Handbook of AI-Based Models in Healthcare and Medicine (Artificial Intelligence in Smart Healthcare Systems)

Handbook of AI-Based Models in Healthcare and Medicine (Artificial Intelligence in Smart Healthcare Systems)

This handbook provides thorough, in-depth, and well-focused developments of artificial intelligence (AI), machine learning (ML), deep learning (DL), natural language processing (NLP), cryptography, and blockchain approaches, along with their applications focused on healthcare systems. Handbook of AI-Based Models in Healthcare and Medicine: Approaches, Theories, and Applications highlights different approaches, theories, and applications of intelligent systems from a practical as well as a theoretical view of the healthcare domain. It uses a medically oriented approach in its discussions of human biology, healthcare, and medicine and presents NLP-based medical reports and medicine enhancements. The handbook includes advanced models of ML and DL for the management of healthcare systems and also discusses blockchain-based healthcare management. In addition, the handbook offers use cases where AI, ML, and DL can help solve healthcare complications. Undergraduate and postgraduate students, academicians, researchers, and industry professionals who have an interest in understanding the applications of ML/DL in the healthcare setting will want this reference on their bookshelf. Dr. Bhanu Chander , working as Assistant Professor at Indian Institute of Information Technology (IIIT-K), Pala, Kerala, India, graduated from Acharya Nagarjuna University, Andhra Pradesh, India, and received a postgraduate degree from the Central University of Rajasthan, India. Dr. Bhanu earned a Ph.D. in Machine Learning in Wireless Sensor Networks for Sensor Data Classification from Pondicherry University, India, in 2022. Dr. Bhanu’s primary research interests are in the areas of wireless sensor networks, machine learning, and IoT security. As we know, computer science as a field has largely focused on problems relevant to the developed world. The internet and the world wide web have remained largely urban phenomena, which means that a significant fraction of the developing world, especially in rural and underdeveloped regions, remains disconnected from the rest of the world. Dr. Bhanu is an academic reviewer recognized by IEEE, ACM, and Springer, and has served for 16 various scientific journals and conferences in the review process of more than 50 articles. He contributed as a track chair and session chair for numerous international conferences and workshops, and performed as a technical program committee (TPC) member for several international conferences organized by IEEE, Springer, and ACM. He is interested in machine learning techniques for energy-efficient 6G networks, blockchain technology for the security of the Internet of Things and wireless communications, analysis and verification of cryptographic protocols, and identification of novel features or misclassified features in satellite image analysis. He published eight articles in peer-reviewed journals, five international conferences, and eight book chapters (Elsevier, Wiley, CRC, and Springer). Presently, his main areas of interest include wireless sensor networks (WSN), IoT and healthcare, cryptography, machine learning, and deep learning. Dr. Anoop , working as Postdoctoral Research Fellow at Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Centre, US, received a Ph.D. degree in the Development of Automated Methods for Retinal Optical Coherence Tomography Image Analysis from the National Institute of Technology, Surathkal, India, in 2021 and M.Tech on Signal Processing from National Institute of Technology, Calicut, India, 2013. He received the Best Paper award at the ninth International Conference on Pattern Recognition and Machine Intelligence, PReMI 2021 for the work entitled “Attention-Assisted Patchwise CNN for the Segmentation of Fluids from the Retinal Optical Coherence Tomography Images” Organized by Machine Intelligence Unit, Indian Statistical Institute (ISI), Kolkata, India, in December 2021. He attended the 43rd-annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC21). Research fellow on a project entitled “Retinal Cysts Identification and Quantification from Low-SNR Optical Coherence Tomography Scans Using Image Processing Techniques” (funding agency: DST-SERB) from May 2017 to March 2020. He has membership in the professional bodies IEEE, IEEE Signal Processing, Engineering in Medicine and Biology Society, and Internet Society. He published six articles in peer-reviewed journals and two book chapters. Presently, his main areas of interest include medical image processing, deep learning, GANs and auto-encoders. Dr. Koppala Guravaiah , working as Assistant Professor at Indian Institute of Information Technology (IIIT-K), Pala, Kerala, India, completed a Ph.D. on the topic of Performance of Routing Protocols in Wireless Sensor Networks using River Formation Dynamics from National Institute of Technology Tiruchirappalli, India. His research interests include the Internet