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mods: Artificial intelligence, machine learning, and mental health in pandemics a computational approach Jain, Shikha (Computer scientist) editor. Pandey, Kavita editor. Jain, Princi editor. Seng, Kah Phooi 1974- editor. ScienceDirect (Online service) text bibliography Electronic books. enk London Academic Press 2022 monographic eng remote 1 online resource (xliv, 374 pages) 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. Mental health impact of COVID-19 and machine learning applications in combating mental disorders : a review / Chirantan Ganguly, Sagnik Nayak and Anil Kumar Gupta -- Multimodal depression detection using machine learning / Roshan Jahan and Manish Madhav Tripathi -- A graph convolutional network based framework for mental stress prediction / Akhila Sahiti Racherla, Raj Sahu and Vandana Bhattacharjee -- Women working in healthcare sector during COVID-19 in…