Bioinformatics and Medical Applications

Bioinformatics and Medical Applications
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BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician’s important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.

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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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Figure 1.6 Random forest algorithm.

Some of the disadvantages are as follows:

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