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Research Paper

Lightweight Machine Learning Models for Detecting DNS Data Exfiltration Attacks in Cloud and Enterprise Networks

Cloud and Enterprise Networks are the foundation of today's digital age, facilitating frictionless communication and service delivery. Cloud and Enterprise Network attacks increasingly depend on trusted protocols, and DNS Data Exfiltration Attacks in Cloud and Enterprise Networks have evolved as a devious and powerful means to evade classic defences. Detecting DNS Data Exfiltration Attacks in Cloud and Enterprise Networks is therefore a pressing challenge that requires efficient and accurate solutions. This study investigates Machine Learning Models for Detecting DNS Data Exfiltration Attacks in Cloud and Enterprise Networks, focusing on lightweight approaches such as Random Forest, Decision Tree, Multi-Layer Perceptron, Logistic Regression, and Gaussian Naïve Bayes. Both Random Forest and Decision Tree achieved perfect evaluation scores (100%) across standard metrics, but closer inspection of confusion matrices revealed Random Forest as the superior model, misclassifying only two malicious instances while generating no false positives. The significance of this research lies in demonstrating that lightweight models, particularly Random Forest, can provide highly accurate, resource-efficient, and practical real-time protection against DNS exfiltration threats, ensuring the resilience of cloud and enterprise infrastructures.

Published by: Tolulope Onasanya, Hannah I. Tanimowo, John Aigberua, Oduwunmi Esther Odukoya

Author: Tolulope Onasanya

Paper ID: V12I1-1155

Paper Status: published

Published: February 21, 2026

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Research Paper

Tribal Health in India: Status, Challenges, and Strategies for Strengthening Healthcare Delivery

Tribal health remains one of the most neglected domains within the Indian public health system despite constitutional safeguards and multiple targeted programmes. Scheduled Tribes (STs), constituting approximately 8.6% of India’s population, continue to experience disproportionately high morbidity and mortality due to a complex interaction of socio-economic deprivation, geographical isolation, cultural barriers, and systemic inadequacies in healthcare delivery. Historical marginalisation, poverty, low literacy levels, and poor living conditions have collectively contributed to persistent health inequities among tribal communities. Conventional healthcare models, which largely follow a uniform national approach, have failed to adequately address the unique cultural, social, and environmental contexts of tribal populations, resulting in limited utilisation of health services and delayed care-seeking behaviour. This paper presents a detailed narrative analysis of the health status of tribal populations in India, drawing upon secondary data from national surveys, census reports, and published literature. The study examines key indicators related to maternal and child health, nutritional status, communicable and non-communicable diseases, and healthcare utilisation patterns among tribal communities. It further explores systemic barriers such as inadequate infrastructure, workforce shortages, accessibility issues, financial constraints, and discrimination within healthcare settings. By reviewing existing policy frameworks and community-based models, the paper proposes context-specific and culturally sensitive strategies to strengthen primary healthcare delivery in tribal areas. The findings emphasise the need for integrated, participatory, and rights-based approaches to reduce health disparities and improve overall health outcomes among tribal populations.

Published by: Aadya Gaur

Author: Aadya Gaur

Paper ID: V12I1-1159

Paper Status: published

Published: February 13, 2026

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Review Paper

A Survey on Modern Computational Methods for Drug Repurposing

Drug repurposing, the process of identifying new therapeutic uses for existing drugs, offers a promising strategy to accelerate drug development by significantly reducing costs, time, and risks compared to de novo drug discovery. The increasing availability of large-scale biomedical data has catalysed the development of computational approaches to systematically identify and prioritise repurposing candidates. This survey reviews the state-of-the-art computational methodologies, with a particular focus on network medicine and machine learning-based techniques. We discuss key approaches such as pathway-based analysis, network proximity, matrix factorisation, and the growing application of deep learning, particularly Graph Neural Networks (GNNs), which leverage complex biomedical networks. The paper explores how these methodsutilisee heterogeneous data—including drug-target interactions, gene-disease associations, and molecular structures—to generate repurposing hypotheses. Furthermore, we outline the primary challenges in the field, including data integration, model generalizability, and the need for explainability, and discuss future directions, such as the integration of multi-modal data and the development of more sophisticated, interpretable AI models.

Published by: Aditi Dipak Thorat, Shlok Shivaji Kaule, Paras Vijay Tak, Anuj Prakash Gagare, Vijayendra S. Gaikwad

Author: Aditi Dipak Thorat

Paper ID: V12I1-1157

Paper Status: published

Published: February 13, 2026

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Research Paper

Predicting Adolescent Psychological Outcomes of Therapeutic Chatbot Use by Integrating Neuroscience, Chatbot, and User Behaviour

As we find our lives more and more intertwined with Artificial Intelligence, we use it for a variety of purposes. Using an AI assistant means that many tasks previously done by us can now be outsourced. This has many implications, cognitive, sociological and emotional. Earlier research in neuroscience suggests that teenagers and young adults are more vulnerable to negative psychological impacts from external influences. A study shows an increase in cognitive decline in students who use AI for essay writing. (Kosmyna). Another preprint finding shows how AI can aid medical misinformation sometimes and enhance patient care other times. (Jedrzejczak et al.). This paper discusses the effects of AI usage for companionship or mental health-focused conversations on adolescents and youth. Drawing on neuroscience literature and understanding the reward circuitry of the brain, it assesses the potential downsides of long-term usage. Deploying a basic chatbot to engage in empathetic conversations and conducting a survey (n=90) post interaction, perceived empathy, validation and other emotional factors are assessed. Another experiment is conducted to quantitatively measure chatbot validation. This paper proposes that AI is over-validating by nature and that it fosters reliance.

Published by: Kavika Singhal

Author: Kavika Singhal

Paper ID: V12I1-1151

Paper Status: published

Published: February 5, 2026

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Research Paper

Growth of Small and Medium Enterprises in Asian Countries with Special Emphasis on India

This paper examines the growth trajectory of Small and Medium Enterprises (SMEs) across Asian countries, with an emphasis on India. SMEs constitute the backbone of economic development in Asia, contributing significantly to employment generation, poverty alleviation, and export growth, yet they face persistent structural challenges like limited access to finance and inadequate infrastructure. This paper also delves into various Government interventions and policies supporting MSMEs. It also draws a comparison of India with other Asian countries such as Indonesia, Malaysia, the Philippines, and Thailand.

Published by: Daksh Singhal

Author: Daksh Singhal

Paper ID: V12I1-1148

Paper Status: published

Published: February 5, 2026

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Research Paper

The Economic Analysis of Gender Pay Gap in Sports

This research paper examines the causes of the gender pay gap in the sports industry. It looks at the many factors affecting the wages of female athletes by highlighting the role of sponsors, industries, labour markets and media. It also reflects the presentation of women in sports using sexual connotations, the limited opportunities and reduced investment they receive, which restricts their skill development and therefore the wages they earn. This research paper emphasises how earnings are not due to lack of performance or athletic abilities, but due to institutional inequalities present in sports industries consisting of women, thus they are not a true reflection of the performance of female athletes.

Published by: Advika Rao

Author: Advika Rao

Paper ID: V12I1-1147

Paper Status: published

Published: February 5, 2026

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