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 MetaboliteβProtein 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 Alzheimerβs 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