Md Nasim Fardous Zim | Searcharxiv

arXiv2026

Balancing Innovation and Sustainability: Addressing the Environmental Impact of Bitcoin Mining

This study explores the intersection of technological innovation and environmental sustainability in the context of Bitcoin mining. With Bitcoin's growing adoption, concerns surrounding the energy consumption and environmental impact of mining activities have intensified. The study examines the core process of Bitcoin mining, focusing on its energy-intensive proof-of-work mechanism, and provides a detailed analysis of its ecological footprint, especially in terms of carbon emissions and electronic waste. Various models estimate that Bitcoin's energy consumption rivals that of entire nations, highlighting serious sustainability concerns. To address these issues, the paper unearths potential technological innovations, such as energy-efficient mining hardware and the integration of renewable energy sources, as viable strategies to reduce environmental impact. Additionally, the study reviews current sustainability initiatives, including efforts to lower carbon footprints and manage electronic waste effectively. Regulatory developments and market-based approaches are also discussed as possible pathways to mitigate the environmental harm associated with Bitcoin mining. Ultimately, the paper advocates for a balanced approach that fosters technological innovation while promoting environmental responsibility, suggesting that, with appropriate policy and technological interventions, Bitcoin mining can evolve to be both innovative and sustainable.

cs.CY↗

arXiv2025

detectGNN: Harnessing Graph Neural Networks for Enhanced Fraud Detection in Credit Card Transactions

Credit card fraud is a major issue nowadays, costing huge money and affecting trust in financial systems. Traditional fraud detection methods often fail to detect advanced and growing fraud techniques. This study focuses on using Graph Neural Networks (GNNs) to improve fraud detection by analyzing transactions as a network of connected data points, such as accounts, traders, and devices. The proposed "detectGNN" model uses advanced features like time-based patterns and dynamic updates to expose hidden fraud and improve detection accuracy. Tests show that GNNs perform better than traditional methods in finding complex and multi-layered fraud. The model also addresses real-time processing, data imbalance, and privacy concerns, making it practical for real-world use. This research shows that GNNs can provide a powerful, accurate, and a scalable solution for detecting fraud. Future work will focus on making the models easier to understand, privacy-friendly, and adaptable to new types of fraud, ensuring safer financial transactions in the digital world.

cs.CR↗