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Artificial Intelligence: Exploring Machine Learning, Deep Learning, and Neural Networks
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Author(s): C. Thiyagarajan (Panimalar Engineering College, India), V. Kalyanasundaram (SRM Institute of Science and Technology, India), R. Siva Subramanian (R.M.K. College of Engineering and Technology, India), K. Saravanan (SRM Institute of Science and Technology, India), S. Vidyasagar (SRM Institute of Science and Technology, India), V. Kalai Priya (RMK College of Engineering and Technology, India)and M. Nalini (S.A. Engineering College, India)
Copyright: 2026
Pages: 28
Source title:
Enhancing Autonomous and Adaptive Systems With AI and IoT
Source Author(s)/Editor(s): Ben Othman Soufiene (King Faisal University, Saudi Arabia)
DOI: 10.4018/979-8-3373-3146-1.ch002
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Abstract
Artificial Intelligence (AI) has rapidly advanced with key contributions from Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN). These technologies form the foundation for numerous applications across diverse domains such as healthcare, finance, and autonomous systems. This chapter explores the fundamental concepts, techniques, and architectures of ML, DL, and NN, delving into the evolution of these fields and their current state. The chapter also highlights the challenges, such as data quality, interpretability, and computational efficiency, while discussing emerging trends like Explainable AI, Federated Learning, and energy-efficient AI. As AI continues to evolve, it holds the potential to transform industries and society, underscoring the importance of responsible and sustainable development.
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