The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases.

Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Big Data
Deep Learning
Human behavior modeling
Image Data
Internet of Medical Things
Internet of Things
Medical Images
Medical Sensors
Mental Healthcare
Mobile Health
Multimodal Analysis

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Editor/Author Biographies

Dr. Noor Zaman received a Ph.D. degree in IT from UTP, Malaysia. He has great international exposure in academia, research, administration, and academic quality accreditation. He was with ILMA University, KFU for a decade, and is currently with Taylor’s University, Malaysia. He has 19 years of teaching and administrative experience. Besides scientific research activities, he had worked a decade for academic accreditation and earned ABET accreditation twice for three programs at CCSIT, King Faisal University, Saudi Arabia. Dr. Noor Zaman has recently been awarded as a top reviewer (1% globally) by WoS/ISI (Publons). He has edited/authored more than 11 research books with international and reputed publishers, earned several research grants, and has a great number of indexed research articles on his credit. He has supervised several postgraduate students including masters and Ph.D candidates. He is an Associate Editor of IEEE ACCESS, Guest editor of several reputed journals, member of the editorial board of several research journals, and an active TPC member of reputed conferences around the globe.

Loveleen Gaur is currently working as an adjunct professor with Taylor University, Malaysia & University of South Pacific, Fiji and academic consultant with Australian School of Graduate Studies. Before moving to USA, she was working as Professor with Amity University, India. She has supervised several PhD scholars, Post Graduate students, mainly in Artificial Intelligence and Data Analytics for business and healthcare. Under her guidance, the AI/Data Analytics research cluster has published extensively in high impact factor journals and has established extensive research collaboration globally with several renowned professionals. She is a senior IEEE member and Series Editor with CRC and Wiley. She has high indexed publications in SCI/ABDC/WoS/Scopus and has several Patents/copyrights on her account, edited/authored many research books published by world-class publishers. She has excellent experience in supervising and co-supervising postgraduate and PhD students internationally. An ample number of Ph.D. and master’s students graduated under her supervision. She is an external Ph.D./Master thesis examiner/evaluator for several universities globally. She has also served as Keynote speaker for several international conferences, presented several Webinars worldwide, chaired international conference sessions. Prof. Gaur has significantly contributed to enhancing scientific understanding by participating in many scientific conferences, symposia, and seminars, by chairing technical sessions and delivering plenary and invited talks. She has specialized in the fields of Artificial Intelligence, Machine Learning, Pattern Recognition, Internet of Things, Data Analytics and Business Intelligence. She has chaired various positions in International Conferences of repute and is a reviewer with top rated journals of IEEE, SCI and ABDC Journals. She has been honored with prestigious National and International awards. She has introduced courses related to Artificial Intelligence specialization including, Predictive Analytics, Deep and Reinforcement learning etc. She has vast experience teaching advanced-era specialized courses, including Predictive Analytics, Data Visualization, Social Network Analytics, Deep Learning, Power BI, Digital Marketing and Digital Innovation etc., besides other undergraduate and postgraduate courses, graduation projects, and thesis supervision.

Mamoona Humayun has completed her PhD in Computer Sciences from Harbin Institute of Technology, China. She has 15 years of teaching and administrative experience internationally. She has an extensive teaching, research supervision, and administrative work background. She is the guest Editor and reviewer for several reputable journals and conferences around the globe. She has authored several research papers, supervised many postgraduate students, and has an external thesis examiner to her credit. She has strong analytical, problem-solving, interpersonal, and communication skills. Her areas of interest include Cyber Security, Wireless Sensor Networks (WSN), the Internet of Things (IoT), Requirement Engineering, Global Software Development, and Knowledge Management.

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