Artificial Intelligence and Intelligent Decision Support Systems: Emerging Trends and Applications in Computer Science
TL;DR
The fundamental concepts of AI, its major techniques, including machine learning, deep learning, expert systems, fuzzy logic, reinforcement learning, explainable AI, and generative AI, and their roles in modern decision support systems are examined.
Abstract
Artificial Intelligence (AI) has become a transformative technology for enhancing Intelligent Decision Support Systems (IDSS) by enabling accurate, adaptive, and data-driven decision-making across diverse computer science applications. This review examines the fundamental concepts of AI, its major techniques, including machine learning, deep learning, expert systems, fuzzy logic, reinforcement learning, explainable AI, and generative AI, and their roles in modern decision support systems. It further discusses emerging trends such as human-centered AI, edge AI, federated learning, digital twins, hybrid AI models, and responsible AI that are reshaping intelligent decision-making. The review also highlights the applications of AI-enabled IDSS in cybersecurity, software engineering, cloud computing, the Internet of Things, healthcare informatics, robotics, big data analytics, smart manufacturing, and education technologies. In addition, key challenges related to data quality, explainability, scalability, privacy, ethics, computational complexity, and user trust are critically discussed, followed by future research directions emphasizing foundation models, neuro-symbolic AI, Green AI, Quantum AI, AI governance, and autonomous decision intelligence. Overall, the review provides a comprehensive overview of recent advancements and identifies promising opportunities for developing trustworthy and efficient AI-driven intelligent decision support systems.