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The Impact of Digital Payments in the Modern Global World: A Comparison Between India, EU/US/UK and Italy

This study explores the rapid growth of digital payment systems and their impact on financial inclusion and literacy around the world. Over the past decade, these platforms have become increasingly integrated into the economies of many countries, changing the way people access and manage money. The research focuses on how digital payment systems have emerged in the modern global context and examines their influence on financial literacy across different populations. A comparative analysis is conducted for countries including the United States, India, the European Union, and Italy, highlighting differences in adoption, regulatory approaches, and accessibility. Using qualitative analysis of policy reports and secondary data from international financial institutions, the study identifies both opportunities and challenges associated with digital payment expansion. Findings suggest that while these systems make financial transactions easier and more efficient, gaps in digital skills, infrastructure, and access remain. The study emphasizes the need for targeted policies and education programs to ensure that digital payments contribute to inclusive and sustainable financial development.

Published by: Vedaansh Chaudhary

Author: Vedaansh Chaudhary

Paper ID: V12I1-1163

Paper Status: published

Published: March 5, 2026

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Research Paper

Impact of Aging on the Endocrine System

The endocrine system plays a central role in maintaining homeostasis through hormonal regulation. Aging is associated with progressive dysregulation of several endocrine axes, resulting in widespread effects on metabolism, musculoskeletal integrity, immune function, mental health, etc. This paper examines the impact of aging on key systems, including the growth hormone–insulin-like growth factor-I axis (GH-IGF-I Axis), dehydroepiandrosterone (DHEA(S)), the hypothalamo–pituitary–adrenal axis (HPA Axis), menopause, and andropause. Understanding these hormonal changes is essential for addressing age-related morbidity and improving quality of life in older populations.

Published by: Paarth Dhawan

Author: Paarth Dhawan

Paper ID: V12I1-1154

Paper Status: published

Published: March 5, 2026

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Research Paper

Life Cycle Assessment of Electric Vehicles vs. Internal Combustion Engine Vehicles: A Post-2020 Comparative Analysis

The rapid electrification of transportation after 2020 has increased discussions about whether electric vehicles (EVs) are more environmentally friendly than internal combustion engine vehicles (ICEVs). Although EVs do not produce tailpipe emissions, their total impact on the environment depends on the manufacturing process, battery production, the combination of electricity production, and end-of-life treatment. In this work, the LCA of EVs and ICEVs on the basis of cradle-to-grave life cycle analysis is provided under the conditions of technological progress and the grid in the post-2020 period, with references to the Indian electricity mix. Mathematical emission modeling, break-even analysis, and sensitivity analysis are conducted for a functional unit of 150,000 km. Results indicate that EVs exhibit higher manufacturing emissions but lower operational emissions. Compared to ICEVs, EVs exhibit 6–30% lower total life cycle emissions under current grid conditions (0.7 kg CO₂/kWh). The study also emphasizes how important grid decarbonization is to improving EV sustainability performance.

Published by: Khushdeen Singh Khosa, Harpreet Singh

Author: Khushdeen Singh Khosa

Paper ID: V12I1-1182

Paper Status: published

Published: March 2, 2026

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Research Paper

Computer Networks VR

Computer Networks VR is an engaging educational game that turns key ideas of computer networking into a gameplay experience focused on packet transfer methods. Players take on the role of "Ping," moving through a factory designed like a Wi-Fi router, which serves as a metaphor for actual network operations. By handling packets shown as labeled boxes and setting IP and destination addresses, along with solving routing problems, players gain a solid understanding of data transmission. As they advance through different levels, they come across packet structures and protocols, solidifying their theoretical knowledge with practical simulations. This game highlights the potential of virtual reality as a valuable tool for making abstract networking concepts interactive and hands-on.

Published by: Shrey Sharma, Nendra Namgyel Wangchuk, Abhijeet Sharma

Author: Shrey Sharma

Paper ID: V12I1-1177

Paper Status: retracted

Submitted: February 26, 2026

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Research Paper

An Intelligent AI-Based Framework for Handwritten Character Recognition

Handwritten Character Recognition (HCR) remains a fundamental yet challenging problem in pattern recognition due to variations in writing styles, distortions, noise, and inter-class similarity. This study proposes an intelligent AI-based framework for robust handwritten character recognition using a Convolutional Neural Network (CNN). The model is trained and evaluated on a self-curated dataset comprising 13,640 grayscale images representing 62-character classes, including digits (0–9), lowercase letters (a–z), and uppercase letters (A–Z). Images are standardized to a resolution of 28×28 pixels and normalized to enhance learning efficiency. The proposed CNN architecture leverages hierarchical feature extraction through multiple convolutional and pooling layers, followed by dense layers for classification. Experimental results demonstrate a recognition accuracy of approximately 93%, indicating strong generalization capability despite handwriting variability. The proposed framework emphasizes automated feature learning, eliminating the dependency on handcrafted descriptors traditionally used in character recognition systems. The model exhibits strong adaptability across diverse handwriting patterns, demonstrating robustness to intra-class variations. Furthermore, the lightweight CNN architecture ensures computational efficiency, making the system suitable for real-time applications and deployment in resource-constrained environments. The study also highlights the critical role of dataset quality, preprocessing strategies, and normalization techniques in improving recognition performance. Overall, the findings confirm that deep learning-driven approaches offer a reliable, scalable, and efficient solution for handwritten character recognition.

Published by: Ajay Kumar R, Umadevi C, Savitha M M, Dennis Thomas, Sahana G, Bhuvaneshwari MJ, Hemanth V, Roja KV, Tejas NR

Author: Ajay Kumar R

Paper ID: V12I1-1166

Paper Status: published

Published: February 23, 2026

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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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