Artificial Intelligence, Machine Learning, and Mental Health in Pandemics : A Computational Approach
Language: English
Published by Elsevier Inc, 2022
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
US$ 183.22
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nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researchers and health professionals in psychological health. The book takes a unique approach by exploring how Artificial Intelligence (AI) and Machine Learning (ML) based solutions can assist with monitoring, detection and intervention for mental health at an early stage. Chapters include computational approaches, computational models, machine learning based anxiety and depression detection and artificial intelligence detection of mental health. With the increase in number of natural disasters and the ongoing pandemic, people are experiencing uncertainty, leading to fear, anxiety and depression, hence this is a timely resource on the latest updates in the field.
Seller Inventory # 9780323911962
- Title
- Artificial Intelligence, Machine Learning, and Mental Health in Pandemics : A Computational Approach
- Author
- Princi Jain
- Publisher
- Elsevier Inc
- Publication year
- 2022
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 032391196X
- ISBN 13
- 9780323911962
- Item weight
- 560 grams
- Dimensions
- 229x152x22 mm
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researchers and health professionals in psychological health. The book takes a unique approach by exploring how Artificial Intelligence (AI) and Machine Learning (ML) based solutions can assist with monitoring, detection and intervention for mental health at an early stage. Chapters include computational approaches, computational models, machine learning based anxiety and depression detection and artificial intelligence detection of mental health.
With the increase in number of natural disasters and the ongoing pandemic, people are experiencing uncertainty, leading to fear, anxiety and depression, hence this is a timely resource on the latest updates in the field.
- Examines the datasets and algorithms that can be used to detect mental disorders
- Covers machine learning solutions that can help determine the precautionary measures of psychological health problems
- Highlights innovative AI solutions and bi-statistics computation that can strengthen day-to-day medical procedures and decision-making
"Synopsis" may belong to another edition of this title.
About the Author
Kavita Pandey received her B.Tech. in Computer Science and Engineering from M.D. University in 2002 and M.Tech. (CS) from Banasthali Vidyapeeth University in year 2003. She has obtained her Ph.D. (CS) from Jaypee Institute of Information Technology (JIIT), Noida, India in January, 2017. She is currently working as an Assistant Professor (Senior Grade) in JIIT, Noida. Her research interests include Soft Computing, Machine Learning, Vehicular Ad hoc Networks, Internet of Things and Optimization Techniques. She has published various papers in International journals and conferences including Wiley, IEEE, Springer, Inderscience, etc. She worked as a guest editor of special issue, "Advances in Computational Intelligence and Applications" in International Journal of Information Retrieval Research (IJIRR), IGI Global, ESCI, Web of Science. She worked as a reviewer in many international journals of renowned publishers including Elsevier, Inderscience, IEEE Access, etc. She is an active TPC member of many conferences such as REDSET, IC3, UPCON, TEAMC, ICTCS and many more. She is also a senior member of IEEE society.
Dr. Princi Jain is a Professor in the Department of Medicine at Lady Hardinge Medical College and Associated SSKH, New Delhi. A trained MBBS and MD examiner, she is an internal medicine specialist with clinical expertise in rheumatology, diabetology, and cardiology. Dr. Jain is deeply committed to serving underserved patient populations, providing free diagnosis and treatment, and coordinating advanced interventions through other hospitals when needed. She has received specialized training through the Government of India’s NELS Program and Nuclear Disaster Management, reflecting her dedication to national health initiatives and emergency preparedness. Certified in ACLS and BLS, she upholds the highest standards of emergency care. Dr. Jain actively participates in public awareness programs, teaches MBBS and postgraduate students, supervises thesis work, and contributes to conferences and workshops. During COVID-19, she served both in ward duties and as a nodal officer.
Prof. Kah Phooi Seng is an established senior academic with proven track record of teaching, research and leadership in Data Science including Machine Learning and Artificial Intelligence (AI), Engineering, Computer Science and IT disciplines. She has a PhD in AI and Signal Processing from the University of Tasmania, Australia. She has about more than 15 years substantial academic experience in Australian–based and UK–based universities. Currently, she is the Adjunct Professor in School of Engineering & IT at University of New South Wales (UNSW). Her previous academic experiences include being Professor in Computer Science and Head of Department of Computer Science & Networked Systems at Sunway University (affiliated with Lancaster University UK). Research-wise, she has a strong record of publications and published over 250 papers in journals and international refereed conferences in the areas of data science/analytics, machine learning, AI and intelligent systems, mobile software development, Big data, IoT & cloud computing, multimodal signal processing, visual information engineering, sensor networks, reconfigurable embedded systems, etc. She has made significant contributions to research in those fields as testified by publications in premier journals such as the IEEE Transactions. More than 50 of her journal papers are Tier One & Two (Q and Q2) papers. Her recent research papers have been published by IEEE Transactions journals with Impact Factors ranging from 2.563 to 7.596. The total count of her citations is about 4500, with h-index of 25 and i10-index of 60 following Google Scholar.
"About the title" may belong to another edition of this title.
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