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E-grāmata: Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies

Edited by , Edited by (King Faisal University, Saudi Arabia.), Edited by , Edited by (University of Petroleum and Energy Studies, India)
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This book focuses on Artificial Intelligence and Machine Learning technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems.



This book focuses on Artificial Intelligence and Machine Learning technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications they are finding use in.

Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies

offers a broad package involving the incubation of ML and AI with various emerging technologies such as IoT, Healthcare, Smart Cities, Robotics, and more. The book discusses various data collection and data transformation techniques and maps the legal and ethical issues of data-driven e-healthcare systems while covering the possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.

With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.

1. Deep Learning Strategies in Biomedicine Imaging Technique.
2. X-Ray-Based Pneumonia Detection Using ResNet50 and VGG16 Extracted Features and Conventional Machine Learning Algorithms.
3. Enrichment of Human Life through Intelligent Wearable Technology.
4. Reliability and Validity of Survey Questionnaires for Identifying Learning Disabilities in an Intelligent Tutoring System.
5. A Survey of Artificial Intelligent Techniques for Cancer Detection.
6. Ethical Issues in Medical Data.
7. AI-Based Waste Classification in the Healthcare Industry.
8. SmartWear: An IoT-Based Integration of Home Automation and Healthcare Watch.
9. An Analytical Comparison of the Identification of Non-Small Cell Lung Cancer Nodules Using CT Scans and Prominent Deep Learning Models.
10. Abnormality Classifications Using Machine Learning.
11. Multilayer Perceptron-Based Speech Emotion Recognition for Identifying the Problematic Skills of Dyslexic Learners.
12. An Improved Convolutional Neural Network-Based Detection Framework for COVID-19 Omicron and Delta Variants Employing CT Scans.
13. A Survey of IoT in Healthcare: Technologies, Applications, and Challenges.

Rohit Tanwar, PhD, is an Associate Professor in the School of Computer Science at the University of Petroleum and Energy Studies (UPES), Dehradun, Misraspatti, India.

Surbhi Bhatia Khan, PhD, a Lecturer in the Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom.

Varun Sapra, PhD, is presently associated with the School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India. Before joining academia, he was in the corporate sector and worked at Cupid Software, Web Opac Applications, CMA, and many more.

Neelu Jyothi Ahuja, PhD, is a Professor and Head of the Department of Systemics at the School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.
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