Harnessing AI-Driven Analytics, Cybersecurity, and Heat Transfer Optimization: A Multidisciplinary Strategy for Revolutionizing Healthcare, Strengthening Risk Management, and Enhancing Industrial Performance
Year: 2025
Type: article
Abstract: The integration of AI-driven analytics, cybersecurity, and heat transfer optimization is transforming modern industries by enhancing efficiency, security, and sustainability. AI plays a crucial role in cybersecurity by detecting and mitigating threats while also improving thermal management systems across various sectors, including healthcare, manufacturing, and energy. This multidisciplinary approach enables real-time monitoring, predictive maintenance, and automated decision-making, leading to optimized industrial performance and risk mitigation. However, the widespread adoption of AI comes with ethical concerns, security vulnerabilities, and implementation challenges. AI biases in decision-making, lack of transparency, and privacy risks raise concerns about fairness and reliability. Additionally, AI-powered systems are vulnerable to adversarial attacks, data poisoning, and cyber threats, necessitating robust security frameworks. In heat transfer optimization, AI-driven models enhance energy efficiency, smart cooling, and industrial process management, promising significant advancements in renewable energy and sustainable technologies. This paper highlights key insights, challenges, and future prospects for AI integration in cybersecurity and heat transfer, offering recommendations for further research and industry adoption to build resilient, efficient, and secure technological ecosystems.
Authors: Nahid Neoaz, Mohammad Hasan Amin
Institutions: Wilmington University, Kettering University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407900866