Оглавление
Группа авторов. Bioinformatics and Medical Applications
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Bioinformatics and Medical Applications. Big Data Using Deep Learning Algorithms
Preface
1. Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction
Abstract
1.1 Introduction
1.1.1 Scope and Motivation
1.2 Literature Review
1.2.1 Comparative Analysis
1.2.2 Survey Analysis
1.3 Tools and Techniques
1.3.1 Description of Dataset
1.3.2 Machine Learning Algorithm
1.3.3 Decision Tree
1.3.4 Random Forest
1.3.5 Naive Bayes Algorithm
1.3.6 K Means Algorithm
1.3.7 Ensemble Method
1.3.7.1 Bagging
1.3.7.2 Boosting
1.3.7.3 Stacking
1.3.7.4 Majority Vote
1.4 Proposed Method. 1.4.1 Experiment and Analysis
1.4.2 Method
1.5 Conclusion
References
2. Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach
Abstract
2.1 Introduction
2.1.1 Motivation of the Study
2.1.1.1 Problem Statements
2.1.1.2 Authors’ Contributions
2.1.1.3 Research Manuscript Organization
2.1.1.4 Definitions
2.1.2 Computer-Aided Diagnosis System (CADe or CADx)
2.1.3 Sensors for the Internet of Things
2.1.4 Wireless and Wearable Sensors for Health Informatics
2.1.5 Remote Human’s Health and Activity Monitoring
2.1.6 Decision-Making Systems for Sensor Data
2.1.7 Artificial Intelligence and Machine Learning for Health Informatics
2.1.8 Health Sensor Data Management
2.1.9 Multimodal Data Fusion for Healthcare
2.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT
2.2 Literature Review
2.3 Proposed Systems
2.3.1 Framework or Architecture of the Work
2.3.2 Model Steps and Parameters
2.3.3 Discussions
2.4 Experimental Results and Analysis
2.4.1 Tissue Characterization and Risk Stratification
2.4.2 Samples of Cancer Data and Analysis
2.5 Novelties
2.6 Future Scope, Limitations, and Possible Applications
2.7 Recommendations and Consideration
2.8 Conclusions
References
3. Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case
Abstract
3.1 Introduction
3.2 Human Coronavirus Types
3.3 The SARS-CoV-2 Pandemic Impact
3.3.1 RNA Virus vs DNA Virus
3.3.2 The Coronaviridae Family
3.3.3 The SARS-CoV-2 Structural Proteins
3.3.4 Protein Representations
3.4 Computational Predictors
3.4.1 Supervised Algorithms
3.4.2 Non-Supervised Algorithms
3.5 Polarity Index Method®
3.5.1 The PIM® Profile
3.5.2 Advantages
3.5.3 Disadvantages
3.5.4 SARS-CoV-2 Recognition Using PIM® Profile
3.6 Future Implications
3.7 Acknowledgments
References
4. Deep Learning in Gait Abnormality Detection: Principles and Illustrations
Abstract
4.1 Introduction
4.2 Background. 4.2.1 LSTM
4.2.1.1 Vanilla LSTM
4.2.1.2 Bidirectional LSTM
4.3 Related Works
4.4 Methods. 4.4.1 Data Collection and Analysis
4.4.2 Results and Discussion
4.5 Conclusion and Future Work
4.6 Acknowledgments
References
5. Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health
Abstract
5.1 Introduction
5.2 Types of Biological Networks
5.3 Methodologies in Network Embedding
5.4 Attributed and Non-Attributed Network Embedding
5.5 Applications of Network Embedding in Computational Biology
5.5.1 Understanding Genomic and Protein Interaction via Network Alignment
5.5.2 Pharmacogenomics
5.5.2.1 Drug-Target Interaction Prediction
5.5.2.2 Drug-Drug Interaction
5.5.2.3 Drug-Disease Interaction Prediction
5.5.2.4 Analysis of Adverse Drug Reaction
5.5.3 Function Prediction
5.5.4 Community Detection
5.5.5 Network Denoising
5.5.6 Analysis of Multi-Omics Data
5.6 Limitations of Network Embedding in Biology
5.7 Conclusion and Outlook
References
6. Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier
Abstract
6.1 Introduction
6.2 Related Study
6.3 Methodology. 6.3.1 Pre-Processing
6.3.2 Region of Interest Extraction
6.3.3 Segmentation
6.3.4 Feature Extraction
6.3.5 Disease Classification
6.4 Implementation and Result Analysis. 6.4.1 Dataset Description
6.4.2 Testbed
6.4.3 Discussion
6.4.3.1 K-Fold Cross-Validation
6.4.3.2 Confusion Matrix
6.5 Conclusion
References
7. Deep Learning for Medical Informatics and Public Health
Abstract
7.1 Introduction
7.2 Deep Learning Techniques in Medical Informatics and Public Health
7.2.1 Autoencoders
7.2.2 Recurrent Neural Network
7.2.3 Convolutional Neural Network (CNN)
7.2.4 Deep Boltzmann Machine
7.2.5 Deep Belief Network
7.3 Applications of Deep Learning in Medical Informatics and Public Health
7.3.1 The Use of DL for Cancer Diagnosis
7.3.2 DL in Disease Prediction and Treatment
7.3.3 Future Applications
7.4 Open Issues Concerning DL in Medical Informatics and Public Health
7.5 Conclusion
References
8. An Insight Into Human Pose Estimation and Its Applications
Abstract
8.1 Foundations of Human Pose Estimation
8.2 Challenges to Human Pose Estimation
8.2.1 Motion Blur
8.2.2 Indistinct Background
8.2.3 Occlusion or Self-Occlusion
8.2.4 Lighting Conditions
8.3 Analyzing the Dimensions
8.3.1 2D Human Pose Estimation
8.3.1.1 Single-Person Pose Estimation
8.3.1.2 Multi-Person Pose Estimation
8.3.2 3D Human Pose Estimation
8.4 Standard Datasets for Human Pose Estimation
8.4.1 Pascal VOC (Visual Object Classes) Dataset
8.4.2 KTH Multi-View Football Dataset I
8.4.3 KTH Multi-View Football Dataset II
8.4.4 MPII Human Pose Dataset
8.4.5 BBC Pose
8.4.6 COCO Dataset
8.4.7 J-HMDB Dataset
8.4.8 Human3.6M Dataset
8.4.9 DensePose
8.4.10 AMASS Dataset
8.5 Deep Learning Revolutionizing Pose Estimation
8.5.1 Approaches in 2D Human Pose Estimation
8.5.2 Approaches in 3D Human Pose Estimation
8.6 Application of Human Pose Estimation in Medical Domains
8.7 Conclusion
References
9. Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare
Abstract
9.1 Introduction. 9.1.1 Brain Tumor
9.1.2 Big Data Analytics in Health Informatics
9.1.3 Machine Learning in Healthcare
9.1.4 Sensors for Internet of Things
9.1.5 Challenges and Critical Issues of IoT in Healthcare
9.1.6 Machine Learning and Artificial Intelligence for Health Informatics
9.1.7 Health Sensor Data Management
9.1.8 Multimodal Data Fusion for Healthcare
9.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT
9.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System
9.2 Literature Survey
9.3 System Design and Methodology. 9.3.1 System Design
9.3.2 CNN Architecture
9.3.3 Block Diagram
9.3.4 Algorithm(s)
9.3.5 Our Experimental Results, Interpretation, and Discussion
9.3.6 Implementation Details
9.3.7 Snapshots of Interfaces
9.3.8 Performance Evaluation
9.3.9 Comparison with Other Algorithms
9.4 Novelty in Our Work
9.5 Future Scope, Possible Applications, and Limitations
9.6 Recommendations and Consideration
9.7 Conclusions
References
10. Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century
Abstract
10.1 Introduction. 10.1.1 Scenario of Pollution and the Need to Connect with Indian Culture
10.1.2 Global Pollution Scenario
10.1.3 Indian Crisis on Pollution and Worrying Stats
10.1.4 Efforts Made to Curb Pollution World Wide
10.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease
10.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni
10.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects
10.1.8 Effect of Different Woods and Cow Dung Used in Yajna
10.1.9 Use of Sensors and IoT to Record Experimental Data
10.1.10 Analysis and Pattern Recognition by ML and AI
10.2 Literature Survey
10.3 The Methodology and Protocols Followed
10.4 Experimental Setup of an Experiment
10.5 Results and Discussions
10.5.1 Mango
10.5.2 Bargad
10.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance
10.7 Future Research Perspectives
10.8 Novelty of Our Research
10.9 Recommendations
10.10 Conclusions
References
11. An Economical Machine Learning Approach for Anomaly Detection in IoT Environment
Abstract
11.1 Introduction
11.2 Literature Survey
11.3 Proposed Work
11.4 Analysis of the Work
11.5 Conclusion
References
12. Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach
Abstract
12.1 Introduction. 12.1.1 Different Types of Diseases. 12.1.1.1 Diabetes (Madhumeha) and Its Types
12.1.1.2 TTH and Stress
12.1.1.3 Anxiety
12.1.1.4 Hypertension
12.1.2 Machine Vision
12.1.2.1 Medical Images and Analysis
12.1.2.2 Machine Learning in Healthcare
12.1.2.3 Artificial Intelligence in Healthcare
12.1.3 Big Data and Internet of Things (IoT)
12.1.4 Machine Learning in Association with Data Science and Analytics
12.1.5 Yajna Science
12.1.6 Mantra Science
12.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting
12.1.6.2 Significance of Mantra on Indian Culture and Mythology
12.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama
12.1.8 Effects of Yajna and Mantra on Human Health
12.1.9 Impact of Yajna in Reducing the Atmospheric Solution
12.1.10 Scientific Study on Impact of Yajna on Air Purification
12.1.11 Scientific Meaning of Religious and Manglik Signs
12.2 Literature Survey
12.3 Methodology
12.4 Results and Discussion
12.5 Interpretations and Analysis
12.6 Novelty in Our Work
12.7 Recommendations
12.8 Future Scope and Possible Applications
12.9 Limitations
12.10 Conclusions
12.11 Acknowledgments
References
13. Collection and Analysis of Big Data From Emerging Technologies in Healthcare
Abstract
13.1 Introduction
13.2 Data Collection
13.2.1 Emerging Technologies in Healthcare and Its Applications
13.2.1.1 RFID
13.2.1.2 WSN
13.2.1.3 IoT
13.2.2 Issues and Challenges in Data Collection
13.2.2.1 Data Quality
13.2.2.2 Data Quantity
13.2.2.3 Data Access
13.2.2.4 Data Provenance
13.2.2.5 Security
13.2.2.5.1 WSN Attacks
13.2.2.5.2 Data Aggregators Vulnerabilities
13.2.2.5.3 Social Engineering
13.2.2.6 Other Challenges
13.3 Data Analysis
13.3.1 Data Analysis Approaches
13.3.1.1 Machine Learning
13.3.1.2 Deep Learning
13.3.1.3 Natural Language Processing
13.3.1.4 High-Performance Computing
13.3.1.5 Edge-Fog Computing
13.3.1.6 Real-Time Analytics
13.3.1.7 End-User Driven Analytics
13.3.1.8 Knowledge-Based Analytics
13.3.2 Issues and Challenges in Data Analysis
13.3.2.1 Multi-Modal Data
13.3.2.2 Complex Domain Knowledge
13.3.2.3 Highly Competent End-Users
13.3.2.4 Supporting Complex Decisions
13.3.2.5 Privacy
13.3.2.6 Other Challenges
13.4 Research Trends
13.5 Conclusion
References
14. A Complete Overview of Sign Language Recognition and Translation Systems
Abstract
14.1 Introduction
14.2 Sign Language Recognition. 14.2.1 Fundamentals of Sign Language Recognition
14.2.2 Requirements for the Sign Language Recognition
14.3 Dataset Creation
14.3.1 American Sign Language
14.3.2 German Sign Language
14.3.3 Arabic Sign Language
14.3.4 Indian Sign Language
14.4 Hardware Employed for Sign Language Recognition
14.4.1 Glove/Sensor-Based Systems
14.4.2 Microsoft Kinect–Based Systems
14.5 Computer Vision–Based Sign Language Recognition and Translation Systems
14.5.1 Image Processing Techniques for Sign Language Recognition
14.5.2 Deep Learning Methods for Sign Language Recognition
14.5.3 Pose Estimation Application to Sign Language Recognition
14.5.4 Temporal Information in Sign Language Recognition and Translation
14.6 Sign Language Translation System— A Brief Overview
14.7 Conclusion
References
Index
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