published by: vikaspatanker

Authors: Mr. Shantanu Gamre, Dr. Jasbir Kaur, Assistant Professor Mr. Suraj Kanal

Abstract: This research takes a close look at how different parts of the world are tackling cybersecurity, comparing the well-established approach of the European Union with the evolving systems in South Asia and Latin America. We found that there are still big gaps in global cybersecurity efforts. These gaps exist because cyber threats don't respect borders, countries have very different levels of readiness and resources, and cybersecurity laws often clash or aren't consistent worldwide. The European Union stands out with its strong, rights-focused legal framework, including key laws like GDPR and NIS2, and powerful agencies like ENISA. In contrast, South Asian countries are rapidly embracing digital technology but often struggle with outdated or inconsistent laws, political challenges, and a tricky balance between national security and individual online freedoms. Meanwhile, Latin American nations face advanced cybercrime and even attacks from other governments. While some, like Brazil with its LGPD, have made good progress in data protection, the region generally suffers from a shortage of skilled cybersecurity professionals and difficulties in putting plans into action. Ultimately, this research concludes that to truly make our digital world safer and more fair, we need a global shift towards shared responsibility, focused efforts to build up cybersecurity capabilities where they're weakest, and much stronger international cooperation.

DOI: https://doi.org/10.5281/zenodo.15773377

Published by: vikaspatanker

Authors: Aryan Bhatt, Aryan Verma, Syed Qayam

Abstract: Social media has transformed the way people consume news by providing affordable, readily available, and quick information communication. Unfortunately, it has also provided a breeding ground for fake news—intentionally misleading or untruthful information—that can have serious consequences for individuals and society. Identification of fake news on social media has therefore become an essential research area. This problem differs from conventional news media, since false news is deliberately designed to mislead and thus cannot be easily detected by content alone. To overcome this, auxiliary data, including user behavior and social interactions, are usually needed. But it is hard to make use of this information because it is large-scale, incomplete, unstructured, and noisy. Considering the significance and complexity of the issue, the paper discusses Artificial Intelligence (AI) in the detection of false news on social media. We present an overview of the fake news features according to psychological theory and social theory, discuss previous AI-based detection algorithms, as well as assessment metrics and corpora. Further, we stress major challenges, current research work, and the possible future areas of AI use in detecting fake news.

DOI:

 

Published by: vikaspatanker

Authors: Sambasiva Rao Madamanchi

Abstract: Disaster recovery (DR) planning for hybrid infrastructures that combine Solaris and Linux poses unique challenges due to differences in tooling, system architecture, and operational practices. Solaris often supports legacy, mission-critical applications, while Linux drives modern, scalable workloads. This document provides a comprehensive guide to building a resilient DR strategy across both platforms. Key areas include risk assessment, backup and recovery tooling, system state preservation, and application/database restoration. Emphasis is placed on automation through Ansible, shell, and Python scripts, as well as configuration management and monitoring integration. The guide also highlights best practices such as maintaining consistent time and user IDs, isolating recovery zones, and leveraging enterprise backup solutions. Through clear documentation, defined team roles, and routine testing, organizations can achieve a DR framework that is platform-aware, repeatable, and aligned with evolving operational and compliance requirements.

DOI: https://doi.org/10.5281/zenodo.15771601

 

Published by: vikaspatanker

Authors: Research Scholar Mitali Khandelwal, Dr. Kshmasheel Mishra

Abstract: Road traffic accidents are a leading cause of fatalities and injuries worldwide, with their severity influenced by numerous dynamic and interrelated factors. Predicting the severity of such accidents is critical for timely emergency response, infrastructure planning, and overall road safety enhancement. This study explores advanced machine learning (ML) techniques to accurately predict accident severity using diverse datasets comprising weather conditions, road characteristics, vehicle information, and driver behavior. A novel hybrid model is proposed, integrating classical pre-trained models with ensemble learning and deep neural networks to capture complex nonlinear relationships within the data. To address class imbalance a common issue in accident datasets, strategies such as data augmentation and cost-sensitive learning are incorporated. The proposed architecture undergoes rigorous performance evaluation using metrics like accuracy, precision, recall, and F1-score, demonstrating superior predictive capability compared to traditional models. Further, the study emphasizes model interpretability through tools like SHAP and LIME to enhance transparency and trust. By leveraging real-world traffic data and scalable ML infrastructure, the developed model offers a reliable solution for severity prediction, aiding emergency services and policy decisions. Ultimately, this research contributes to the reduction of road casualties and the advancement of intelligent transport systems.

Published by: vikaspatanker

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Published by: vikaspatanker

Authors: Joyshree Saikia. Assistant Professor

Abstract: The representation of North East India has been colonial and ethnographically reductive and there has been a gross misrepresentation of the heterogeneous traits of the region under the faulty homogeneous label of 'Northeast'. It is undeniable that the region is a treasure house of unique traditions, cultures, languages and dialects. However, the only obvious trait of homogeneity among them is reclaiming their indigenous oral tradition/s in present time. Within any colonial discourse, the oral form “is generally identified with the illiterate and even the uncivilized” (Ao 104). Hence, tribal oral culture has oftentimes been misinterpreted by colonial discourse, which leads to identifying the communities as illiterate, ignorant, and backward. The study will examine how the concept of writing orality is accomplished in Boats on Land, a short story collection by contemporary Khasi writer Janice Pariat. The stories in this collection attempt to disrupt the hierarchy of the textual over the spoken, a binary that Pariat believes is a colonial construct. The study aims to show how the valorisation of the Khasi oral tradition in the text challenges the pre-established dominance of written literature.

DOI:

 

Published by: vikaspatanker

Authors: Riya Dadarao Gawai

Abstract: India's wedding traditions are deeply rooted in cultural rituals, symbolic gestures, and spatial expressions. With the rise of destination weddings in India, there's an increasing demand for architectural spaces that not only cater to luxury and comfort but also honor the rituals, aesthetics, and materiality of Indian traditions. This research investigates how contemporary wedding venues can reflect India's cultural diversity through regional architectural language, use of traditional materials, and planning of ritual-centric spaces. The goal is to bridge modern hospitality design with traditional Indian values.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: A. S. Bagawan, C. S. Katageri, S. N. Benal

Abstract: Biodegradable plastics have emerged as a potential solution to mitigate the environmental impacts of conventional plastics, which persist in ecosystems for centuries. This article evaluates the environmental safety of biodegradable plastics, focusing on their degradation mechanisms, ecological impacts, and lifecycle sustainability. Through a mixed-methods approach combining literature review and experimental analysis, we assess the performance of common biodegradable plastics like polylactic acid (PLA) and polyhydroxyalkanoates (PHA) under various environmental conditions. Findings indicate that while biodegradable plastics offer reduced persistence compared to conventional plastics, their environmental safety depends on proper waste management infrastructure and environmental conditions conducive to biodegradation. Challenges such as incomplete degradation, microplastic formation, and high production costs underscore the need for improved materials and policies. This study highlights the potential of biodegradable plastics to enhance environmental safety while identifying critical areas for future research and development.

DOI: http://doi.org/10.5281/zenodo.15754651

Published by: vikaspatanker

Authors: Shriti Malviya, Professor Shashikant B. Dhobale

Abstract: The present study demonstrates the study and modelling of the geo polymer and Geo-Jute Fabric Pavement. The main idea is to reduce the Water Infiltration and the Erosive property of the traditional bitumen roads. In this project we have introduced 2 layers of Geo-Jute Fabric which are placed between the Subgrade and the Base course and another one is placed between the Binder course and the Surface course. The Geo-Jute layer between the Subgrade and Base course will reduce the Water Infiltration property whereas the Geo-Jute Fabric between the Binder course and the Surface course will reduce the progression of wear and tear underneath the Surface course. The table compares the key properties of two materials: GeoJute and Geopolymer, focusing on their CBR value, water absorption, and cost. GeoJute shows a higher California Bearing Ratio (CBR) value of 4.6, compared to 3.5 for Geopolymer. This indicates that GeoJute provides better strength and load-bearing capacity in subgrade soil reinforcement applications. GeoJute exhibits significantly higher water absorption, with a rate of 200%, while Geopolymer has a much lower absorption rate of 10%. This suggests that GeoJute retains more moisture, which might affect its performance in wet conditions.

 

 

Published by: vikaspatanker