Computational Intelligence and Data Sciences: Paradigms in Biomedical Engineering

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Editors
Contributors
Chapter 1 Performance of Diverse Machine Learning Algorithms for Heart Disease Prognosis
	1.1 Introduction
	1.2 Literature Review
	1.3 Materials and Methods
		1.3.1 Data
		1.3.2 Outlier Detection
		1.3.3 Data Preprocessing
		1.3.4 Dimensionality Reduction
		1.3.5 Ensemble Methods of Machine Learning
	1.4 Proposed Approach for the Classification Model
		1.4.1 Logistic Regression
		1.4.2 Random Forest
		1.4.3 Gradient Boosting
		1.4.4 Extra-Trees Classifier
		1.4.5 AdaBoost
		1.4.6 MLP
		1.4.7 Decision Tree Classifier
	1.5 Results
	1.6 Conclusions
	References
Chapter 2 Intelligent Ovarian Detection and Classification in Ultrasound Images Using Machine Learning Techniques
	2.1 Introduction
	2.2 Materials and Methods
		2.2.1 Datasets
		2.2.2 Methodology
			2.2.2.1 Preprocessing
			2.2.2.2 Feature Extraction
			2.2.2.3 Machine Learning-Based Ovarian Detection
			2.2.2.4 Intelligent System for Ovarian Classification (ISOC)
			2.2.2.5 Performance Metrics
	2.3 Results
		2.3.1 Preprocessing
		2.3.2 Feature Extraction
			2.3.2.1 Intensity Features
			2.3.2.2 Texture Features
		2.3.3 Machine Learning-Based Ovarian Detection (MLOD)
		2.3.4 Intelligent System for Ovarian Classification
			2.3.4.1 Classification Using ANN
			2.3.4.2 Classification Using LDA
			2.3.4.3 Classification Using SVM
	2.4 Discussion
	2.5 Conclusions
	Acknowledgements
	References
Chapter 3 On Effective Use of Feature Engineering for Improving the Predictive Capability of Machine Learning Models
	3.1 Introduction
	3.2 Background
	3.3 Data Description and Preparation
	3.4 Domain Knowledge and Feature Engineering
	3.5 Balanced Data Creation Using OCSVM
		3.5.1 One-Class SVM
		3.5.2 Data Preparation for OCSVM
	3.6 Results and Discussion
	3.7 Conclusions
	Declarations
	References
Chapter 4 Artificial Intelligence Emergence in Disruptive Technology
	4.1 Introduction
	4.2 Artificial Intelligence
	4.3 Components of Artificial Intelligence
	4.4 Types of Artificial Intelligence
		4.4.1 Reactive Machines
		4.4.2 Limited Memory
		4.4.3 Theory of Mind
		4.4.4 Self-Awareness
	4.5 Artificial Intelligence for Modern Businesses
		4.5.1 Interactive Artificial Intelligence (IAI)
		4.5.2 Functional Artificial Intelligence (FAI)
		4.5.3 Analytic Artificial Intelligence (AAI)
		4.5.4 Text Artificial Intelligence (TAI)
		4.5.5 Visual Artificial Intelligence (VAI)
	4.6 Disruptive Technology
		4.6.1 Digital Transformation
		4.6.2 Examples of Disruptive Technology
		4.6.3 Impact of Big Data in Disruptive Technology
	4.7 Artificial Intelligence as a Disruptive Technology
		4.7.1 Artificial Intelligence as a Disruptive Technology in Various Sectors
			4.7.1.1 Accounting and Finance
			4.7.1.2 Marketing
			4.7.1.3 E-Commerce
			4.7.1.4 Contact Centre
			4.7.1.5 Telecommunications
	4.8 Business Benefits of Adopting AI
	4.9 Conclusions
	References
Chapter 5 An Optimal Diabetic Features-Based Intelligent System to Predict Diabetic Retinal Disease
	5.1 Introduction
	5.2 Experimental Methods
		5.2.1 Dataset Description
		5.2.2 Preprocessing of Data
		5.2.3 Dataset Splitting
	5.3 Machine Learning Classification Approach
		5.3.1 Kernel-Based SVMs
		5.3.2 Linear Model
		5.3.3 Boosted Regression
		5.3.4 K-nearest Neighbor (KNN)
		5.3.5 CART (Classification and Regression Tree)
		5.3.6 Ensemble-Based Algorithms
			5.3.6.1 Random Forest Ensemble Machine Learning Algorithm
			5.3.6.2 AdaBoost Random Forest Ensemble Learner
			5.3.6.3 Gradient Boost Random Forest Ensemble Learner
	5.4 Results and Impact
	5.5 Conclusions
	Acknowledgement
	References
Chapter 6 Cross-Recurrence Quantification Analysis for Distinguishing Emotions Induced by Indian Classical Music
	6.1 Introduction
	6.2 Music, Emotion and Cognition
	6.3 Materials and Methods
		6.3.1 Signal Acquisition
		6.3.2 Music Stimulus
		6.3.3 Pre-processing of EEG Signals
	6.4 Phase Space Plots
	6.5 Cross-Recurrence Plots
	6.6 Cross-Recurrence Quantification Analysis
	6.7 Results and Discussion
	6.8 Conclusions
	Acknowledgments
	References
Chapter 7 Pattern Recognition and Classification of Remotely Sensed Satellite Imagery
	7.1 Introduction
	7.2 Methodologies
		7.2.1 Classification Techniques
			7.2.1.1 MLP Neural Network
			7.2.1.2 K-SOM Neural Network
			7.2.1.3 Maximum Likelihood Classification Algorithm
			7.2.1.4 Mahalanobis Distance Classification Algorithm
			7.2.1.5 Spectral Correlation Mapper Classification Algorithm
		7.2.2 Assessment of Classification Accuracy
	7.3 Empirical Illustrations
		7.3.1 Data and Implementation
	7.4 Discussion
	7.5 Conclusions
	Acknowledgements
	References
Chapter 8 Viability of Information and Correspondence Innovation for the Improvement of Communication Abilities in the Healthcare Industry
	8.1 Introduction
		8.1.1 Effective Ways to Improve Communication Skills
		8.1.2 Technologies of IT in Developing Communication Skills
		8.1.3 Need for Communication in Healthcare
	8.2 Literature Review
	8.3 Research Design
		8.3.1 Problem Statement
		8.3.2 Objectives
		8.3.3 Importance of This Study
		8.3.4 Research Questions
	8.4 Research Methodology
		8.4.1 Survey Approach
		8.4.2 Populations and Samples
		8.4.3 Data Collection Methods
		8.4.4 Tools for Data Analysis
	8.5 Results
	8.6 Findings
	8.7 Limitations
	8.8 Conclusions
	References
Chapter 9 Application of 5G/ 6G Smart Systems to Overcome Pandemic and Disaster Situations
	9.1 Introduction
	9.2 4G, 5G and 6G
		9.2.1 4G
		9.2.2 5G
			9.2.2.1 Why 5G?
			9.2.2.2 5G Is Far Superior to 4G
		9.2.3 6G
		9.2.3.1 Why 6G?
	9.3 Smart Environment
	9.4 Summary and Conclusions
	References
Chapter 10 Risk Perception, Risk Management, and Safety Assessments: A Review of an Explosion in the Fireworks Industry
	10.1 Introduction
	10.2 Composition
	10.3 Manufacturing Process
	10.4 Field Study
	10.5 Hazards in Fireworks Industries
		10.5.1 Fire Accidents
		10.5.2 Chemical Risk Factors
		10.5.3 Study of the Workers
		10.5.4 Analysis of Safety
		10.5.5 Workers Safety Using Regression Analysis
		10.5.6 Safety Environment Prediction Using Chi-Square Analysis
		10.5.7 Job Safety Analysis
	10.6 Findings
		10.6.1 Lack of Training
		10.6.2 Usage of Personal Protective Equipment
		10.6.3 Health Issues in Fireworks Industries
		10.6.4 Causes for Accidents
		10.6.5 The Age Group Dispersion in Fireworks Industries
	10.7 Conclusions
	References
	Book
	Conference
	Website
Chapter 11 High-Utility Itemset Mining: Fundamentals, Properties, Techniques and Research Scope
	11.1 Introduction
		11.1.1 Utility Mining
		11.1.2 High-Utility Itemset Mining
	11.2 Frequent Itemset Mining and High-Utility Itemset Mining
		11.2.1 Frequent Itemset Mining
	11.3 High-Utility Itemset Mining
	11.4 Comprehensive Analysis of HUIM Techniques
		11.4.1 Two-Phase Algorithm
		11.4.2 Faster High-Utility Itemset Mining (FHM)
		11.4.3 Efficient High-Utility Itemset Mining (EFIM)
		11.4.4 High-Utility Itemset Miner (HUI-Miner)
		11.4.5 High-Utility Pruning Strategy (HUP-Miner)
		11.4.6 Utility Pattern Growth (UP-Growth)
		11.4.7 Utility List Buffer (ULB-Miner)
		11.4.8 Hybrid Technique by the Integration of UP-Growth and FHM (UFH-Miner)
		11.4.9 Direct Discovery of High-Utility Itemset (D2HUP)
		11.4.10 Optimization Approaches for HUIM
	11.5 Conclusions
	References
Chapter 12 A Corpus Based Quantitative Analysis of Gurmukhi Script
	12.1 Introduction
	12.2 Data Collection and Pre-processing
	12.3 Basic Concepts and Research Methods
		12.3.1 Sentences, Words, and Characters
		12.3.2 Method of Analysis
			12.3.2.1 Mean, Mode, and Median
			12.3.2.2 Standard Deviation
			12.3.2.3 Skewness
			12.3.2.4 Correlation
			12.3.2.5 Type Token Ratio
			12.3.2.6 Frequency
	12.4 Results and Discussion
		12.4.1 Word
		12.4.2 Sentence
	12.5 Conclusions
	References
Chapter 13 An Analysis of Protein Interaction and Its Methods, Metabolite Pathway and Drug Discovery
	13.1 Introduction
		13.1.1 Related Works
	13.2 Methodology
		13.2.1 The Rosetta Stone Method
		13.2.2 Yeast Two-Hybrid Method
		13.2.3 Sequence Alignment
		13.2.4 Docking and Drug Discovery
		13.2.5 Metabolite–Protein Interactions
		13.2.6 Protein Function Prediction
		13.2.7 Pathway of the Protein Interaction Network
		13.2.8 The Two-Hybrid System
		13.2.9 Perception of Protein Interaction Methods
	13.3 Conclusions
	References
Chapter 14 Biosensors for Disease Diagnosis
	14.1 Introduction
		14.1.1 Disposable Immunosensors
		14.1.2 Point-of-Care Diagnostics
	14.2 Biosensors in the Diagnosis of Alzheimer’s Disease
	14.3 Biosensors in Diagnosis of Cancer
	14.4 Biosensor in Detection of Hepatitis
	14.5 Biosensors in Diagnosis of HIV
	14.6 Biosensors in Diagnosis of SARS-CoV-2
	14.7 Conclusion
	Acknowledgments
	References
Index