Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach

Front Cover
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND MENTAL HEALTH IN PANDEMICS
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND MENTAL HEALTH IN PANDEMICSA COMPUTATIONAL APPROACH
Copyright
Contents
Contributors
Author biographies
Editor biographies
Acknowledgment
Glossary
Introduction
One - Mental health impact of COVID-19 and machine learning applications in combating mental disorders: a review
	1. Introduction
	2. Methods
		2.1 Search strategy
		2.2 Study selection
	3. Findings
		3.1 Overview of article characteristics
		3.2 An assessment of the pandemic's impacts on mental health
			3.2.1 Students
				3.2.1.1 School students
				3.2.1.2 College students
			3.2.2 Working professionals
			3.2.3 Healthcare professionals
			3.2.4 Retired individuals/senior citizen
			3.2.5 Individuals suffering from chronic illness
		3.3 ML application domains in mental health
	4. Challenges of using machine learning in studying mental health
		4.1 Unavailability of large and diverse datasets
		4.2 Real-life implementation of ML models to study mental health
		4.3 Lack of specificity
		4.4 Nonutilization of data that are not in English
		4.5 Lack of data security and unambiguous ethical code
	5. Limitations
	6. Conclusion and future directions
	References
	Further reading
Two - Multimodal depression detection using machine learning
	1. Introduction
	2. Literature survey
		2.1 Post-level analysis
		2.2 User-level behaviors
		2.3 Depression detection using deep learning
		2.4 Selection of words detecting depression
		2.5 Modeling for detecting depression
	3. Social media data
		3.1 Data cleaning and preprocessing
	4. Attributes definition
		4.1 Multimodular attributes
	5. Hybrid deep learner model
		5.1 Model overview
		5.2 Convolutional neural network
		5.3 Bidirectional gated recurrent unit neural network
	6. Experimental setting
		6.1 Evaluation metrics
		6.2 Experimental results
	7. Discussion
	8. Conclusion and future work
	References
Three - A graph convolutional network based framework for mental stress prediction
	1. Introduction
	2. Related work
	3. Overview of graph neural networks
	4. Methodology
		4.1 Experiments
	5. Results
	6. Conclusion
	References
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
		4.1 Emotional stress
			4.1.1 Pre-COVID
			4.1.2 During COVID time
		4.2 Mental stress
			4.2.1 Pre-COVID
			4.2.2 During COVID time
		4.3 Physical stress
			4.3.1 Pre-COVID
			4.3.2 During COVID time
		4.4 Lack of resources
		4.5 Work–life balance
	5. Findings
	6. Suggestions
	7. Limitations
	8. Implications
	9. Conclusion
	References
Five - Impact of COVID-19 on women educators
	1. Introduction
	2. Research methodology
	3. Literature review
		3.1 Teachers with newborn babies
		3.2 Teachers with school-going children
		3.3 Teachers with college-going children
		3.4 Mental health of women educators during COVID-19
		3.5 Technological issues
	4. Findings
	5. Conclusion
	References
Six - A deep learning approach toward prediction of mental health of Indians
	1. COVID-19 and its impact
		1.1 Introduction
	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
		7.1 Convolutional neural network
		7.2 Basic architecture
	8. Survey of online versus offline modes of teaching
		8.1 Design of questionnaires for survey
	9. Prediction of mental health in online mode of teaching using convolutional neural network
		9.1 Recommendations
	10. Summary
	References
Seven - Machine learning based analysis and prediction of college students' mental health during COVID-19 in India
	1. Introduction
	2. Related work
		2.1 Machine-learning-based mental health analysis
		2.2 COVID-19 analysis based on machine learning
	3. Proposed framework
		3.1 Preparing questionnaire
		3.2 Data collection and preprocessing
		3.3 Clustering and label validation
		3.4 Classification models
			3.4.1 Performance measures for classifiers
	4. Data analysis of students
	5. Experimentation and results
		5.1 Clustering
			5.1.1 Cluster analysis
			5.1.2 Validation of k-mean result
		5.2 Classification models
			5.2.1 Experimental setup
			5.2.2 Results and analysis
		5.3 Discussion
	6. Conclusion
	References
Eight - Modeling the impact of the COVID-19 pandemic and socioeconomic factors on global mobility and its effects o ...
	1. Introduction
	2. Background
	3. Methods
		3.1 Data
		3.2 Linear regression
		3.3 Data analyses
	4. Results
		4.1 Impact of COVID-19 and socioeconomic factors on mobility in different regions in India
			4.1.1 Retail and recreation
			4.1.2 Grocery and pharmacy
			4.1.3 Parks
			4.1.4 Transit stations
			4.1.5 Workplaces
			4.1.6 Residential areas
		4.2 Impact of COVID-19 and socioeconomic factors on mobility in different regions of the world
			4.2.1 Retail and recreation
			4.2.2 Grocery and pharmacy
			4.2.3 Parks
			4.2.4 Transit stations
			4.2.5 Workplaces
			4.2.6 Residential
	5. Discussion
	6. Conclusion
	References
Nine - Depression detection: approaches, challenges and future directions
	1. Introduction
	2. Depression
		2.1 Need of automatic depression detection
	3. Depression datasets
	4. Depression detection
		4.1 Questionnaire-based depression detection
		4.2 Psychological-theory-based depression detection
		4.3 Automatic (machine learning) depression detection
		4.4 Microexpressions-based depression detection
	5. Importance of personality in depression detection
	6. State of the art
	7. Discussion
	8. Conclusion and future directions
	References
Ten - Improving mental health surveillance over Twitter text classification using word embedding techniques
	1. Introduction
	2. Related work
	3. Methodology
		3.1 Dataset
		3.2 Text-preprocessing
		3.3 Word vector conversion
			3.3.1 Word embedding
			3.3.2 Word2Vec
			3.3.3 ELMo
			3.3.4 BERT
		3.4 K-means clustering
		3.5 Classification
	4. Evaluation and results
		4.1 Datasets
		4.2 Word embedding-based clustering
		4.3 Similarity measure
		4.4 Evaluation
	5. Conclusion
	References
Eleven - Predicting loneliness from social media text using machine learning techniques
	1. Introduction
	2. Related work
	3. Background
		3.1 Word embedding
		3.2 Word2vec
		3.3 GloVe
		3.4 Classifiers
			3.4.1 Random forest
			3.4.2 XGBoost
			3.4.3 Bi-LSTM
			3.4.4 GRU
	4. Proposed work
	5. Implementation details
		5.1 Dataset used
		5.2 Preprocessing
		5.3 Word embedding used
		5.4 GloVe embedding
		5.5 Word2Vec embedding
		5.6 Classification models
	6. Results and discussion
	7. Conclusion
	References
Twelve - Perceiving the level of depression from web text
	1. Introduction
	2. Related work
	3. Background
		3.1 GloVe
		3.2 BERT
		3.3 RoBERTa
		3.4 DistilBERT
		3.5 XLNet
		3.6 LSTM/BiLSTM
		3.7 GRU/BiGRU
	4. Proposed approach
		4.1 Preprocessing
		4.2 MODEL 1: BERT
		4.3 MODEL 2–4: other transformer-based models
		4.4 MODEL 5: LSTM/BiLSTM
		4.5 MODEL 6: GRU/BiGRU
		4.6 MODEL 7: LSTM with GloVe
		4.7 MODEL 8: GRU with BERT
	5. Results
	6. Conclusion
	References
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
		4.1 The rise of cyber-attacks during COVID-19
		4.2 Cyber threats of COVID-19 vaccines: prevention and containment
		4.3 Challenges in cybersecurity and IoT in the distribution of COVID-19 vaccine
	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
		8.1 Role of Internet of Things in healthcare system
		8.2 Benefits and safety challenges in patient-centric healthcare systems
		8.3 Blockchain technology and its architecture
		8.4 Various frameworks for healthcare systems using blockchain technology
		8.5 Markle root tree (MRT) for COVID-19 vaccination
			8.5.1 The need for Merkle tree
	9. Research challenges in vaccination and its distribution
		9.1 Mental health during COVID-19 times and vaccine for stress relief
		9.2 Vaccination storage facility shortage, Indian scenario and mental health
	10. Conclusion and future directions
	References
Fourteen - A blockchain approach on security of health records for children suffering from dyslexia during pandemic ...
	1. Introduction
	2. Related study
		2.1 Healthcare approach
		2.2 Pandemic survey
		2.3 Blockchain survey-based approach
		2.4 Additional research on children suffering from dyslexia
			2.4.1 Observation from study
	3. Healthcare use cases
		3.1 Sharing of information between traditional care and telemedicine
		3.2 Patient-controlled cancer data sharing
		3.3 Medical insurance claim adjudication
	4. Proposed methodology
	5. Data acquisition
		5.1 Sampling method
		5.2 Methods of collecting data
			5.2.1 Assessment tests
			5.2.2 Surveys
		5.3 Preprocessing of data – I
		5.4 Fuzzy rule-based learning
		5.5 Classification techniques used in model
		5.6 Preprocessing of data – II
		5.7 Secure data in HER using blockchain technology
			5.7.1 User layer
			5.7.2 Blockchain layer
				5.7.2.1 Blockchain assets
				5.7.2.2 Governance rules
				5.7.2.3 Network
			5.7.3 Implementation layer
	6. Implementation
		6.1 Model phase – I
		6.2 Model phase – I
	7. Conclusion
	References
Index
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	M
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	O
	P
	Q
	R
	S
	T
	U
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	X
	Z
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