Handbook of Machine Learning for Computational Optimization: Applications and Case Studies (Demystifying Technologies for Computational Excellence)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Random Variables in Machine Learning
	1.1 Introduction
	1.2 Random Variable
		1.2.1 Definition and Classification
			1.2.1.1 Applications in Machine Learning
		1.2.2 Describing a Random Variable in Terms of Probabilities
			1.2.2.1 Ambiguity with Reference to Continuous Random Variable
		1.2.3 Probability Density Function
			1.2.3.1 Properties of pdf
			1.2.3.2 Applications in Machine Learning
	1.3 Various Random Variables Used in Machine Learning
		1.3.1 Continuous Random Variables
			1.3.1.1 Uniform Random Variable
			1.3.1.2 Gaussian (Normal) Random Variable
		1.3.2 Discrete Random Variables
			1.3.2.1 Bernoulli Random Variable
			1.3.2.2 Binomial Random Variable
			1.3.2.3 Poisson Random Variable
	1.4 Moments of Random Variable
		1.4.1 Moments about Origin
			1.4.1.1 Applications in Machine Learning
		1.4.2 Moments about Mean
			1.4.2.1 Applications in Machine Learning
	1.5 Standardized Random Variable
		1.5.1 Applications in Machine Learning
	1.6 Multiple Random Variables
		1.6.1 Joint Random Variables
			1.6.1.1 Joint Cumulative Distribution Function (Joint CDF)
			1.6.1.2 Joint Probability Density Function (Joint pdf)
			1.6.1.3 Statistically Independent Random Variables
			1.6.1.4 Density of Sum of Independent Random Variables
			1.6.1.5 Central Limit Theorem
			1.6.1.6 Joint Moments of Random Variables
			1.6.1.7 Conditional Probability and Conditional Density Function of Random Variables
	1.7 Transformation of Random Variables
		1.7.1 Applications in Machine Learning
	1.8 Conclusion
	References
Chapter 2 Analysis of EMG Signals using Extreme Learning Machine with Nature Inspired Feature Selection Techniques
	2.1 Introduction
	2.2 Data Set
	2.3 Feature Extraction
	2.4 Nature Inspired Feature Selection Methods
		2.4.1 Particle Swarm Optimization Algorithm (PSO)
		2.4.2 Genetic Algorithm (GA)
		2.4.3 Fire-Fly Optimization Algorithm (FA)
		2.4.4 Bat Algorithm (BA)
		2.4.5 Whale Optimization Algorithm (WOA)
			2.4.5.1 Exploitation Phase
			2.4.5.2 Exploration Phase
	2.5 Extreme Learning Machine (ELM)
	2.6 Results and Discussion
	2.7 Conclusion
	References
Chapter 3 Detection of Breast Cancer by Using Various Machine Learning and Deep Learning Algorithms
	3.1 Introduction
		3.1.1 Risk Factors for Breast Cancer
		3.1.2 Screening Guidelines
		3.1.3 Consequences of Misidentifying the Tumor
		3.1.4 Materials and Methods
	3.2 Model Selection
		3.2.1 Logistic Regression
		3.2.2 Nearest Neighbor
		3.2.3 Support Vector Machine
		3.2.4 Naive Bayes Algorithm
		3.2.5 Decision Tree Algorithm
		3.2.6 Random Forest Classification
	3.3 Detection of Breast Cancer by Using Deep Learning
	3.4 Conclusion
	References
Chapter 4 Assessing the Radial Efficiency Performance of Bus Transport Sector Using Data Envelopment Analysis
	4.1 Introduction
		4.1.1 Background Work
	4.2 Methodology Framework
		4.2.1 DEA Background
		4.2.2 New Slack Model
	4.3 Performance Evaluation of Depots
		4.3.1 Data Collection
		4.3.2 Region-wise Classification of Depots
		4.3.3 Input and Output Parameters
		4.3.4 Empirical Results
		4.3.5 Input Targets for Inefficient Depots
	4.4 Conclusion
	Acknowledgement
	References
	Appendix (A)
Chapter 5 Weight-Based Codes—A Binary Error Control Coding Scheme—A Machine Learning Approach
	5.1 Introduction
	5.2 Encoding
	5.3 Decoding (Machine Learning Approach)
		5.3.1 Principle of Decoding
		5.3.2 Algorithm
	5.4 Output Test Case
	5.5 Conclusion
	References
Chapter 6 Massive Data Classification of Brain Tumors Using DNN: Opportunity in Medical Healthcare 4.0 through Sensors
	6.1 Introduction
		6.1.1 Brain Tumor
		6.1.2 Big Data Analytics in Health Informatics
		6.1.3 Machine Learning (ML) in Healthcare
		6.1.4 Sensors for Internet of Things
		6.1.5 Challenges and Critical Issues of IoT in Healthcare
		6.1.6 Machine Learning (ML) and Artificial Intelligence (AI) for Health Informatics
		6.1.7 Health Sensor Data Management
		6.1.8 Multimodal Data Fusion for Healthcare
		6.1.9 Heterogeneous Data Fusion and Context-Aware Systems—a Context-Aware Data Fusion Approach for Health-IoT
		6.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System
	6.2 Literature Survey
	6.3 System Design and Methodology
		6.3.1 System Design
		6.3.2 CNN Architecture
		6.3.3 Block Diagram
		6.3.4 Algorithm(s)
		6.3.5 Our Experimental Results, Interpretation, and Discussion
		6.3.6 Implementation Details
		6.3.7 Snapshots of Interfaces
		6.3.8 Performance Evaluation
		6.3.9 Comparison with Other Algorithms
	6.4 Novelty in Our Work
	6.5 Future Scope, Possible Applications, and Limitations
	6.6 Recommendations and Consideration
	6.7 Conclusions
	References
Chapter 7 Deep Learning Approach for Traffic Sign Recognition on Embedded Systems
	7.1 Introduction
	7.2 Literature Review
	7.3 General Challenges
	7.4 Proposed Solution
		7.4.1 Hardware
	7.5 Models
		7.5.1 YOLOV3
		7.5.2 Tiny-YOLOV3
		7.5.3 Darknet Reference Model
	7.6 Flowcharts
	7.7 Key Features of the System
	7.8 Technology Stack
	7.9 Dataset
	7.9.1 Labeling/Annotating the Dataset
	7.10 Training the Model
	7.11 Result
	7.12 Future Scope
	References
Chapter 8 Lung Cancer Risk Stratification Using ML and AI on Sensor-Based IoT: An Increasing Technological Trend for Health of Humanity
	8.1 Introduction
		8.1.1 Motivation to the Study
		8.1.2 Problem Statements
		8.1.3 Authors’ Contributions
		8.1.4 Research Manuscript Organization
		8.1.5 Definitions
		8.1.6 Computer-aided Diagnosis System (CADe or CADx)
		8.1.7 Sensors for the Internet of Things
		8.1.8 Wireless and Wearable Sensors for Health Informatics
		8.1.9 Remote Human’s Health and Activity Monitoring
		8.1.10 Decision-Making Systems for Sensor Data
		8.1.11 Artificial Intelligence (AI) and Machine Learning (ML) for Health Informatics
		8.1.12 Health Sensor Data Management
		8.1.13 Multimodal Data Fusion for Healthcare
		8.1.14 Heterogeneous Data Fusion and Context-Aware Systems—a Context-Aware Data Fusion Approach for Health-IoT
	8.2 Literature Review
	8.3 Proposed Systems
		8.3.1 Framework or Architecture of the Work
		8.3.2 Model Steps and Parameters
		8.3.3 Discussions
	8.4 Experimental Results and Analysis
		8.4.1 Tissue Characterization and Risk Stratification
		8.4.2 Samples of Cancer Data and Analysis
	8.5 Novelties
	8.6 Future Scope, Limitations, and Possible Applications
	8.7 Recommendations and Considerations
	8.8 Conclusions
	References
Chapter 9 Statistical Feedback Evaluation System
	9.1 Introduction
	9.2 Related Work
	9.3 Types of Feedback Evaluation Systems
		9.3.1 Questionnaire-Based Feedback Evaluation System (QBFES)
		9.3.2 Star-Point-based Feedback Evaluation System (SBFES)
		9.3.3 Text-Based Feedback Evaluation System (TBFES)
	9.4 Statistical Feedback Evaluation System
		9.4.1 Aspect Extraction
			9.4.1.1 Feedback Collector
			9.4.1.2 Feedback Preprocessor
			9.4.1.3 Aspect Validator
		9.4.2 Aspect Weight Estimation
		9.4.3 Sentiment Evaluation
			9.4.3.1 Sentiment Estimator
			9.4.3.2 Sentiment Aggregator
		9.4.4 Customized Evaluation
		9.4.5 Aspect-Based Questionnaire Design
	9.5 Result Analysis and Discussion
	9.6 Conclusion
	9.7 Future Work
	References
Chapter 10 Emission of Herbal Woods to Deal with Pollution and Diseases: Pandemic-Based Threats
	10.1 Introduction
		10.1.1 Scenario of Pollution and 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 Diseases
		10.1.6 The Yajna Science: A Boon to Human Race from Rishis and Munis
		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
		10.2.1 Gist
		10.2.2 Methodology Used in This Paper
		10.2.3 Instruments and Data Set Used
		10.2.4 The Future Scope Discussed
	10.3 The Methodology and Protocols Followed
	10.4 Experimental Setup of an Experiment
		10.4.1 Airveda and Different Sensor-Based Instruments
	10.5 Results and Discussions
		10.5.1 Mango v/s Banyan (Bargad)
			10.5.1.1 Mango
			10.5.1.2 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
Chapter 11 Artificial Neural Networks: A Comprehensive Review
	11.1 Introduction
	11.2 Activation Function
		11.2.1 Linear Activation Function
		11.2.2 Nonlinear Activation Function
			11.2.2.1 Sigmoid (Logistic) Function
			11.2.2.2 Tanh Activation Function
			11.2.2.3 Rectified Linear Unit (ReLU) Function
	11.3 Artificial Neural Network (ANN)
		11.3.1 Supervised Learning
		11.3.2 Unsupervised Learning
		11.3.3 Reinforcement Learning
	11.4 Types of Artificial Neural Network
		11.4.1 Single-Layer Feedforward Neural Network
		11.4.2 Multilayer Feedforward Neural Networks
		11.4.3 Recursive Neural Network (RNN)
		11.4.4 Convolutional Layer Network (CNN)
	11.4.5 Backpropagation Neural Network
		11.4.5.1 Static Backpropagation
		11.4.5.2 Recurrent Backpropagation
	11.5 Problems in Artificial Neural Networks
		11.5.1 Techniques to Avoid Overfitting When Neural Networks are Trained
	11.6 Convergence of Neural Network
		11.6.1 Adaptive Convergence (or Just Convergence)
		11.6.2 Reactive Convergence
	11.7 Key Features of the Error Surface
		11.7.1 Local Minima
		11.7.2 Flat Regions (Saddle Points)
		11.7.3 High-Dimensional
	11.8 Application of Artificial Neural Network
	11.9 Conclusion
	References
Chapter 12 A Case Study on Machine Learning to Predict the Students’ Result in Higher Education
	12.1 Introduction
		12.1.1 Literature Review
	12.2 Proposed Model
		12.2.1 Participants and Datasets
		12.2.2 Data Retrieval
		12.2.3 Data Preprocessing
	12.3 Result and Discussion
		12.3.1 Model Evaluation Metrics
		12.3.2 Decision Tree Classification
		12.3.3 KNN Classification
		12.3.4 Random Forest Tree Classification
		12.3.5 X-Gradient Boosting Tree Classification
	12.4 Comparative Results for Different Classification Models
	12.5 Conclusion and Future Scope
	References
Chapter 13 Data Analytic Approach for Assessment Status of Awareness of Tuberculosis in Nigeria
	13.1 Introduction
	13.2 Related Works
	13.3 Materials and Methods
		13.3.1 Population and Sample
		13.3.2 Tools and Designing
		13.3.3 Task Procedures
		13.3.4 Data Analysis and Results
	13.4 Results and Discussion
	13.5 Conclusions
	Acknowledgements
	References
Chapter 14 Active Learning from an Imbalanced Dataset: A Study Conducted on the Depression, Anxiety, and Stress Dataset
	14.1 Introduction
	14.2 Literature Survey
	14.3 Problem Statement
	14.4 Necessity of Defining the Problem/Research Gap
	14.5 Objectives
		14.5.1 Primary Objective
		14.5.2 Secondary Objective
	14.6 Dataset
		14.6.1 Data Collection
		14.6.2 Data Description
		14.6.3 Data Preprocessing
		14.6.4 Exploratory Data Analysis
			14.6.4.1 Analysis of DASS
			14.6.4.2 Analysis of the TIPI Test
			14.6.4.3 Analysis of Time Taken by the Users to Complete the Survey
			14.6.4.4 Analysis of the Validity-Check List and their Relationship with the Education Information
	14.7 Implementation Design
		14.7.1 Class Imbalance
		14.7.2 SMOTE
		14.7.3 Model Building
		14.7.4 Evaluation Metric
	14.8 Results and Conclusion
	References
Chapter 15 Classification of the Magnetic Resonance Imaging of the Brain Tumor Using the Residual Neural Network Framework
	15.1 Introduction
	15.2 Literature Review
	15.3 Architecture of Resnet Medical Imaging Modalities
	15.4 Stages for Implementation of the Resnet Framework
		15.4.1 Preprocessing
		15.4.2 Training the Network
		15.4.3 Segmentation
		15.4.4 Focal Loss Function
	15.5 Results and Discussions
	15.6 Conclusions and Future Scope
	References
Index