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The convergence of AI and the Internet of Things (IoT) is rapidly transforming the landscape of intelligent devices. As AI algorithms become more sophisticated and embedded in everyday objects, devices are empowered to learn from data and operate with minimal human intervention. This integration not only enhances operational efficiency and user experience but also paves the way for innovative applications across all sectors. Exploring AI advancements in IoT reveals the transformative potential and emerging challenges of a future shaped by intelligent, interconnected systems. AI Advancements in Internet of Things, Smart Healthcare, and Intelligent Devices presents a comprehensive exploration of the powerful convergence between AI, machine learning, and IoT, with a strong emphasis on their integration into intelligent devices. It not only explores foundational concepts but also addresses the practical challenges and opportunities in deploying intelligent systems at scale. Covering topics such as cloud computing, predictive maintenance, and software testing, this book is an excellent resource for academicians, researchers, students, engineers, technologists, policymakers, and more.

Muneer Khan is an accomplished professional in the field of electrical engineering, with a strong background in both academia and industry. He holds a Master of Science in Electrical Engineering from Columbia University, specializing in intelligent and connected devices, sensors, Embedded AI, semiconductor physics, and integrated photonics. Khan has extensive experience in research and applied science, having worked as an Applied Scientist at Harvard University and as a Research Assistant at Columbia University's Laboratory of Unconventional Electronics. He has credited a couple of internships in France, Russia and India. His work spans a variety of advanced technologies, including PCB design, machine learning algorithms, hardware inspection, and robotic sensors. In addition to his academic and research roles, Khan is a successful entrepreneur, having founded Cadre Technologies Services and PICAR Technologies. His ventures focus on AI, machine learning, and assistive technologies, among other areas. He has been recognized for his contributions to science and technology with numerous awards, including the Distinguished Young Scientist Award from the Government of India and multiple accolades from academic institutions. Khan's work has led to several patents, publications, and significant funding, including a $2 million seed fund from the National Science Foundation for his startup. His technical skills are broad, encompassing programming, circuit design, and advanced testing procedures, and he has been involved in various innovative projects and product developments across his career.

Shantanu Awasthi has an extensive academic background, holding a Ph.D. in Mathematics from North Dakota State University, an M.S. in Mathematics from Virginia State University, and a B.Tech in Electronics and Communication Engineering from Maharaja Agrasen Institute of Technology. His professional experience spans several academic and industry roles, including positions as an Assistant Professor of Data Analytics at Missouri Southern State University and Data Science roles at various institutions, such as Sense 360 and the University of North Dakota. His research interests focus on stochastic processes, machine learning, and deep learning, with publications in journals like the Journal of Safety Research and the Journal of Stochastic Analysis. In addition to his research, Shantanu has presented his work at notable conferences, including the North American Meetings of the Regional Science Association International and the SIAM Northern States Section Student Chapter Conference. His teaching experience covers data science, statistics, and mathematics courses at multiple universities. He has also earned several honors, including a Graduate Student Travel Grant and a Department Research Award. Furthermore, he holds certifications in actuarial sciences, SAS programming, and deep learning.