The book explores the ways artificial intelligence (AI) theory is being applied to an extremely diverse range of industries, illustrating just how robust and adaptable this technology has become. AI is revolutionizing the way the world operates by allowing machines to learn from data, make choices, and even resolve problems similar to a human, but quicker and oftentimes more precisely. From its integration into innovative applications such as smart assistants and personalized suggestions to the application of quantum machine learning in identifying multifunctional materials for future technologies, AI is breaking boundaries everywhere. Within the book, every chapter explores a particular industry including energy, healthcare, manufacturing, and business and how AI is not only a theoretical idea, but a real-world tool that fuels innovation. For example, a variety of topics are examined such as how AI is central to the computational design of semiconductors, which are critical to electronics and emerging tech innovations. In manufacturing, AI solves challenges in virtual metrology, enhancing the precision and efficiency of production lines. The book also showcases how AI enables sustainability and assists in energy management and the fight against climate change. Oil and gas industries are getting smarter and more efficient with the application of AI to optimize operations and safety. In healthcare, AI is transforming preventive care, assisting doctors in identifying diseases at an early stage and tailoring treatments. The book also dives into the deployment of AI on devices such as smartphones and wearables, enabling real-time, privacy-focused decision-making. Businesses benefit from AI's capability in data-driven decision-making, leading to smarter strategies and faster growth. Lastly, AI is enhancing our interaction with technology through immersive AR/VR experiences, making them more intuitive and responsive. This book encourages interdisciplinary thinking between energy, healthcare, and business fields.

Autorentext

Aditya Mishra, Ph.D., is a Lithography and Device Integration Yield Engineer at Intel Corporation in Oregon, USA. He received his Ph.D. from the University of Texas at Dallas. Dr. Mishra has authored numerous peer-reviewed publications in the field of optoelectronics and semiconductor technology, with his work appearing in prestigious, highly-cited academic journals. In recognition of his significant contributions to perovskite-based optoelectronic device research, he was awarded the SPIE Fellowship in 2022. His technical expertise includes CMOS Semiconductor Process Integration, electron beam metrology, electron beam lithography, and failure analysis methodologies.

Yogesh Kumar Rathore, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in Raipur, India. He completed his Ph.D. in Information Technology from the National Institute of Technology, Raipur. Before that, he completed his M. Tech. from Chhattisgarh Swami Vivekanand Technical University and his B. Eng. from Pt. Ravishankar Shukla University. He has published two edited books, contributed chapters to internationally edited books, and authored a textbook on data mining. Dr. Rathore has also been involved in patenting with three Indian patents published and two USA-granted patents, showcasing his expertise in the field of computer science engineering.

Suman Kumar Swanrkar, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in Raipur, India. He received his Ph.D. (CSE) from Kalinga University, Nayaraipur and his M.Tech. (CSE) from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has contributed to book chapters published by Elsevier and Springer and is a member of IEEE, IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOP, IAOIP, EAI, and CSTA. Dr. Swanrkar's research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.