Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems (Studies in Computational Intelligence, 1038)

✍ Scribed by Essam Halim Houssein (editor), Mohamed Abd Elaziz (editor), Diego Oliva (editor), Laith Abualigah (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 501
• Category: Library

✦ Synopsis

This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms.

The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material canbe helpful for research from the evolutionary computation, artificial intelligence communities.

✦ Table of Contents

About This Book
Contents
Combined Optimization Algorithms for Incorporating DG in Distribution Systems
1 Introduction
2 Objective Function
3 Active Power Loss Sensitivity Factor (PLSF)
4 Voltage Stability Index (VSI)
5 Voltage Deviation
6 Metaheuristic Optimization Techniques
6.1 Moth Flame Optimization (MFO) Algorithm
6.2 Chaotic Moth-Flame Optimization (CMFO) Algorithm
6.3 Salp Swarm Algorithm (SSA)
7 Simulation Results
7.1 IEEE 33-Bus RDS
7.2 IEEE 69-Bus RDS
8 Conclusion
References
Intelligent Computational Models for Cancer Diagnosis: A Comprehensive Review
1 Introduction
2 Preliminaries
2.1 Cancer Diagnosis Overview
2.2 Computational Modeling Overview
2.3 DNA Microarray Datasets
3 Common Techniques Used in Cancer Diagnosis
3.1 Machine Learning Techniques
3.2 Meta-Heuristics Optimization Algorithms
4 Application Areas of Intelligent Computational in Cancer Diagnosis
4.1 Filter-Based Studies
4.2 Wrapper-Based Studies
4.3 Hybrid-Based Studies
4.4 Embedded-Based Studies
5 Open Issues and Challenges
6 Conclusions and Future Research Issues
References
Elitist-Ant System Metaheuristic for ITC 2021—Sports Timetabling
1 Introduction
1.1 Problem Statement
1.2 Objectives
1.3 Scope
1.4 Hypothesis
1.5 Contribution
2 Literature Review
2.1 Studies Based on Round-Robin Tournaments for Sport Timetabling Problems
2.2 Time-Constrained Double Round-Robin Tournaments ITC 2021
3 The ESA-ILS Algorithm
3.1 ESA-ILS
3.2 EAS-ILS Implementation
4 Results and Discussion
4.1 Experimental Setup
4.2 Experimental Results
4.3 Discussion
5 Conclusions
Appendix
References
Swarm Intelligence Algorithms-Based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review
1 Introduction
2 Literature Reviews
3 Basics and Background
3.1 Swarm Intelligence Algorithms Overview
3.2 Machine Learning Techniques Overview
4 Application Areas of SI Algorithms and ML in Medical Diagnosis
4.1 Swarm Intelligence Algorithms in Medical Diagnosis
4.2 Machine Learning in Medical Diagnosis
4.3 Swarm Intelligence Algorithms and Machine Learning in Medical Diagnosis
5 Open Issues and Challenges
6 Conclusions and Future Research Issues
References
Aggregation of Semantically Similar News Articles with the Help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies
1 Introduction
2 Review of Literature
3 Proposed Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Embedding Text to Vectors
3.4 Clustering
3.5 Clustering-Validation (Result Analysis)
4 Result Analysis
5 Conclusion
References
Integration of Machine Learning and Optimization Techniques for Cardiac Health Recognition
1 Introduction
2 Cardiac Health Recognition
2.1 ECG Data
2.2 MIT-BIH Arrhythmia Database
2.3 Data Filtering
2.4 Heartbeats Segmentation
2.5 Feature Extraction
2.6 Feature Selection
2.7 Classification
3 Machine Learning Techniques
3.1 Support Vector Machine (SVM)
3.2 Decision Trees
3.3 Random Forests (RF)
3.4 K-Nearest Neighbor (KNN)
3.5 Perceptron
3.6 Artificial Neural Network (ANN)
3.7 Summarizing and Comparison
4 Optimization Techniques
4.1 Evolutionary Algorithms
4.2 Physics-Inspired Algorithms
4.3 Swarm-Based Algorithms
4.4 Human-Based Algorithms
5 Integration of Machine Learning and Optimization Techniques
6 Open Issues and Challenges
7 Conclusion
References
Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review
1 Introduction
2 Mathematical Model of PV Cell/Module
3 The Objective Function
4 Metaheuristics for Parameter Estimation of PV Cell
4.1 Evolutionary Algorithms
4.2 Human Algorithms
4.3 Physicals-Based Algorithms
4.4 Swarm-Based Algorithms
5 Conclusion
References
Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework
1 Introduction
2 Literature Review
2.1 Overview of Big Data Clustering
2.2 Tasks Involving Big Data
2.3 Analyzing Big Data
2.4 Previous Studies
2.5 Research Gap
3 Implementation and Experiment Work
3.1 K-means Clustering
3.2 Harris Hawk’s Optimization
3.3 MapReduce
3.4 Dataset
3.5 Experimental Setup
3.6 Training and Testing
3.7 Evaluation Methods
4 Experimental Results
4.1 Results Metric and Dataset Training
4.2 Results
5 Conclusions and Future Work
References
Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review
1 Introduction
1.1 Virus Structure and Classification
1.2 RNA Viruses
1.3 Human RNA Viruses
2 RNA Virus Mutations
2.1 RNA Viruses Versus DNA Viruses
2.2 Viruses that Are Single-Stranded Have a Higher Mutation Rate Than Viruses that Are Double-Stranded
3 Machine Learning Techniques
3.1 Logistic Regression
3.2 Random Forest
3.3 Artificial Neural Networks
3.4 Deep Learning
4 Machine Learning for Virus Mutations Prediction
4.1 RNA Genome Mutations
4.2 Spike Protein Mutations
4.3 The Machine Learning Role with the Novel Corona Virus
5 Open Issues and Challenges
6 Conclusion and Future Works
References
2D Target/Anomaly Detection in Time Series Drone Images Using Deep Few-Shot Learning in Small Training Dataset
1 Introduction
1.1 Motivation
1.2 Vertical and Oblique Views in Time Series Drone Images
1.3 Depth Estimation for Time Series Drone Images
1.4 Deep Domain Adaptation for Time Series Drone Images
1.5 Contributions
2 Experiments and Results
2.1 Proposal Model
2.2 Datasets
2.3 Implementation Details
2.4 Experimental Results
3 Future Work
4 Conclusions
References
Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons
1 Introduction
2 Related Works
3 Multilayer Perceptron Training
4 Proposed Method
4.1 Standard MFO
4.2 Improved MFO
4.3 AMFOOBL for Training MLPs
5 Experimental Simulations and Results
5.1 Experiment Setting
5.2 Experiment 1: Benchmark Functions
5.3 Experiment 2: Multilayer Perceptron Training
6 Results Analysis and Discussion
6.1 Benchmark Function Test
6.2 Multilayer Perceptron Training
7 Conclusions and Future Directions
References
Early Detection of Coronary Artery Disease Using PSO-Based Neuroevolution Model
1 Introduction
2 Materials and Methods
2.1 Particle Swarm Optimization (PSO)
2.2 Multi-Layer Perceptron (MLP)
2.3 Suggested Classification Method
2.4 Dataset and Feature Selection Strategy
3 Experiments
3.1 Experimental Setup
3.2 Evaluation Metrics
4 Discussions and Results
5 Conclusions
References
Review for Meta-Heuristic Optimization Propels Machine Learning Computations Execution on Spam Comment Area Under Digital Security Aegis Region
1 Introduction
2 Literature Review
2.1 Optimization
2.2 Overview of Machine Learning
2.3 Used Machine Learning Algorithms Example
2.4 Digital Security
3 Methodology
4 Result
4.1 Rank-Based Meta-Heuristic Optimization
4.2 Max Voting Meta-Heuristic Optimization
5 Conclusion
References
Solving Reality-Based Trajectory Optimization Problems with Metaheuristic Algorithms Inspired by Metaphors
1 Introduction
2 Metaheuristic Algorithms Implemented
2.1 Collective Animal Behavior
2.2 Social Spider Optimization
2.3 Side Blotched Lizard
2.4 Selfish Herd Optimizer Algorithm
2.5 Related Work
2.6 Fuzzy Logic Based Optimization Algorithm
3 Trajectory Optimization Problems
3.1 MGA Global Optimisation Problems
3.2 Cassini 1
3.3 GTOC 1
3.4 MGA-1DSM
3.5 Cassini 2
3.6 Tandem Atlas
3.7 Messenger (Reduced Version)
3.8 Messenger (Full Version)
3.9 Rosetta
3.10 Sagas
4 Metodology
5 Results and Discussions
6 Conclusions
References
Parameter Tuning of PID Controller Based on Arithmetic Optimization Algorithm in IOT Systems
1 Introduction
2 AOA Algorithm
3 PID’s Parameter Estimation Based on AOA Algorithm
4 Experimental Results
4.1 Speed Regulator of DC Motor System
4.2 Liquid Level Tank
5 Conclusions and Future Work
References
Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms
1 Introduction
2 Literature Review
3 Methodology
4 System Design
5 Experimental Results
6 Conclusion
References
AI Based Technologies for Digital and Banking Fraud During Covid-19
1 Introduction
2 Importance of AI Techniques in Detecting Frauds
3 Possible Ways to Strengthen the Processes for Data Processing to Avoid and Identify Fraud
4 Measures Incorporated to Minimise the Fraudulent Activities
5 Challenges Faced by AI Enabled Fraud Detection Techniques
6 Discussion
7 Conclusion
References
Gradient-Based Optimizer for Structural Optimization Problems
1 Introduction
2 Preliminaries
2.1 Gradient-Based Optimizer (GBO) Algorithm
3 Experimental Results and Discussion
3.1 Corrugated Bulkhead Design
3.2 Tubular Column Design
3.3 A Reinforced Concrete Beam Design
4 Conclusions and Future Work
References
Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing
1 Introduction
2 Task Scheduling Problem and Its Notations
3 The Proposed Swarm Intelligence Scheduler Method
3.1 Aquila Optimizer (AO)
3.2 Particle Swarm Optimizer (PSO)
3.3 The Proposed IAO Scheduler
4 Experiments Results and Discussion
5 Conclusion and Potential Future Works
References
512393_1_En_20_Chapter_OnlinePDF.pdf
Correction to: Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons
Correction to: Chapter “Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons” in: E. H. Houssein et al. (eds.), Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems, Studies in Computational Intelligence 1038, https://doi.org/10.1007/978-3-030-99079-411