Artificial Intelligence, Machine Learning, and Mental Health in Pandemics
A Computational Approach
- 1st Edition - April 22, 2022
- Imprint: Academic Press
- Editors: Shikha Jain, Kavita Pandey, Princi Jain, Kah Phooi Seng
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 1 9 6 - 2
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 5 5 5 - 7
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researche… Read more
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Request a sales quoteArtificial 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
Researchers and students in Psychology, Computer Science and Health Care clinicians. Health Care Professionals, Decision makers who are utilizing and developing AI based solutions
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Author biographies
- Editor biographies
- Acknowledgment
- Glossary
- Introduction
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- Chapter 10
- Chapter 11
- Chapter 12
- Chapter 13
- Chapter 14
- Chapter One. Mental health impact of COVID-19 and machine learning applications in combating mental disorders: a review
- 1. Introduction
- 2. Methods
- 3. Findings
- 4. Challenges of using machine learning in studying mental health
- 5. Limitations
- 6. Conclusion and future directions
- Chapter Two. Multimodal depression detection using machine learning
- 1. Introduction
- 2. Literature survey
- 3. Social media data
- 4. Attributes definition
- 5. Hybrid deep learner model
- 6. Experimental setting
- 7. Discussion
- 8. Conclusion and future work
- Chapter Three. A graph convolutional network based framework for mental stress prediction
- 1. Introduction
- 2. Related work
- 3. Overview of graph neural networks
- 4. Methodology
- 5. Results
- 6. Conclusion
- Chapter Four. Women working in healthcare sector during COVID-19 in the National Capital Region of India: a case study
- 1. Introduction
- 2. Research methodology
- 3. Literature review
- 4. Problems
- 5. Findings
- 6. Suggestions
- 7. Limitations
- 8. Implications
- 9. Conclusion
- Chapter Five. Impact of COVID-19 on women educators
- 1. Introduction
- 2. Research methodology
- 3. Literature review
- 4. Findings
- 5. Conclusion
- Chapter Six. A deep learning approach toward prediction of mental health of Indians
- 1. COVID-19 and its impact
- 2. Impact of COVID-19
- 3. Mental health
- 4. Impact of mental health in education sector
- 5. Artificial intelligence
- 6. Machine learning
- 7. Deep learning
- 8. Survey of online versus offline modes of teaching
- 9. Prediction of mental health in online mode of teaching using convolutional neural network
- 10. Summary
- Chapter Seven. Machine learning based analysis and prediction of college students' mental health during COVID-19 in India
- 1. Introduction
- 2. Related work
- 3. Proposed framework
- 4. Data analysis of students
- 5. Experimentation and results
- 6. Conclusion
- Chapter Eight. Modeling the impact of the COVID-19 pandemic and socioeconomic factors on global mobility and its effects on mental health
- 1. Introduction
- 2. Background
- 3. Methods
- 4. Results
- 5. Discussion
- 6. Conclusion
- Chapter Nine. Depression detection: approaches, challenges and future directions
- 1. Introduction
- 2. Depression
- 3. Depression datasets
- 4. Depression detection
- 5. Importance of personality in depression detection
- 6. State of the art
- 7. Discussion
- 8. Conclusion and future directions
- Chapter Ten. Improving mental health surveillance over Twitter text classification using word embedding techniques
- 1. Introduction
- 2. Related work
- 3. Methodology
- 4. Evaluation and results
- 5. Conclusion
- Chapter Eleven. Predicting loneliness from social media text using machine learning techniques
- 1. Introduction
- 2. Related work
- 3. Background
- 4. Proposed work
- 5. Implementation details
- 6. Results and discussion
- 7. Conclusion
- Chapter Twelve. Perceiving the level of depression from web text
- 1. Introduction
- 2. Related work
- 3. Background
- 4. Proposed approach
- 5. Results
- 6. Conclusion
- Chapter Thirteen. Technologies for vaccinating COVID-19, its variants and future pandemics: a short survey
- 1. Introduction
- 2. Crowdsourcing in COVID-19
- 3. Internet of Things (IoT) network for vaccination and its distribution
- 4. Cybersecurity and IoT for vaccination and its distribution
- 5. Parallel and distributed computing architecture using IoT network for vaccination and its distribution
- 6. Postquantum cryptography solutions for futuristic security in vaccination and its distribution
- 7. Drone and robotics operation management using IoT network for vaccination and its distribution
- 8. Blockchain technology and IoT for vaccination and its distribution
- 9. Research challenges in vaccination and its distribution
- 10. Conclusion and future directions
- Chapter Fourteen. A blockchain approach on security of health records for children suffering from dyslexia during pandemic COVID-19
- 1. Introduction
- 2. Related study
- 3. Healthcare use cases
- 4. Proposed methodology
- 5. Data acquisition
- 6. Implementation
- 7. Conclusion
- Index
- Edition: 1
- Published: April 22, 2022
- No. of pages (Paperback): 418
- No. of pages (eBook): 418
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323911962
- eBook ISBN: 9780323915557
SJ
Shikha Jain
Shikha Jain is presently working with Jaypee Institute of Information Technology (JIIT), NOIDA, INDIA as Assistant Professor. She has more than seventeen years of research and academic experience. She has received her PhD in computer science from Jaypee Institute of Information Technology, Noida, India. She has published a number of research papers in the renowned journals and conferences. She was advisory board member of a book entitled “Nature-Inspired Algorithms for Big Data Frameworks”, IGI Global. She was a guest editor of a special issue “Advances in Computational Intelligence and its applications” in International Journal of Information Retrieval Research (Publishing phase). She is active reviewer of many International Journals and technical program committee member of various International Conferences. Her research area includes Affective Computing, Emotion Modelling, Cognitive Affective Architectures, Machine Learning and Soft Computing. She is a senior member of IEEE.
Affiliations and expertise
Assistant Professor, Jaypee Institute of Information Technology, Noida, IndiaKP
Kavita Pandey
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.
Affiliations and expertise
Assistant Professor, Jaypee Institute of Information Technology, Noida, IndiaPJ
Princi Jain
Dr. Princi Jain is an Associate Professor at Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS), Lohia Hospital, Delhi, India. She has obtained MD (Internal Medicine) from Vardhman Mahavir Medical College & Safdarjang Hospital, Delhi. She has more than eight years of academic and research experience. She is an active member of Association of Physicians of India and American College of Physicians. She has published a number of research papers in renowned journals and conferences
Affiliations and expertise
Associate Professor, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS), Lohia Hospital, Delhi, IndiaKS
Kah Phooi Seng
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.
Affiliations and expertise
Professor, Xian Jiaotong Liverpool University, Suzhou, China
Adjunct Professor, Queensland University of Technology, Brisbane, AustraliaRead Artificial Intelligence, Machine Learning, and Mental Health in Pandemics on ScienceDirect