Artificial Intelligence and Data Science in Healthcare Applications (Hardcover)

Artificial Intelligence and Data Science in Healthcare Applications provides a thorough in-depth examination of how AI and data science are transforming predictive analytics and outlier detection in every industry. With in-depth examinations of machine learning, neural networks, NLP, and ethics of AI, this book prepares readers with both theoretical principles and practical tools to create smart, scalable systems. Real-world healthcare, cybersecurity, and finance case studies exemplify real world applications, and tutorials with leading libraries serve as a starting point for implementation.

Features:

• Offers a broad overview of both foundational and advanced topics in artificial intelligence and data science, focusing particularly on their applications in prediction and detection.
• Addresses the ethical implications and social impact of artificial intelligence and data science, discussing topics such as algorithmic bias, privacy, and the ethical use of predictive technologies.
• Discusses enhanced deep learning model to detect lung diseases from Chest-Xray.
• Highlights lung cancer prediction using variational autoencoders and early stopping for neural network clustering and optimal tuning.
• Evaluates mental well-being through wearable sensors utilizing machine learning.

It will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer engineering, information technology, and biomedical engineering.

Ashwani Kumar is working as a Professor, in the Department of Computer Science and Engineering, School of Engineering and Technology, at Bennett University, India. He has more than fourteen years of teaching and research experience. He has published more than seventy research papers in internationally reputed journals indexed in SCI/SCIE Web of Science and Scopus. His research area includes image security, watermarking techniques, multimedia applications, security and privacy, cryptography, and network security. He is also a lifetime member of professional societies like IEEE, ISTE, IACSIT, IAENG, and SDIWC.

Gautam Srivastava (Senior Member, IEEE) is currently a Professor of Computer Science at Brandon University, Brandon, Canada. In his 10-year academic career, he has published a total of 400 papers in high-impact conferences in many countries and high-status journals (SCI and SCIE). He is an Editor of several international scientific research journals, including IEEE Transactions on Industrial Informatics, IEEE Transactions on Computational Social Systems, and IEEE Internet of Things Journal.

P. K. Gupta is currently working as a Professor and academic coordinator, in the Department of Computer Science and Engineering, at Jaypee University of Information Technology (JUIT), Solan, India. He has more than twenty years of extensive experience in the IT industry and Academics in India and abroad. He has research experience in the Internet of Things, Artificial Intelligence, Cloud Computing, and Fog Computing. He has authored more than a hundred research papers in referred journals and international conferences. He is currently serving as a Life Member of the Computer Society of India (CSI), a Life member of the Indian Science Congress Association (ISCA), a Member of IEEE, a Professional member of ACM, a Senior member of IACSIT, and a Member of the Indian Society for Technical Education (ISTE).

Mohit Kumar is presently working as an Associate Professor in the Department of Computer Science & Engineering at Amity University Jharkhand, India. He has authored/ co-authored papers in reputed top-cited journals like IEEE Transaction in Industrial Informatics, IEEE Transaction on Network Science and Engineering, Journal of Information Science & Engineering, MDPI Sensors, Symmetry, Electronics, and many more with a cumulative impact factor of >50. His research interests include Wireless Sensor Networks, the Internet of Things, Data Science, Machine Learning, and Deep Learning.