Note: Intro -- Preface -- Contents -- About the Editors -- Internet of Things and Web Services for Handling Pandemic Challenges -- 1 Introduction -- 1.1 IoT Process to Combat the Covid-19 Pandemic -- 2 IoT for Handling the Pandemic Challenges -- 2.1 Literature Survey -- 2.2 IoT in Personal Medical Devices -- 2.3 In Smart Home IoT -- 2.4 In IT Sector -- 2.5 Challenges of IoT in the Wake of COVID-19 -- 2.6 Uses of IoT for COVID-19 Pandemic -- 2.7 Practices Involved in IoT for Tracking COVID-19 Patients -- 2.8 Global Technological Advancements to Resolve COVID-19 Cases Rapidly -- 3 Web Services for Handling COVID-19 Pandemic Challenges -- 3.1 Accessing COVID-19 Data -- 3.2 Contact Tracing -- 3.3 Support Work from Home Activities -- 3.4 Overcome Challenges in Critical Sectors -- 3.5 Adoption of Microservices Technology -- 4 Conclusion -- References -- Corona Thwack: Socio-Economic Impact of Covid-19 Pandemic in India -- 1 Introduction -- 1.1 Coronavirus: A Bird's Eye View -- 1.2 Covid-19 Acquisition -- 1.3 Covid-19 Complications -- 1.4 Safety Measures -- 2 Pathogenic Diagnosis -- 3 Corona Cure -- 3.1 Treatment -- 3.2 Vaccine: The Ray of Hope -- 4 Covid-19 Pandemic in India -- 4.1 Cataclysm -- 4.2 History -- 4.3 Initiatives -- 4.4 Creating History -- 5 Socio-Economic Impact of Covid-19 in India -- 5.1 Social Impact -- 5.2 Economic Impact -- 6 Conclusion -- References -- Mathematical Modeling on Double Quarantine Process in the Spread and Stability of COVID-19 -- 1 Introduction -- 2 Mathematical Model -- 2.1 Model Analysis -- 2.2 Calculation of R0 2 from SIQ1 R Model -- 2.3 Stability Analysis for SIQ1 R Model -- 2.4 Calculation of Basic Reproduction Number from SQH IQ1 R Model -- 2.5 Stability Analysis for SQH IQ1 R Model -- 2.6 Analysis of Global Stability at Endemic Equilibrium -- 3 Discussion of the Result -- 4 Conclusion -- References. , A Study and Novel AI/ML-Based Framework to Detect COVID-19 Virus Using Smartphone Embedded Sensors -- 1 Introduction -- 2 Smart Phones to Detect COVID-19 -- 3 Combating Covid-19 with Artificial Intelligence/Machine Learning -- 4 Proposed Smartphone Based Framework -- 5 Conclusion -- References -- Transmission Modelling on COVID-19 Pandemic and Its Challenges -- 1 Introduction -- 1.1 Nomenclature -- 2 Model Formations and Basic Assumptions -- 2.1 Mathematical Model -- 2.2 Boundedness and Positivity of the Model -- 3 Stability Analysis and Calculation of the Basic Reproduction Number -- 3.1 Calculation of Basic Reproduction Number -- 3.2 Global Stability -- 4 Discussion of the Result -- 5 Conclusion -- References -- Effect of COVID-19 Pandemic on Mental Health: An Under-Realized Sociological Enigma -- 1 COVID-19 Pandemic Effect on Mental Health: Introduction -- 2 Common Psychological Problems Post COVID-19 -- 2.1 Among General Population -- 2.2 Among COVID-19 Patients -- 2.3 Among Close Relatives and Neighbors -- 2.4 Among Healthcare Workers -- 2.5 Among Geriatric Patients with Co-morbidities -- 3 Psychological Imbalance in Home Quarantine -- 3.1 Some Major Risk Factors of Stressor in Quarantine -- 3.2 How to Minimize the Consequence of Home Quarantine -- 4 Psychological Imbalance in Hospital Quarantine -- 4.1 Psychological Problem During Hospital Quarantine Can Be Categories into Two-Part [22] -- 4.2 Exacerbation of Preexisting Psychiatric Conditions -- 4.3 There is Three Risk Factor Related to Exacerbation of Preexisting Psychiatric Condition -- 4.4 How to Minimize the Negative Psychological Effect of Hospital Quarantine -- 5 Psychological Approach to Assess Mental Health Post COVID-19 -- 5.1 Relevance of Yoga on COVID-19 -- 5.2 Use of Digital Platforms -- 5.3 Shielding Measures in Psychiatric Hospitals. , 5.4 Rehabilitation of Mental Health in Times of COVID-19 -- 5.5 Coping Mental Health Issues in the Wake of COVID-19 Pandemic -- 6 Steps to Mitigate Pandemic in Future -- 6.1 Awareness of Situation -- 6.2 Eliminating Sparks of Pandemic -- 6.3 Communication of Risk -- 6.4 Scaling up of Potentials -- 7 Conclusion -- References -- Predicting the COVID-19 Outspread in Andhra Pradesh Using Hybrid Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Deep Learning Techniques -- 3 Methodology -- 3.1 Deep Learning Classifiers -- 3.2 Data Pre-processing -- 3.3 Feature Selection -- 4 Performance Measures -- 4.1 Experiment Setup -- 4.2 Performance Metrics -- 4.3 Performance Results -- 5 Conclusion -- References -- Mental Health Decline During Corona Virus Outbreak -- 1 Introduction -- 1.1 Worldwide Impact -- 1.2 Effect in India -- 1.3 Mental Effects of the Pandemic/Lockdown -- 2 Investigation of Indian Population (Survey Findings) -- 2.1 Review Methodology and Demographics -- 2.2 Vulnerability of Duration -- 2.3 Dread of Contracting Covid-19 -- 2.4 Dread of Job Steadiness/Layoff -- 2.5 Impact of Covid-19 Related Stress on Lifestyle -- 2.6 Lockdown Fatigue -- 3 Effect of Prolonged Lockdown on Stress Levels -- 3.1 Fall in Work-Life Balance -- 3.2 Postponement of Exams -- 3.3 Pay Cut/Job Loss -- 3.4 Net Impact of Stress Through the Lockdown -- 3.5 Post Unlocks Fears -- 3.6 Net Impact on Emotions at the Beginning of Lockdown -- 3.7 City Wise Comparison -- 4 Net Change in Stress Levels in Top Cities, in Comparison to the Whole Country -- 4.1 Top 5 Coping Mechanisms to Deal with Stress During Lockdown -- 4.2 Meditations by Government and Other Organizations -- 4.3 Utilization Surge on Therapy Platforms-27 -- 5 Sample Case Studies -- 5.1 Case 1-Difficulty in Adjusting to Life at Home [21] -- 5.2 Case 2-Compulsive Cleaning. , 5.3 Case 3-Irrational Fear -- 5.4 Case 4-Violent Behavior Toward Parents -- 5.5 Recommendations -- 5.6 Management Strategies -- 6 Conclusion -- References -- Social Challenges and Consequences of COVID-19 -- 1 Introduction -- 2 Total Lockdown -- 3 India's Overstretched Healthcare System -- 4 The Human Face of COVID-19 -- 5 Themes from the Story of the Patient -- 6 The Indigenous Communities -- 7 Developmental Sport and Stability -- 8 The Effect on the Economy -- 9 Challenges in Socio-Culture -- 10 Urban Migrant Workers Plight -- 11 Dissemination of Misinformation -- 12 The Way Forward -- 13 Social Unrest Management -- 14 Completion -- References -- Economic Impact and Measures of Corona Regime -- 1 Introduction -- 2 Beginning of Economic Slowdown -- 3 International Trade Plunged as the Virus Spread and Projections by Various Agencies -- 4 Growth in Trade of Medical Products -- 5 Impact on Major Economies During the Period -- 6 Turbulence in Global Financial Markets -- 7 Impact of COVID-19 on Indian Economy -- 8 Policy Responses Taken so Far -- 9 Some of the Immediate Measures Included -- 10 How the Story Looks Like Now? -- 11 Debt Sustainability Issues -- 12 Rising Unemployment -- 13 China Leading the New Growth Phases -- 14 Inequality of Income -- 15 Slowdown of Globalization -- 16 Conclusion -- References -- Modeling the Impact of Various Treatment and Prevention Tact's on COVID-19 Worldwide -- 1 Introduction -- 1.1 Bats-Hosts-Reservoir-People -- 1.2 SIR Model -- 1.3 SEIR Model -- 2 Formulation of Mathematical Model -- 3 Basic Reproduction Number -- 4 Summary and Conclusion -- References -- Understanding Emotional Health Sustainability Amidst COVID-19 Imposed Lockdown -- 1 Introduction -- 2 Related Works -- 3 Data and Methodology -- 3.1 Dataset -- 3.2 Two-Way Emotion Characterization -- 3.3 Internet Portal -- 4 Results and Discussion. , 5 Reflections -- References -- Industry 4.0 Technologies and Their Applications in Fighting COVID-19 -- 1 Introduction -- 1.1 Industry 4.0 Overview -- 1.2 Industry 4.0 Technologies -- 2 COVID-19 Challenges -- 2.1 Review of Literature -- 2.2 COVID-19 Challenges in Lockdown Phase -- 2.3 After Lockdown: Unlock 5.0 -- 3 Industry 4.0 Applications in COVID-19 Challenges -- 3.1 Face Mask Detection System for COVID-19 -- 3.2 COVID-19 Self-test Software -- 3.3 Keep Track COVID-19 Positive Patient -- 3.4 Social Distancing Analysis System for COVID-19 -- 3.5 Care of Old Age -- 3.6 COVID-19 Data Analysis -- 4 Conclusion -- References -- Internet of Medical Things (IoMT) Enabled TeleCOVID System for Diagnosis of COVID-19 Patients -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Proposed Design -- 4 Results and Analysis -- 4.1 Results and Analysis of Activity Detection System -- 4.2 Results of the TeleCOVID Diagnostic System -- 5 Conclusion -- References.

Note: Intro -- Preface -- Overview -- Objective -- Organization -- Target Audiences -- Acknowledgements -- Contents -- Editors and Contributors -- Abbreviations -- Deep Learning for Biomedical Engineering and Health Informatics -- MedNLU: Natural Language Understander for Medical Texts -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Word Embedding -- 3.2 Character Embedding -- 3.3 Feature Vector -- 3.4 Bidirectional Long Short-Term Memory (Bi-LSTM) -- 3.5 Conditional Random Fields -- 4 Corpora Statistics -- 5 Experiments and Observations -- 6 Conclusion -- References -- Deep Learning Based Biomedical Named Entity Recognition Systems -- 1 Introduction -- 2 Literature Review -- 3 Architecture -- 3.1 Extraction of Features in Sentence Level -- 3.2 Criteria of Label -- 3.3 Stochastic Gradient -- 4 Experiment -- 5 Result of Experiment and Its Analysis -- 6 Conclusion and Future Scope -- References -- Disambiguation Model for Bio-Medical Named Entity Recognition -- 1 Introduction -- 1.1 Rule-Based Approach -- 1.2 Dictionary-Based Approach -- 1.3 Machine Leaning Based Approach -- 2 Background -- 2.1 Deep Learning Technique -- 3 Methodology -- 4 Evaluation -- 4.1 Dataset Description -- 4.2 Evaluation Metric -- 4.3 Post Processing and Parameters Setting -- 5 Result and Discussion -- 6 Conclusions -- References -- Applications of Deep Learning in Healthcare and Biomedicine -- 1 Introduction -- 1.1 Machine Learning -- 1.2 Artificial Neural Network -- 1.3 Deep Learning -- 2 Deep Learning: Recent Trends -- 2.1 In Non-biological Domains -- 2.2 In Biological Domain -- 3 Applications of Deep Learning in Biomedicine -- 3.1 Biomarkers -- 3.2 Genomic Study -- 3.3 Transcriptomic Analysis -- 3.4 Medical Image Processing -- 3.5 Splicing -- 3.6 Proteomic Study -- 3.7 Structural Biology and Chemistry -- 3.8 Drug Discovery. , 4 Applications of Deep Learning in Health Care -- 4.1 Translational Bioinformatics -- 4.2 Universal Sensing for Health and Wellbeing -- 4.3 Informatics in Medicine -- 4.4 Public Health -- 5 Challenges of Deep Learning in Biomedicine and Healthcare -- 6 Conclusion -- References -- Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare -- 1 Introduction -- 2 Deep Learning and Image Analysis -- 3 DL and Natural Language Processing -- 3.1 Challenges for Using DL for NLP in Healthcare -- 4 DL and Wearable Device Technology -- 5 Issues in Using DL for CDSS -- 6 Future Research Directions -- 7 Conclusions -- References -- Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics -- 1 Introduction to Biomedical and Health Informatics -- 2 Introduction to Recommender System -- 2.1 Application in Healthcare -- 2.2 System Architecture -- 3 Overview of Health Recommender System -- 4 Learning Techniques for Health Informatics -- 4.1 Supervised Learning -- 4.2 Semi-supervised Learning -- 4.3 Unsupervised Learning -- 4.4 Performance Metrics -- 5 Deep Learning for Health Data -- 5.1 Supervised Learning -- 5.2 Unsupervised Deep Learning -- 6 Conclusion -- References -- Deep Learning and Electronics Health Records -- Deep Learning and Explainable AI in Healthcare Using EHR -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Conceptual System Design -- 3.2 Attention Models -- 3.3 GRU: How It Works -- 3.4 LIME Algorithm -- 4 Results and Discussions -- 4.1 Multi-layer Perceptron(MLP) -- 4.2 Random Forest Algorithm -- 4.3 Naive Bayes Algorithm -- 4.4 Results for Attention Mechanisms -- 5 Conclusions -- References -- Deep Learning for Analysis of Electronic Health Records (EHR) -- 1 Introduction -- 2 Electronic Health Record (EHR) Systems -- 3 An Overview of Machine Learning. , 4 Deep Learning and Its Approaches -- 4.1 Multilayer Perceptron (MLP) -- 4.2 Convolutional Neural Networks (CNN) -- 4.3 Recurrent Neural Networks (RNN) -- 4.4 Auto-encoders (AE) -- 4.5 Restricted Boltzmann Machine (RBM) -- 5 Deep EHR Learning Applications -- 5.1 EHR Information Extraction (IE) -- 5.2 EHR Representation Learning -- 6 Interpretability -- 6.1 Maximum Activation -- 6.2 Constraints -- 6.3 Qualitative Clustering -- 6.4 Mimic Learning -- 7 Discussion and Future Prospectus -- References -- Application of Deep Architecture in Bioinformatics -- 1 Introduction -- 1.1 Deep Learning: An Overview -- 1.2 An Overview of Protein Structures -- 2 Deep Learning Approaches for Predicting Protein Structures -- 2.1 Predicting with Long Short Term Memory (LSTM) Network -- 2.2 Deep Supervised and Convolutional Generative Stochastic Networks -- 2.3 Latent Convolutional Neural Networks -- 3 Deep Learning Approach for Protein-Protein Interaction and Protein Function Prediction -- 3.1 Identification of Protein Function Based on Its Structure Using Deep CNN -- 3.2 DL Based PPI Interface Residue Pair Prediction -- 4 DL in Medical Imaging and Disease Diagnosis -- 4.1 Patch-Based CNN Approach for Brain MRI Segmentation -- References -- Intelligent, Secure Big Health Data Management Using Deep Learning and Blockchain Technology: An Overview -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Internet of Things (IoT) -- 3.2 Big Data -- 3.3 Deep Learning -- 3.4 Popular Deep Learners -- 3.5 Applications and Challenges of Deep Learners -- 3.6 Blockchain Technology -- 3.7 Types of Blockchain -- 3.8 Challenges of Blockchain in Healthcare -- 4 System Model -- 5 Open Research Issues -- 6 Conclusion -- References -- Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers -- 1 Introduction -- 2 Background. , 2.1 Convolutional Neural Network (CNN) -- 2.2 Stochastic Gradient Descent (SGD) -- 2.3 RMSprop -- 2.4 Adaptive Moment Estimation (ADAM) -- 3 Automated Diagnosis of Malaria -- 3.1 Image Acquisition -- 3.2 Data Visualization -- 3.3 Data Preprocessing -- 4 Proposed Model -- 4.1 Malaria Detection Using SGD Optimizer -- 4.2 Malaria Detection Using RMSprop Optimizer -- 4.3 Malaria Detection Using ADAM Optimizer -- 5 Comparison of Different Techniques -- 6 Conclusion and Future Work -- References -- Deep Reinforcement Learning Based Personalized Health Recommendations -- 1 Introduction -- 2 Background -- 2.1 Recommendation Systems -- 2.2 Facts and Figures -- 2.3 Big Data -- 2.4 Reinforcement Learning -- 3 Problems -- 3.1 Data Utilization -- 3.2 Health Awareness -- 3.3 Doctor to Patient Ratio -- 3.4 Information Security -- 4 The Limitations of Existing Solutions -- 4.1 Lack of an All-round Solution -- 4.2 System Bias -- 4.3 Myopic Recommendation -- 5 Features of RL that Can Help Solve the Problems -- 5.1 Discounted Future Rewards -- 5.2 Exploration-Exploitation Control -- 5.3 Ability to Learn in Dynamic Environments -- 6 The Proposed Framework -- 6.1 The Data Preprocessing Layer -- 6.2 The Disease Prediction Layer -- 6.3 The Recommendation Generation Layer -- 7 Future Improvements -- 7.1 Actor-Critic Recommendation System -- 7.2 Recommendations -- 7.3 Data Preprocessing -- 7.4 Disease Prediction -- 8 Conclusion -- References -- Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs -- 1 Introduction -- 2 Deep Learning for Natural Language Processing -- 2.1 Distributed Representation -- 2.2 Convolutional Neural Networks (CNN) -- 2.3 Recurrent Neural Networks -- 2.4 Transformer-Based Neural Networks -- 2.5 Generative Adversarial Network -- 3 Major Applications of Deep Learning in Medical Information Processing. , 3.1 Representation Learning (RL) -- 3.2 Information Extraction (IE) -- 3.3 Clinical Predictions (CP) -- 4 Challenges and Remaining Problems -- 5 Conclusion and Direction of Future Research -- References -- Deep Learning for Medical Image Processing -- Diabetes Detection Using ECG Signals: An Overview -- 1 Introduction -- 2 Diabetes -- 2.1 Diabetes and Its Associated Mechanism -- 2.2 Types of Diabetes -- 2.3 Complications Due to Diabetes -- 2.4 Causes (Risk Factors) of Diabetes -- 2.5 Treatment and Management of Diabetes -- 3 Common Methods of Diabetes Detection -- 3.1 Invasive Methods of Diabetes Detection (Blood Testing) -- 3.2 Non-invasive Methods of Diabetes Detection (Using ECG Analysis) -- 4 Machine Learning for Diabetes Detection -- 4.1 Time Domain Methods -- 4.2 Frequency Domain Methods -- 4.3 Wavelet Transform -- 4.4 Nonlinear Methods -- 5 Methodology of Deep Learning Techniques -- 5.1 Autoencoder (AE) -- 5.2 Convolutional Neural Network (CNN) -- 5.3 Recurrent Structures (RNN, LSTM and GRU) -- 5.4 Hybrid of CNN-RNN, CNN-LSTM, CNN-GRU -- 6 Literature Survey -- 6.1 Earlier Methods of Analysis of HRV Signals -- 6.2 Previous Works of Diabetes Detection Using Heart Rate (Including Machine Learning Based) -- 6.3 Deep Learning Based Diabetes Detection Works Using HRV -- 7 Architecture and Implementation of Deep Learning Architecture-Sample Study -- 8 Deep Learning in Big Data Analysis: Limitations and Challenges -- 9 Conclusion -- References -- Deep Learning and the Future of Biomedical Image Analysis -- 1 Introduction -- 1.1 Deep Learning -- 1.2 Biomedical Imaging -- 1.3 Role of Deep Learning in Diagnosis from Various Medical Images -- 1.4 Applications -- 2 Deep Learning in Medical Imaging -- 2.1 Classification -- 2.2 Detection -- 2.3 Segmentation -- 2.4 Registration -- 2.5 Other Tasks in Medical Imaging. , 3 Future of Deep Learning in Biomedical Imaging.

Note: Intro -- Preface -- Contents -- Editors and Contributors -- Data Visualization in the Transformation of Healthcare Industries -- 1 Introduction -- 1.1 Reasons Behind the Use of Data Visualization Tools in Health Care -- 1.2 Key Benefits of Medical Dashboards for Healthcare Providers -- 2 Data Visualization Tools and Applications -- 2.1 Visualization Tool Types and Advantages -- 2.2 Examples of Data Visualization Tools -- 3 How Data Visualization Has Transferred the Healthcare Industry -- 3.1 Infographics -- 3.2 Control Panel Analytics -- 3.3 Customized Data Visualization in Healthcare Industries -- 3.4 Interactive Widgets -- 4 Recent Advancements in Data Visualizations of the Medical Care -- 4.1 Healthcare Dashboards -- 4.2 Global Health Parameters -- 4.3 Visualization Tools for Health Score Computation -- 5 Safety and Security Issues with These Tools -- 6 Conclusions -- References -- Emerging Healthcare Problems in High-Dimensional Data and Dimension Reduction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Principle Component Analysis (PCA) -- 3.2 Linear Discriminant Analysis (LDA) -- 3.3 tDistributed Stochastic Neighborhood Embedding (t-SNE) -- 3.4 Singular Value Decomposition (SVD) -- 4 Result and Discussion -- 5 Conclusion -- References -- Applications of Fuzzy Logic in Bioinformatics: A Survey -- 1 Introduction -- 2 A Prolegomena to FRB Models and FRBD Models -- 3 Basic Structure of FRB Models -- 3.1 Linguistic Variables -- 3.2 If-Then Fuzzy Rules -- 3.3 Inference Procedure in FRB-Models -- 3.4 Defuzzification Methods -- 4 Basic Structure of the FRBD Model -- 5 Example of Some Models Based on FRB Models and FRBD Models -- 6 Discussion and Conclusion -- References -- Disease Prediction Using Data Mining and Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Naive Bayes -- 3.2 Decision Tree. , 3.3 Logistic Regression -- 3.4 Random Forest -- 3.5 Convolutional Neural Networks -- 3.6 Recurrent Neural Networks -- 4 Results -- 4.1 Cancer Data Set -- 4.2 Brain Tumor Data Set -- 5 Conclusion -- References -- Spatial Contextual Thresholding Technique: A Case Study to Detect Nodule of Thyroid in Ultrasound Images -- 1 Introduction -- 2 Material and Techniques -- 2.1 Image Dataset -- 2.2 Evaluation Metrics -- 2.3 Techniques -- 3 Results and Discussion -- 4 Conclusion and Future Scope -- References -- Cognitive Intelligent Healthcare (CIH) Framework by Integration of IoT with Machine Learning for Classification of Electroencephalography (EEG) -- 1 Introduction -- 2 Related Works -- 3 Aim -- 4 Objective -- 5 Existing System -- 5.1 Disadvantages of Existing System -- 6 Internet of Things: Problems and Challenges -- 7 Proposed Methodology -- 7.1 System Model -- 7.2 Cognitive Intelligent Healthcare (CIH) Framework -- 7.3 Pulse Sensor -- 7.4 Oxygen Monitoring Sensor -- 7.5 Arduino Uno Control Unit -- 7.6 Pre-processing of EEG Signal -- 7.7 Feature Extraction and Feature Selection -- 7.8 Logistic Regression (LR) Model for EEG Classification -- 8 Results and Discussion -- 9 Conclusion -- References -- Prognostic Modeling with the Internet of Healthcare Things Applications -- 1 Introduction -- 2 IoT in Healthcare -- 3 Prognostic Modeling (PM) and Prognostic Health Modeling (PHM) with IoT -- 3.1 Role of ML and DL Techniques -- 3.2 Role of Artificial Intelligence (AI) -- 3.3 Role of Big Data and Cloud Computing (CC) -- 4 Application of Prognostic Modeling -- 5 Advantages -- 6 Limitations -- 7 Conclusion -- 8 Future Prospect -- References -- Cancer Tissue Segmentation in Various Conditions with Semiautomatic and Automatic Approach -- 1 Introduction -- 1.1 Medical Image Analysis -- 1.2 Segmentation -- 2 Basic Segmentation Methods -- 2.1 Point Detection. , 2.2 Line Detection -- 2.3 Edge Detection -- 2.4 Edge Processing and Boundary Recognition -- 2.5 Thresholding -- 2.6 Region-Based Segmentation -- 2.7 Template Matching -- 2.8 Texture Segmentation -- 3 Semiautomatic Segmentation -- 3.1 Random Forest -- 3.2 Graph-Cut Method -- 3.3 Random Walk Algorithm -- 3.4 Atlas-Based Methods -- 4 Fully Automatic Segmentation -- 4.1 Supervised Learning Algorithms -- 4.2 Unsupervised Learning Algorithms -- 4.3 Deep Learning Methods -- 5 Conclusion -- References -- Diabetes Prediction Using Machine Learning Approaches -- 1 Introduction -- 1.1 Different Types of Diabetes -- 1.2 Machine Learning Approaches -- 1.3 Objective of This Study -- 2 Related Works -- 3 Background -- 3.1 Description of Classifier Models -- 3.2 Performance Evaluating Metrics -- 4 Dataset Used -- 5 Experimental Results -- 6 Conclusions -- References -- Prediction of Pneumonia from Chest X-Ray Images Using Pre-trained Convolutional Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Pre-trained Convolutional Neural Network Model -- 4 Experimental Results and Discussions -- 4.1 About Datasets -- 4.2 Performance Evaluation on Benchmark Datasets -- 5 Conclusions -- References -- Early Screening of COVID-19 from Chest CT Using Deep Learning Technique -- 1 Introduction -- 1.1 History -- 1.2 Epidemiology and Pathogenesis -- 1.3 Clinical Features -- 1.4 Treatment -- 1.5 Prevention -- 2 Introduction to Deep Learning -- 2.1 History -- 2.2 Architecture -- 2.3 Applications -- 3 Proposed Method -- 3.1 Methodology -- 4 Process -- 4.1 Segmentation of COVID-19 from CT Scans -- 4.2 Details of the Architecture -- 4.3 Segmentation -- 4.4 Performance Measurement -- 5 Result and Discussion -- 6 Conclusion -- References -- A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms. , 1 Introduction -- 1.1 Big Data Computational Epidemiology -- 1.2 Related Work -- 2 Materials and Methods -- 2.1 Data Set -- 2.2 Proposed Methodologies -- 3 Application of Machine Learning Techniques to Cholera Outbreak Prediction -- 4 Application of LSTM to Cholera Outbreak Prediction -- 4.1 Adam -- 4.2 Stochastic Gradient Descent -- 4.3 Stochastic Gradient Descent with Momentum -- 4.4 Root Mean Square Propagation -- 4.5 Gradient Clipping -- 4.6 L2 Regularization -- 4.7 Performance Evaluation Metrics -- 5 Result and Discussion -- 5.1 Performance Metrics Using Machine Learning Algorithms -- 5.2 Performance Metrics Using Long Short-Term Memory Algorithm -- 6 Conclusion -- References -- Clinical Decision-Making and Predicting Patient Trajectories -- 1 Introduction -- 1.1 Steps in Predictive Analysis -- 1.2 Descriptive Analytics: Insight into the Past -- 1.3 Predictive Analytics: Understanding What Is to Come -- 1.4 Prescriptive Analytics: Understanding What's to Return -- 1.5 Prescriptive Analytics: Advice on Possible Outcomes -- 2 Technology and Development -- 2.1 Systems (Precision) Medicine -- 2.2 Personal Medicine -- 2.3 Population Health and Risk Scoring -- 2.4 Integrated Care -- 3 Advantages -- 3.1 Digital Health Tools -- 3.2 Enhanced Clinical Predictive Modeling -- 3.3 Computer-Aided Diagnosis (CAD) -- 3.4 Localization and Segmentation -- 3.5 Feature Extraction and Classification -- 4 Disease Diagnosis and Prognosis -- 4.1 Machine Learning (ML) in Medicine -- 5 Classical ML Versus Deep Learning Methods -- 5.1 State-of-the-Art ML for Cancer Diagnosis/Prognosis -- 6 Predicting Cancer Survival -- 6.1 Deep Learning and Cancer -- 6.2 Model Development and Validation -- 6.3 Model Development -- 6.4 In Practice -- 7 AI in Clinical Healthcare Delivery -- 7.1 Clinical Decision Support -- 7.2 Image Processing -- 7.3 Health Systems. , 7.4 Readiness and Governance -- 8 Medical Prediction -- 8.1 Improve Results -- 9 Cohort Treatment -- 10 Electronic Decision -- 11 Disease-Based Prediction -- 11.1 Inflectional Disease -- 11.2 Heart -- 11.3 Kidney (CKD) -- 11.4 Breast Cancer -- 11.5 Cervical Cancer -- 11.6 Drug Treatment -- 11.7 Technical Implementation-Simulation -- 12 Discussions -- References.

Note: Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Authors -- 1. Introduction to Bioinformatics -- 1 Introduction to Bioinformatics -- 1.1 Importance of Bioinformatics -- 1.2 DNA, RNA and Protein Sequences -- 1.2.1 DNA -- 1.2.2 RNA -- 1.2.3 Protein -- 1.3 Central Dogma of Molecular Biology -- 1.4 The Folding Problem -- 1.5 Genomics and Proteomics -- 1.5.1 Genomics -- 1.5.2 Proteomics -- Appendix -- 2. Computational Biology -- 2.1 Sequence Alignment -- 2.2 Global Sequence Alignment -- 2.3 Local Sequence Alignment -- 2.4 Multiple Sequence Alignment -- Appendix -- Appendix -- 3. Phylogenetics -- 3.1 Introduction to Phylogenetics -- 3.1.1 Rooted Phylogenetic Tree -- 3.1.2 Unrooted Phylogenetic Tree -- 3.2 Molecular Phylogenetics -- 3.3 Phylogenetic Trees and Their Construction -- 3.4 Distance Matrix Methods of Phylogenetic Tree Construction -- 3.4.1 Unweighted Pair Group Method with Arithmetic Mean (UPGMA) - Distance Matrix Method -- 3.4.2 Neighbor Joining Method - Distance Matrix Method -- 3.5 Maximum Likelihood - Character-Based Method for Phylogenetic Tree Construction -- Appendix -- 4. RNA -- 4.1 Structure of RNA -- 4.1.1 Primary Structure of RNA -- 4.1.2 Secondary Structure of RNA -- 4.1.3 Tertiary Structure of RNA -- 4.2 Types of RNA -- 4.3 Functions of RNA -- Appendix -- 5. Pseudoknot -- 5.1 Definition -- 5.1.1 Pseudoknot vs Knot -- 5.1.2 Example of Pseudoknot -- 5.2 Biological Significance of RNA Pseudoknot -- 5.3 Representations of a Pseudoknot -- 5.3.1 Planar Graph Representation -- 5.3.2 Circular Representation -- 5.3.3 Dot Bracket Representation -- 5.3.4 Arc Representation -- 5.4 Types of Pseudoknots -- 5.4.1 H-Type Pseudoknot -- 5.4.2 H-H Type Pseudoknot -- 5.4.3 H-L Type Pseudoknot -- 5.4.3.1 H-Lout Type Pseudoknot -- 5.4.3.2 H-Lin Type Pseudoknot -- 5.4.4 L-L Type Pseudoknot. , 5.4.5 Kissing Hairpin Pseudoknot or H-H-H Type Pseudoknot -- 5.4.6 Three-Knot Pseudoknot -- 5.4.7 Closed Five-Chain Pseudoknot -- 5.4.8 Multiloop Pseudoknot -- 5.4.9 I-Type Pseudoknot -- 5.4.10 B-Type Pseudoknot -- Appendix -- 6. Pseudoknot Prediction Techniques -- 6.1 Dynamic Programming Technique -- 6.2 Comparative Sequence Analysis Technique -- 6.3 Heuristics -- 6.4 Formal Grammar Technique -- 6.5 Integer Programming Technique -- 6.6 Inverse Folding Technique -- Appendix -- 7. Pseudoknot Grammar -- 7.1 Chomsky Hierarchy of Grammars -- 7.2 Context Free Grammar -- 7.3 Parallel Communicating Grammar System -- 7.4 Context Sensitive Grammar -- 7.5 Pair Stochastic Tree Adjoining Grammar -- 7.5.1 Substitution -- 7.5.2 Adjunction -- 7.6 Multiple Context Free Grammar -- 7.7 Path Controlled Grammar -- Appendix -- 8. New Areas of Bioinformatics -- 8.1 Drug Target Identification -- 8.1.1 Target Identification and Validation -- 8.1.2 Tools for Target Identification and Validation -- 8.1.3 Target Validation -- 8.2 Nutrigenomics -- 8.2.1 Prospects and Applications of Nutrigenomics -- 8.3 Toxicogenomics -- 8.4 Bioterrorism -- 8.4.1 Category A Bioterrorism Agents -- 8.4.2 Category B Bioterrorism Agents -- 8.4.3 Category C Bioterrorism Agents -- 8.4.4 Why Are Biological Weapons or Bioterrorism Agents So Disruptive? -- 8.4.5 Effects of Biological Attack -- Appendix -- Glossary -- Important Databases -- References -- Index.

Note: Intro -- Preface -- Contents -- Editors and Contributors -- 1 Mitigate the Impact of Covid-19: Telehealth -- 1 Introduction -- 1.1 Healthcare Infrastructure in India -- 1.2 Telehealth -- 1.3 Telemedicine -- 1.4 Why Telehealth Has Come of Age Now -- 2 Telemedicine Solution eSanjeevaniOPD -- 2.1 Overview of Architecture -- 2.2 The Salient Features of Telehealth Facility -- 3 Detailed Architecture of Telehealth Model -- 3.1 Presentation Layer -- 3.2 Application Layer -- 3.3 Internal Storage -- 3.4 Allocation of Resources for the Developed Solution -- 3.5 eSanjeevani Uses Vertical Scalability -- 3.6 Importance of Cross Platform Testing -- 3.7 Interoperability -- 4 Comparative Strengths of Different Modes of Communications During Covid-19 -- 5 Conclusion -- References -- 2 Epidemic Models in Prediction of COVID-19 -- 1 Introduction -- 2 Time Series Models -- 2.1 ARMA Modeling -- 2.2 ARIMA Modeling -- 3 Epidemic Models with Mathematics -- 3.1 SEIR Compartmental Model -- 3.2 Models with Control Information -- 3.3 Spatial Models -- 3.4 Spatio-temporal Models -- 3.5 Markov Chain Models -- 4 Machine Learning and Deep Learning Models -- 4.1 Fb-Prophet Model -- 4.2 MIT Machine Learning Model to Predict Quarantine Outcome -- 4.3 Deep Learning Models for COVID-19 -- 4.4 Advantages of ML and Deep Learning Models in COVID-19 Prediction -- 5 Conclusions -- References -- 3 SIQRS Epidemic Modelling and Stability Analysis of COVID-19 -- 1 Introduction -- 2 Mathematical Model -- 3 Numerical Simulation and Discussion of the Results -- 4 Conclusion -- References -- 4 Mental Health Analysis of Students in Major Cities of India During COVID-19 -- 1 Introduction -- 2 Related Work -- 3 Details of Data -- 3.1 Study Location -- 3.2 Data Collection -- 3.3 Data Model -- 4 Sentimental Analysis of Tweets to Estimate the Severity of Psychological Stress. , 5 Analysis of Depressing Tweets -- 5.1 Depressing and Non-depressing Tweets -- 5.2 Depression Dynamics in Major Cities Along with Divisions of Rajasthan -- 5.3 Stress Level Versus Recovered COVID-19 Cases -- 5.4 Psychological Stress Level in Major Cities of India -- 6 Stress Dynamics in Students Due to COVID-19 and Lockdown -- 6.1 Dynamics of Students Tweets -- 6.2 Sentimental Analysis of Students Tweet -- 6.3 Stress Level Among Students in Different Cities of India -- 7 Conclusions -- References -- 5 Analyzing the Impact on Online Teaching Learning Process on Education System During New Corona Regime Using Fuzzy Logic Techniques -- 1 Introduction -- 1.1 Impact of COVID-19 in Education Sector -- 1.2 Fuzzy Logic -- 2 Methodology -- 2.1 Sample and Procedure -- 2.2 Normalized Weight of Skill Sets (NWS) and Synthesis of Preferences Using Analytic Hierarchy Process (AHP) -- 2.3 Fuzzy Logic in Evaluation Process -- 2.4 Implementation Process -- 3 Results and Discussion -- 4 Future Scope -- 5 Conclusion -- References -- 6 Healthcare 4.0 in Future Capacity Building for Pandemic Control -- 1 Introduction -- 2 Evolution of Healthcare -- 2.1 Digital Healthcare -- 2.2 Transition from Digital to Advanced Healthcare Systems -- 3 Healthcare in the Era of Pandemic -- 3.1 Challenges with Respect to COVID Patients -- 3.2 Challenges with Respect to Non-COVID Patients -- 4 Technology-Driven Health Care-Preparing for the Next-Generation Health Care -- 4.1 Technological Solutions to Deal with COVID Patients -- 4.2 Technological Solutions to Deal with Non-COVID Patients -- 5 Telemedicine-A Priceless Boon for Healthcare System -- 5.1 Telemedicine for Tacking Pandemic -- 5.2 Telemedicine Initiative in Various Countries -- 5.3 Future of Telemedicine -- 6 Discussion -- 7 Conclusion -- References -- 7 Preventive Behavior Against COVID 19: Role of Psychological Factors. , 1 Introduction -- 1.1 Preventive Health Behavior and COVID 19 Pandemic -- 1.2 Risk Communication -- 1.3 Hygiene Practices -- 1.4 Social Distancing -- 2 Psychological Factors and Preventive Health Behavior -- 2.1 Health Belief Model -- 2.2 Protection Motivation Theory -- 2.3 Theory of Planned Behavior -- 3 Risk Perception and COVID 19 -- 3.1 Risk Perception and COVID 19 -- 4 Adherence to Preventive Behavior -- 4.1 Factors Associated with Non-adherence -- 5 Conclusion -- References -- 8 Impact of the COVID-19 on Consumer Behavior Towards Organic Food in India -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 3.1 Survey Method and Research Design -- 3.2 Questionnaire Development -- 3.3 Reliability and Validity of Questionnaire -- 3.4 Respondents Profile -- 4 Analysis and Result -- 4.1 Factors Discussion -- 4.2 Demographic Impact on Consumer Behavior Towards Organic Food -- 4.3 Income (Refer Table 6) -- 4.4 Education (Refer Table 7) -- 4.5 Age (Refer Table 8) -- 4.6 Profession (Refer Table 9) -- 4.7 Overall Frequencies and Agreement & -- Non-agreement Percentage -- 4.8 Discussion -- 4.9 Managerial Implication -- 4.10 Limitation and Further Scope of Study -- 5 Conclusion -- References -- 9 Socioeconomic Impacts and Opportunities of COVID-19 for Nepal -- 1 Introduction -- 2 Impacts of COVID-19 on Socioeconomic and Health Sectors -- 2.1 Sectors of Gross Domestic Production (GDP) and Employment Situation -- 2.2 Workers in the Informal Sector -- 2.3 Tourism -- 2.4 Agriculture and Livestock Sector -- 2.5 Remittances -- 2.6 Inflation and Food Commodities -- 2.7 Poor, Vulnerable Community and Women -- 2.8 Education -- 2.9 Health Sector -- 2.10 Social and Mental Stress -- 3 Discussion for Opportunities in Terms of Rebuild and Reforms on Socioeconomic and Health Sectors -- 3.1 The Rebuilding of Agriculture and Tourism Sectors. , 3.2 Reforms of the Public Health System and Education -- 4 Conclusion and Implication -- References -- 10 Strategic Decision in Long and Short Run for Cross-Country Commodity Market in the Post-COVID 19 Era -- 1 Introduction -- 2 Research Methodologies -- 3 Data Analysis and Interpretation -- 4 Conclusion -- References -- 11 Prediction of Novel Coronavirus (nCOVID-19) Propagation Based on SEIR, ARIMA and Prophet Model -- 1 Introduction -- 2 Related Work -- 3 SEIR Mathematical Modeling Based on Social Distancing -- 3.1 Social Distancing Applied for Coronavirus -- 4 Time Series Analysis of Corona -- 4.1 ARIMA -- 4.2 Prophet Time Series -- 5 Discussion -- 6 Conclusion -- References -- 12 Innovative Strategies to Understand and Control COVID-19 Disease -- 1 Introduction -- 2 Potential Strategies for Study and Diagnosis of Disease -- 2.1 Bioelectronics and Biosensors -- 2.2 Artificial Intelligence -- 2.3 Mathematical Modeling -- 2.4 3D Printing -- 2.5 Big Data -- 2.6 Robotics -- 2.7 Theranostic Nanoparticles -- 3 Current Strategies Used for the Treatment of COVID-19 -- 3.1 Immunological Approaches -- 3.2 Treatment Based on Vaccination -- 3.3 Antiviral Drugs -- 3.4 Indian Medicinal Plants Against SARS-CoV-2 -- 4 Conclusion and Future Recommendations -- References -- 13 An Investigation on COVID 19 Using Big Data Analytics and Artificial Intelligence -- 1 Introduction -- 1.1 Big Data -- 1.2 Data Analytics -- 1.3 Covid-19 -- 2 Characteristics of Big Data -- 3 Big Data on COVID-19 -- 4 Data Analytics on COVID-19 -- 4.1 Logistic Regression Analysis -- 4.2 Kaplan-Meier Analysis -- 4.3 Compartmental Models -- 4.4 Other Applications of Big Data on COVID-19 -- 5 Comparative Summary -- 6 Challenges Faced -- 6.1 Big Data and Economic Challenges -- 7 Conclusion -- References -- 14 Rise of Online Teaching and Learning Processes During COVID-19 Pandemic. , 1 Introduction -- 2 A Brief Literature Survey -- 3 Features of Online Teaching and Learning Process: The Pros and Cons -- 3.1 Effectiveness of Online Teaching/Learning as Compared to Classroom Teaching/Learning -- 3.2 Infrastructure Requirements for Online Teaching/Learning -- 3.3 Challenges of Online Teaching/learning and How Do Deal with Them -- 3.4 Skills Required by a Teacher for the Online Teaching Process -- 3.5 Moral Duties of Students and Parents to Support Online Teaching Methodologies -- 3.6 Potential Consequences of Online Education Model on Health -- 3.7 Scope of Online Teaching/learning in the Post-Pandemic World -- 4 Comparative Analysis of Digital Tools Available to Aid Online Teaching and Learning Process -- 4.1 Google Classroom -- 4.2 Zoom App -- 4.3 Webex -- 4.4 Google Meet -- 4.5 Microsoft Teams -- 5 Online Teaching and Learning Practices During COVID-19 Pandemic in India: A Brief Online Survey -- 5.1 Data Sampling -- 5.2 Data Summarization and Analysis -- 6 Conclusions -- Appendix -- A.1 SHAGUN -- A.2 DIKSHA -- A.3 e-Pathshala -- A.4 National Repository of Open Educational Resources (NROER) -- A.5 SWAYAM -- A.6 NPTEL -- References -- 15 Robotic Technology for Pandemic Situation -- 1 Introduction -- 2 Robotic Technologies -- 3 Automatic Vacuum Cleaner Robots -- 3.1 Components of the Cleaning Robot System -- 4 Smart Wet Cleaning Robots -- 5 Robotic Nursing Assistant -- 5.1 Dinsow -- 5.2 Paro -- 5.3 Pepper -- 5.4 Trina -- 6 Hospi Robot -- 7 Helpmate Robot -- 8 Automated Guided Vehicle -- 9 Delivery Robots -- 10 Delivery Drone Robot -- 11 Sanitizing Drone Robot -- 12 Conclusion -- References -- 16 Face Mask Detection Using AI -- 1 Introduction -- 1.1 Application Areas of Face Mask Detection System -- 2 Artificial Intelligence (AI)-The Right Way Solution -- 3 Deep Learning -- 4 Deep Learning in Face Mask Detection. , 5 Face Mask Detection-A Simple Implementation Using OpenCV.

Note: Intro -- Preface -- Contents -- About the Editors -- The Rudiments of Energy Conservation and IoT -- 1 Introduction -- 2 Paradigmatic View of Energy-Efficient IoT -- 3 Pragmatic Energy-Efficient IoT System Architecture -- 4 Issues of Energy Conservation in IoT -- 5 Energy Conservation Approaches for IoT Devices and Its Perspectives -- 5.1 Node Activity Management -- 5.2 Data Aggregation and Transmission Process -- 5.3 Media Access Control (MAC) Protocol -- 5.4 Security Management -- 5.5 Topology Management -- 5.6 Routing -- 6 Energy-Efficient System Design for IoT Devices -- 7 Conclusions -- References -- Existing Enabling Technologies and Solutions for Energy Management in IoT -- 1 Introduction -- 2 Architectures of IoT -- 2.1 Three-Layer Architecture -- 2.2 Four Layer Architecture -- 2.3 Five-Layer Architecture -- 3 Components of IoT -- 3.1 Identification -- 3.2 Sensing -- 3.3 Communication -- 3.4 Computation -- 3.5 Services -- 3.6 Semantics -- 4 Applications -- 4.1 Home Automation -- 4.2 Health care -- 4.3 Transportation -- 4.4 Logistics -- 4.5 Smart Environment and Agriculture -- 5 Challenges in IoT -- 6 Energy Management -- 6.1 Energy Harvesting -- 6.2 Energy Conservation -- 7 Research Directions -- 8 Conclusion -- References -- Energy-Efficient System Design for Internet of Things (IoT) Devices -- 1 Introduction -- 2 Operation -- 3 Energy Conservation -- 3.1 Solar Energy Harvesting -- 3.2 Thermal Energy Harvesting -- 3.3 Vibrational Energy Harvesting -- 3.4 Electrostatic Energy Harvesting -- 3.5 Wind Energy Harvesting -- 3.6 RF Energy Harvesting -- 4 Harvesting Module -- 4.1 Rectenna Model -- 4.2 Sensing Antenna -- 4.3 DC-DC Converter -- 4.4 Power Management Unit -- 5 Wireless Energy Harvesting -- 5.1 Near Field Communication -- 5.2 Inductive Coupling -- 6 Applications -- 6.1 Home Appliances -- 6.2 Healthcare Devices. , 6.3 Automatic Vehicles -- 6.4 Business Infrastructure -- 6.5 Farming and Poultry -- 6.6 Smart Utilities -- References -- Models and Algorithms for Energy Conservation in Internet of Things -- 1 Introduction -- 2 Data Centers -- 2.1 Big Data -- 2.2 Cloud Computing -- 3 Virtualization -- 4 Load Balancing -- 4.1 Hardware Versus Software Load Balancing -- 5 Energy Consumptions in Data Centers -- 5.1 Green Computing -- 5.2 Power Calculation at Data Center -- 6 Static Energy-Efficient Algorithms -- 6.1 Exact Allocation Algorithm -- 6.2 Best Fit Heuristic Algorithm -- 7 Dynamic Energy-Efficient Algorithms -- 7.1 Hardware Level Solution -- 7.2 Software Level Solution -- 8 Summary -- References -- An Energy-Efficient IoT Group-Based Architecture for Smart Cities -- 1 Introduction -- 2 Related Work -- 3 System Description -- 3.1 The WSN for e-Health and Human Well-Being Monitoring -- 3.2 Utilities Monitoring Systems -- 3.3 Air Quality and Climate Monitoring Systems -- 3.4 Emergency Situations Monitoring -- 3.5 Other Systems -- 4 Proposed Architecture for the Smart City -- 5 Conclusion and Future Work -- References -- Context-Aware Automation Based Energy Conservation Techniques for IoT Ecosystem -- 1 Introduction -- 1.1 Communication Technologies -- 1.2 Pricing Policies -- 2 Introduction -- 3 Related Work -- 3.1 Demand-Side Management -- 3.2 Usage of Renewable Energy Source -- 3.3 Context-Aware Automation -- 3.4 Feedback-Based Automation -- 4 Case Studies -- 5 Proposed Framework -- 6 Future Directions and Challenges -- 7 Conclusion -- References -- Energy Conservation in IoT-Based Smart Home and Its Automation -- 1 Introduction -- 2 Electrical Network End-to-End System -- 2.1 Generation -- 2.2 Transmission and Distribution -- 2.3 Automation in Demand, Supply, and Monitoring -- 2.4 Load Shedding and Control -- 3 Causes of Energy Losses and Preventive Actions. , 3.1 Electrical Network Improvement -- 3.2 Smart Energy Monitoring Devices -- 4 Automation and Control in Electrical Network -- 4.1 Automation Devices -- 4.2 Standards for Automation Devices -- 4.3 Communication Hardware and Automation Protocols -- 5 Energy Conservation Key Area -- 5.1 Smart Buildings -- 5.2 Smart Homes -- 5.3 Smart Appliances -- 6 Energy Conservation in Smart Home and IoT -- 6.1 Automation and Sensors in Smart Home -- 6.2 Industry Trends and Present Technology -- 6.3 Energy Conservation Components of Smart Home -- 6.4 Renewable Energy Sources with IoT in Smart Home -- 7 Artificial Intelligence in Energy Conservation-Methods and Technology -- 7.1 Digital Signal Processing and IoT -- 7.2 Artificial Intelligence in Smart Home -- 8 Cloud Data Processing Using IoT Devices -- 9 Conclusions -- References -- IoT Architecture for Preventive Energy Conservation of Smart Buildings -- 1 Introduction -- 1.1 Prevalent Smart Components -- 1.2 IoT System Architectures -- 1.3 Smart Buildings -- 1.4 Energy Efficiency in Smart Building IoT Systems -- 2 Requirements and Approaches for Energy Efficiency in Smart Buildings -- 2.1 Requirements for Environmental Conservation -- 2.2 Requirement for Energy Modeling -- 2.3 Requirement for Energy Consumption Monitoring and Evaluation -- 3 Existing Application Architectures -- 3.1 Smart Energy Metering Architectures -- 3.2 Smart Lighting Architectures -- 3.3 Energy Management Interfaces for Buildings -- 3.4 Energy-Efficient Smart Building Automation Architectures -- 3.5 Energy-Efficient Implementations in Smart Grid -- 3.6 Energy-Efficient Comfort Management Systems in Smart Buildings -- 3.7 Energy Monitoring and Saving Methods in Smart Buildings -- 4 Open Challenges and Future Work -- 4.1 Lack of Interoperability for Currently Used Protocols -- 4.2 Need for a Cost-Effective Architecture that Conserves Energy. , 4.3 Integration of Renewable Energy Sources in Smart Buildings -- 4.4 Maintainability of Energy-Efficient Architectures -- 5 Conclusion -- References -- Designing Energy-Efficient IoT-Based Intelligent Transport System: Need, Architecture, Characteristics, Challenges, and Applications -- 1 Introduction -- 1.1 Intelligent Transport System -- 1.2 Motivations for IoT in Transportation -- 1.3 Architecture of ITS -- 2 Key Technologies and Related Power Optimization Bottlenecks -- 2.1 Perception Technology: Precision, Reliability, and Power Constraints -- 2.2 Communication Technology and Related Power Issues -- 2.3 Information Extraction and Underlying Power Issues -- 3 Energy Efficiency Challenges and Corresponding Solutions -- 3.1 Precision, Density, and Reliability of Perception and Smart Sensing Solutions -- 3.2 Information Exchange Based Solutions -- 3.3 Computational Feasibility and Distributed Computing Solutions -- 3.4 Data Collection and Pooling with Energy-Efficient Solutions -- 4 Further Challenges and Opportunities -- 4.1 Further Involvement of Internet of Vehicle (IoV) -- 4.2 Cooperative Automated Vehicle (CAV) Scheme -- 4.3 Utilization of Multiple-Source Data in ITS -- 4.4 Software-Defined Radio (SDR)-Based Communication -- 4.5 Energy Harvesting Corridors -- 5 Conclusion and Future Work -- References -- Capacity Estimation of Electric Vehicle Aggregator for Ancillary Services to the Grid -- 1 Historical Perspective -- 2 Development of Electric Vehicles -- 3 Motivation for Vehicle to Everything (V2X) and V2G Technology -- 4 Electric Vehicles and Solar Power Plants in Smart Grid Environment -- 5 Potential of EV to Grid Connection -- 6 Capacity Estimation of Aggregator -- 7 Battery Management System -- 8 Grid Connection and Performance Testing of V2G -- 9 Commercial Value of V2G -- 10 Challenges and Opportunities -- 11 Discussion and Conclusion. , References -- Need and Design of Smart and Secure Energy-Efficient IoT-Based Healthcare Framework -- 1 Introduction -- 2 Data Generation in IoT Environment -- 3 Applications of IoT -- 4 Publication Trends of IoT -- 5 Critical Human Disorders -- 6 Energy-Efficient IoT Systems (Related Works) -- 7 Role of IoT in Designing Energy-Optimized Systems -- 7.1 Proposed Energy-Efficient IoT-Based Healthcare System for Neurological and Psychological Disorder Patients -- 8 Conclusion -- References -- Medical Information Processing Using Smartphone Under IoT Framework -- 1 Introduction -- 1.1 Motivation -- 1.2 Objectives -- 1.3 Organization of the Chapter -- 2 System Model -- 3 System Requirement -- 4 Importance of Cloud for Smartphone-Enabled IoT -- 5 Internet of Medical Things (IoMT) Using Smartphone -- 6 Biomedical Data Processing -- 6.1 Transmission of Medical Image Signals -- 6.2 Transmission of Biomedical Signals (ECG, EEG, and EMG) -- 6.3 Transmission of Medical Video Signals -- 6.4 Teletrauma System -- 7 Application of IoT -- 7.1 Application Oriented to Health Care -- 8 Application Standards/Protocols Use in IoT (Health Care) -- 9 Challenges -- 10 Conclusion -- References -- Contributing Toward Green IoT: An Awareness-Based Approach -- 1 Introduction -- 2 A Walkthrough of Internets of Things and Its Applications -- 2.1 Challenges of Internet of Things -- 3 Green IoT: An Overview -- 3.1 Smart Homes -- 3.2 Smart Cities -- 3.3 Energy-Efficient Smart Health Care -- 4 Various Approaches to Achieve Green IoT -- 5 Awareness-Based Approach Toward Green IoT -- 5.1 Energy Awareness Campaigns -- 5.2 IoT-Based Smart Metering -- 5.3 Promoting Recycling -- 5.4 Creating Awareness About Green Information Communication Technology -- 5.5 Promoting the Usage of Sensor Cloud: A Step Toward Green IoT. , 6 Creating Awareness Through Prototyping: A Green IoT-Based Smart Home Model.

Note: Intro -- Preface -- Book Contents -- Chapter 1: Organic Semiconductors: Technology and Environment -- Chapter 2: Defining and Visualizing Energy and Environment Related Smart Technologies -- Chapter 3: Energy Minimization in a Sustainably Developed Environment Using Cloud Computing -- Chapter 4: Sensing, Communication with Efficient and Sustainable Energy: An IoT Framework for Smart Cities -- Chapter 5: Existing Green Computing Techniques: An Insight -- Chapter 6: Smart Home for Efficient Energy Management -- Chapter 7: Solar Energy Radiation Forecasting Method -- Chapter 8: Electric Vehicles for Environmental Sustainability -- Chapter 9: Smart Grid: A Survey -- Chapter 10: Green Building: Future Ahead -- Chapter 11: Reliable and Cost-Effective Smart Water Governing Framework for Industries and Households -- Chapter 12: Adaptation of Smart Technologies and E-Waste: Risks and Environmental Impact -- Chapter 13: A Comprehensive Study on the Arsenic Contamination in the Groundwater of Assam and West Bengal with a Focus on Nor... -- Chapter 14: Sustainable Approach for Cloud-Based Framework Using IoT in Healthcare -- Chapter 15: A Case Study on Evaluation of Energy Management System by Implication of Advanced Technology in a Typical Cement F... -- Contents -- Organic Semiconductors: Technology and Environment -- 1 Introduction to Organic Semiconductors -- 1.1 Applications -- 1.2 Limitations -- 2 Types of Organic Semiconductors -- 3 Localisation of Charge -- 3.1 Localisation via Polarization -- 3.2 Localisation via Disorder -- 4 Charge Transport in Organic Semiconductor -- 4.1 Multiple Trapping and Release Model (MTR) -- 4.2 Hopping Model -- 5 Optical Properties of Organic Semiconductors: Excitons -- 6 Energy Level Determination -- 7 Charge Carrier Injection -- 8 Types of Organic Semiconductors -- 8.1 Major Challenges -- 8.2 Ambipolar. , 9 Technology and Environment -- 10 Conclusions -- References -- Defining and Visualizing Energy and Environment Related Smart Technologies -- 1 Introduction -- 2 How Technology Is Making Environment ``Smart´´ -- 2.1 Definition of Smart Environment -- 2.2 Key Features of Smart Environment -- 3 Smart Environment and Pervasive Computing -- 4 Impact of Smart Technology on Environment -- 4.1 Renewable Energy -- 4.2 Direct Air Capture (DAC): Dealing CO2 in Air -- 4.3 Electric Vehicle as a Smart Technology -- 4.4 Smart Technology -- 5 Smart Technology for Energy -- 5.1 Generation of Power -- 5.2 Advantages of Smart Grids During Complex Transmission -- 5.3 Power Distribution -- 6 Smart Technology for Energy and Environment: Future Scope -- 6.1 Green Buildings -- 6.2 Smart Lighting with Smart Grid -- 6.3 Smart Mobility -- 7 Conclusion -- References -- Energy Minimization in a Sustainably Developed Environment Using Cloud Computing -- 1 Introduction -- 1.1 Minimizing the Energy Consumption -- 2 Literature Survey -- 3 Enhancing Energy-Efficiency in a Cloud-Computing Environment -- 4 Measures for Reducing Carbon Emissions -- 4.1 Future Prospects of Sustainable Cloud Computing -- 5 Conclusion -- References -- Sensing, Communication with Efficient and Sustainable Energy: An IoT Framework for Smart Cities -- 1 Introduction -- 2 Literature Review -- 2.1 Smart City -- 2.2 Internet of Things (IoT) -- 2.3 IoT Framework -- 2.4 Data Center and Its Management System -- 2.5 Smart Networks -- 2.6 Energy Conservation in IoT Wireless Networking -- 3 Conceptual Model -- 4 IoT Framework Simulation -- 4.1 RPL-UDP Protocol Simulation -- First Scenario -- Second Scenario -- Third Scenario -- Simulation Results -- 4.2 6lowPAN Protocol Simulation -- Simulation Structure -- CoAP Protocol Simulation -- Simulation Scenario -- Simulation Results -- 5 Conclusion -- 5.1 Future Work. , References -- Existing Green Computing Techniques: An Insight -- 1 Introduction -- 1.1 Green Computing: A Contextual Description of the Concept -- 1.2 Green Computing Goals -- 1.3 Motivations for Green Computing -- 2 Green Design Techniques -- 3 Green Manufacturing Techniques -- 4 Green Utilization Techniques -- 5 Green Disposal Techniques -- 6 Conclusions -- References -- Smart Home for Efficient Energy Management -- 1 Introduction -- 1.1 Definition -- 2 Hardware -- 3 Software -- 3.1 Control Functionality of HEMS -- 4 HEMS Monitoring and Management -- 4.1 Electricity -- 4.2 Solar PV -- 4.3 Battery Storage -- 4.4 Solar Thermal -- 5 HEMS Challenges -- 6 Conclusion -- References -- Solar Energy Radiation Forecasting Method -- 1 Introduction -- 2 Research Motivation -- 3 Solar Radiation Component -- 3.1 Direct Normal Irradiance -- 3.2 Diffuse Horizontal Irradiance -- 3.3 Global Horizontal Irradiance -- 4 Need of Solar Forecasting -- 5 Solar Forecasting Methodologies -- 6 State of Art for Solar Irradiance Forecast -- 6.1 Physical Methods -- Numerical Weather Prediction -- Cloud Imagery and Satellite Models -- 6.2 Statistical Methods -- Time Series Model -- Persistence Model -- Artificial Neural Network -- Support Vector Machine -- Markov Chain -- 7 Empirical Model -- 8 Deep Learning -- 9 Hybrid Method -- 10 Factors Influencing Solar Radiation Forecasting -- 10.1 Input Parameter Selection -- 10.2 Forecast Horizon -- 10.3 Climatic Variability -- 10.4 Night Hour and Normalization -- 10.5 Preprocessing Techniques -- 10.6 Training and Testing Period -- 10.7 Geographical Location -- 11 Solar Forecasting Evaluation Metrics -- 11.1 Contemporary Statistical Metrics -- 12 Conclusion -- References -- Electric Vehicles for Environmental Sustainability -- 1 Introduction -- 2 Electric Vehicles (EVs) -- 2.1 Technical Components of Electric Car -- 2.2 EV Types. , Battery Electric Vehicles (BEV) -- Plug-in Hybrid Electric Vehicle (PHEV) -- Hybrid Electric Vehicles (HEV) -- 3 Impact of Electric Cars on the Environment -- 4 Advantages and Disadvantages -- 4.1 Advantages -- 4.2 Disadvantages -- 5 Market Penetration of Electric Vehicles -- 6 Challenges in Introducing Electric Vehicle Fleets -- 7 Conclusion -- References -- Smart Grid: A Survey -- 1 Introduction -- 2 Smart Grid: Definitions -- 3 Motivation to Build Future Intelligent/Smart Grids -- 4 Characteristics of Smart Grids -- 5 Technologies and Architecture of Smart Grids -- 5.1 Technologies -- Smart Meters -- Automated Meter Reading -- V2G (Vehicle to Grid) -- Smart Sensors -- 5.2 Architecture of Smart Grids -- 6 Goals and Benefits -- 7 The Challenges of Smart Grids -- 8 Conclusion -- References -- Green Building: Future Ahead -- 1 Introduction -- 2 Components of Green Building -- 2.1 Site Planning and Design -- 2.2 Energy -- 2.3 Waste Reduction -- 2.4 Water -- 2.5 Indoor Air Quality -- 2.6 Materials -- 2.7 Commissioning -- 2.8 Marketability -- 2.9 Sustainability -- 3 Health Benefits of Green Buildings -- 4 Green Building Technologies -- 4.1 Solar Power -- 4.2 Biodegradable Materials -- 4.3 Green Insulation -- 4.4 Smart Appliances -- 4.5 Cool Roofs -- 4.6 Sustainable Resource Sourcing -- 4.7 Low-Energy House and Zero-Energy Design -- 4.8 Water Efficiency Technologies -- 4.9 HVAC (Heating, Ventilation and Air Conditioning) -- 4.10 Rammed Earth Bricks -- 4.11 Transportation -- 5 International Rating Systems -- 5.1 LEED -- 5.2 Well -- 5.3 Fitwell -- 5.4 Green Globes -- 6 Challenges Being Faced by Green Building Practices -- 6.1 Limited Awareness -- 6.2 Inadequate Administrative Support -- 6.3 Shortage of Trained and Skilled Manpower -- 6.4 Reduction of Costs of Equipment´s and Products -- 6.5 Non-Financial Incentives -- 7 Future of Green Building. , 7.1 Some Innovations in the Coming Times -- 8 Conclusion -- References -- Reliable and Cost-Effective Smart Water Governing Framework for Industries and Households -- 1 Introduction -- 2 Literature Survey -- 3 Existing System -- 3.1 Working of SCADA System -- 3.2 Drawbacks of SCADA -- Gadget Interconnectivity -- Working Expenses and Costs -- Information Insights -- Adaptability -- 4 Proposed Framework -- 4.1 Water Pipeline Leakage Detection Module -- 4.2 Automatic Water Tank Filling Module -- 4.3 Water Quality Monitoring -- 4.4 Water Consumption Tracking -- 4.5 Block Diagram of Proposed Framework -- 4.6 Framework Components and Description -- GSM (SIM 900a) -- 4.7 Algorithm of Proposed Framework -- 4.8 Flow Chart -- 4.9 System Representation (Fig. 6) -- 4.10 Schematic Diagram (Fig. 7) -- 5 Implementation -- 5.1 Construction -- 6 Results and Discussion -- 7 Conclusion -- 8 Future Scope -- References -- Adaptation of Smart Technologies and E-Waste: Risks and Environmental Impact -- 1 Introduction -- 1.1 Electronic Waste -- 1.2 Components of E-Waste -- 1.3 Adverse Effects of E-Waste -- 2 Issues Regarding E-Waste in Smart Cities -- 3 Approaches to Tackle E-Waste in Smart Cities -- 4 Smart Technologies for E-Waste -- 5 Proposed Solution -- 5.1 Smart Collector Bin -- 5.2 Customer -- 5.3 E-Product -- 5.4 Manufacturer -- 5.5 Retailer -- 5.6 E-Waste Unit -- 5.7 Working -- 6 Conclusion -- References -- A Comprehensive Study on the Arsenic Contamination in the Groundwater of Assam and West Bengal with a Focus on Normalization o... -- 1 Introduction -- 2 Arsenic Affected Areas in the States of Assam and West Bengal -- 3 Methodology -- 4 Remedial Measures to Reduce the Contamination of Groundwater from Arsenic -- 5 Arsenic-Removal Technologies -- 6 Issues Related to Deposition of Arsenic Waste from Arsenic Filters. , 7 Safe Application of Arsenic-Filled Sludge from Arsenic Filters.

Note: Intro -- Preface -- Contents -- Editors and Contributors -- Predictive Analysis of Psychological Disorders on Health -- 1 Introduction -- 2 Classification -- References -- The Rising Aesthetic Concern with Digitalization: Qualitative Evidences from Turkey -- 1 Introduction -- 1.1 Research Design -- 2 Digitalization, Aesthetic Concern, and Aesthetic Surgery -- 3 Turkish Cases: Social Media, Aesthetic Concern and Fake News -- 3.1 Descriptive Findings -- 3.2 A Model: The Link Between Social Media, Aesthetic Concern, and the Desire of Aesthetic Surgery -- 4 Conclusion -- References -- AI-Based Predictive Analytics for Patients' Psychological Disorder -- 1 Introduction -- 2 The Person at Risk of Psychological Disorder -- 3 Classification of Psychological Disorders -- 4 Conclusion -- References -- Monitoring the Impact of Stress on Facial Skin Using Affective Computing -- 1 Introduction -- 2 Background -- 2.1 Dimensional Conceptualization of Emotion -- 2.2 Facial Action Coding System (FACS) -- 2.3 Facial Skin Conditions Due to the Stress Responses -- 3 Literature Review -- 4 Methodology -- 5 Experimental Results and Analysis -- 5.1 Material Used -- 5.2 Results of the Stress in Valence-Arousal (V-A) Space -- 5.3 Experimental Results for Facial Skin Conditions -- 5.4 Overall Evaluation of the Proposed Approach -- 6 Conclusion -- References -- Computational Techniques in Prognostic and Data Modelling of Mentally Ill Patients with Special Emphasis on Post-COVID-19 Scenario -- 1 Introduction -- 1.1 Factors Affecting Mental Health -- 2 Role of Computational Models in Mental Healthcare -- 3 Role of Data Analytical Tools in Mental Healthcare -- 4 Post-Covid Scenario -- 5 Pilot Study -- 6 Role of Prognostic Modelling in Mental Healthcare -- 7 Tele-Healthcare Services in Post-Covid Scenario -- 8 Conclusion and Future Prospects -- References. , Predicting Depression Through Social Media -- 1 Introduction -- 1.1 Social Media Platforms -- 1.2 Depression -- 1.3 Relationship Between Depression and Social Media -- 2 Methods for Prediction -- 3 Sources of Data -- 3.1 Survey Responses -- 3.2 Self-declared Mental Health Status -- 3.3 Forum Data -- 3.4 Annotated Posts -- 4 Future Studies -- 5 Ethical Issues -- 6 Conclusion -- References -- COVID-19 Impact on Online Learning: A Statistical and Machine Learning Model Analysis for Stress Detection -- 1 Introduction -- 2 Predictive Analytics and Its Significance -- 3 Predictive Modeling Approaches -- 3.1 Tree-Based Analysis -- 3.2 Random Forest Technique -- 3.3 Artificial Neural Networks -- 3.4 Stepwise Regression -- 4 Ensembles of Models: Prediction Analytics -- 4.1 Probabilistic Neural Networks (PNN) -- 4.2 Nonlinear AutoRegressive Network with eXogenous Inputs (NARX) -- 4.3 Support Vector Machine (SVM) -- 4.4 Long Short-Term Memory Networks (LSTM) -- 4.5 Multilayer Perceptron (MLP) -- 4.6 Least-Squares Boosting -- 5 Statistical Models and Analysis -- 5.1 ANOVA and MANOVA: Analysis of Variance -- 5.2 Mann-Whitney and Kruskal-Wallis Test -- 5.3 Chi-square Test -- 5.4 Structural Equation Modeling -- 6 Predictive Analysis: Stress Detection Among Design and Technology Student -- 6.1 Participants -- 6.2 Procedure -- 6.3 Measures -- 6.4 Analysis and Results -- 6.5 Discussion -- 7 Conclusions -- References -- Measuring Mental Health at Workplaces Using Machine Learning Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Linear Regression -- 3.2 Logistic Regression -- 3.3 K Nearest Neighbor (KNN) -- 3.4 Decision Tree -- 3.5 Random Forest -- 3.6 Gradient Boosting -- 3.7 Adaptive Boosting -- 4 Results -- 5 Conclusion -- References -- Old Age People Emotional Stress Prediction During Outbreak Using Machine Learning Methods. , 1 Introduction -- 2 Outbreak -- 2.1 Major Outbreaks of the World -- 2.2 Corona Virus -- 3 Stress -- 3.1 Types of Stress -- 3.2 Symptoms of Emotional Stress in Old People -- 3.3 There Are a Plethora of Reasons for the Elevation of Emotional Stress -- Here We Are Explaining Only Those Which Affect Old Age People During Outbreaks -- 4 Methodology -- 4.1 Participant -- 4.2 Parameters -- 4.3 Algorithms -- 4.4 Mathematical Formula -- 4.5 Flow Diagram -- 5 Result and Discussion -- 5.1 Comparative Study Between Algorithms -- 5.2 Accuracy and Error -- 6 Conclusion -- References -- Unlocking the Psychological Toolbox: To Transform or to Sustain -- 1 Introduction -- 2 Reactions to Psychosocial Stressors During the Pandemic -- 3 Conceptual Model -- 4 Rationale -- 5 Design of the Intervention -- 5.1 Understand Distress and Its Nature -- 5.2 Developing Healthy Coping Strategies -- 5.3 Maintaining Psychological Health -- 6 Conclusion, Limitations, and Future Direction -- References -- Sentimental Analysis of Fears, Psychological Disorders and Health Issues Through NVIVO During Second Wave of Covid-19 -- 1 Introduction -- 2 Literature Review -- 3 Objectives -- 4 Results and Discussion -- 4.1 Research Methods -- 4.2 Preliminary Tests -- 4.3 Correlation -- 4.4 Word Clouds Analysis -- 4.5 Sentimental Analysis -- 4.6 Thematic Analysis -- 5 Conclusion -- 6 Future Work -- References -- Tertiary Students Stress Detection During Online Learning in Jos, Nigeria -- 1 Introduction -- 2 Conceptual Clarification on Stress Among Students in Online Learning -- 3 Possible Stressors of Students in Online Learning -- 4 Theoretical Framework of Stress Among Students in Online Learning -- 5 Methods -- 5.1 Sample of Study -- 6 Measures of Stress Perceived by Situation -- 7 Procedures -- 8 Result -- 9 Discussion -- 10 Conclusion -- References. , Prediction of Mental Health in Cancer Patients Using Ensemble Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Materials and Method -- 4 Results -- 5 Conclusion -- References -- Alzheimer's Disease Classification Using Feed Forwarded Deep Neural Networks for Brain MRI Images -- 1 Introduction -- 2 Subjects and MRI Scans -- 3 Methods -- 3.1 Data Pre-processing -- 3.2 Data Segregation -- 3.3 Modeling -- 3.4 Model Training -- 3.5 Classification Models -- 4 Results and Discussion -- 5 Conclusions -- References -- Challenges and Privacy Concerns Related to Use of Information Technology in Mental Healthcare -- 1 Introduction -- 2 Challenges for Maintaining Mental Health -- 2.1 Knowledge Barriers -- 2.2 Attitudinal Barriers -- 2.3 Structural Barriers -- 3 Opportunities of Using ICT for Mental Healthcare -- 3.1 Accessibility and Flexibility -- 3.2 Anonymity and Privacy -- 3.3 Operating with Limited Resources -- 3.4 Easy Outreach and Awareness Campaigns -- 3.5 Continuous Remote Monitoring -- 4 Information Technology Systems Targeting Mental Health -- 4.1 Online Consultation and Therapies -- 4.2 Mobile Phone Applications -- 4.3 Educational Toys -- 5 Challenges to Offer Privacy -- 5.1 Use of Smart Phone Applications -- 5.2 Interests of Third Party and Simple Sharing Mechanisms -- 5.3 Limited Technology Awareness of Care Providers -- 5.4 Security of Devices and Infrastructures -- 5.5 Limited or No Legislation -- 6 Future Trends and Possibilities -- 6.1 Artificial Intelligence Techniques -- 6.2 Advanced Image Processing -- 6.3 Identification of Reality -- 6.4 Recommendation Systems -- 7 Conclusion -- References.