Revolutionizing Healthcare, Risk Management, and Industrial Efficiency through AI-Driven Analytics, Cybersecurity, and Heat Transfer Optimization

Nahid Neoaz and Mohammad Hasan Amin (2025) Revolutionizing Healthcare, Risk Management, and Industrial Efficiency through AI-Driven Analytics, Cybersecurity, and Heat Transfer Optimization. Global Journal of Computer Sciences and Artificial Intelligence, 1 (2). pp. 18-36. ISSN 3079-5753

[thumbnail of article.pdf] Text
article.pdf

Download (1MB)

Abstract

The integration of AI-driven analytics, cybersecurity, and heat transfer optimization is revolutionizing healthcare, risk management, and industrial efficiency. AI enhances data-driven decision-making, enabling predictive analytics for improved diagnostics, risk assessment, and operational efficiency. Cybersecurity safeguards critical digital infrastructures, protecting sensitive data from evolving threats in healthcare and industrial sectors. Meanwhile, heat transfer optimization enhances energy efficiency, ensuring optimal thermal management in medical equipment, manufacturing processes, and energy systems. The synergy of these interdisciplinary technologies fosters innovation, resilience, and sustainability, driving progress in modern industries. However, challenges such as ethical concerns, regulatory compliance, and implementation barriers must be addressed to maximize their potential. Future research will focus on enhancing AI transparency, strengthening cybersecurity frameworks, and advancing thermal management techniques. This multidisciplinary approach promises to transform industries by improving security, efficiency, and sustainability, paving the way for a smarter, safer, and more resilient future.

Item Type: Article
Date Deposited: 14 Oct 2026 05:31
Last Modified: 14 Oct 2026 05:31
URI: https://repository.globalsciencepress.com/id/eprint/576

Actions (login required)

View Item
View Item