Nature-Inspired Optimization Methodologies in Biomedical and Healthcare

Foreword
Preface
Contents
1 Nature-Inspired Optimization Algorithms: Past to Present
1.1 Introduction
1.1.1 Why Do We Need Nature-Inspired Optimization Algorithms?
1.1.2 Classification of Optimization Algorithms
1.2 Background
1.2.1 Natural Computing
1.2.2 Algorithm
1.2.3 Optimization
1.2.4 Metaheuristic
1.3 Broad Review on Nature-Inspired Optimization Algorithms
1.3.1 Genetic Algorithms
1.3.2 Ant Colony Optimization
1.3.3 Swarm Intelligence
1.3.4 Artificial Bee Colony (ABC)
1.3.5 ACO-Ant Colony Optimization
1.3.6 BAT Algorithm
1.4 Theoretical Analysis and Applications
1.4.1 Applications
1.5 Discussions, Challenges, Open Issues and Future Recommendations
1.6 Conclusion
References
2 Preventing the Early Spread of Infectious Diseases Using Particle Swarm Optimization
2.1 Introduction
2.2 Literature Review of the Status of Research and Development in the Subject
2.3 Methodology
2.3.1 Pattern Prediction with Prior Knowledge
2.4 Experiments and Results
2.4.1 Running Environment
2.4.2 Performance Metric
2.5 Conclusion and Future Enhancement
References
3 Optimized Gradient Boosting Tree-Based Model for Obesity Level Prediction from patient’s Physical Condition and Eating Habits
3.1 Introduction
3.2 Literature Study
3.3 Understanding Factors Associated with Obesity
3.4 Proposed Approach
3.4.1 Artificial Physics Optimization
3.4.2 APO Based GBT for Obesity Prediction
3.5 Experimental Result and Analysis
3.6 Conclusion
References
4 Multi-Objective Optimization Algorithms in Medical Image Analysis
4.1 Introduction
4.2 Perceptual Method of Color Correction Based on Multi-Objective Optimization
4.2.1 Loss-Function for Perceptual Color Correction
4.2.2 Multi-Objective Optimization
4.2.3 Color Correction Method
4.3 Experimental Results
4.4 Conclusion
References
5 Heart Failure Detection from Clinical and Lifestyle Information using Optimized XGBoost with Gravitational Search Algorithm
5.1 Introduction
5.2 Literature Survey
5.3 Exploratory Data Analysis of Heart Failure Data
5.4 Proposed Method
5.4.1 Gravitation Search Algorithm
5.4.2 GSA-Based XGB for Heart Failure Detection
5.5 Results and Analysis
5.6 Conclusion
References
6 NIANN: Integration of ANN with Nature-Inspired Optimization Algorithms
6.1 Introduction
6.2 Literature Review
6.3 Integration of Artificial Neural Network and Optimization Algorithm
6.3.1 Artificial Neural Network (ANN)
6.3.2 Optimization Algorithm
6.3.3 Integration of ANN and OA
6.4 Experimental Results and Discussion
6.4.1 Experimental Setup
6.4.2 Discussion on ANN
6.5 Conclusion
References
7 Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization
7.1 Introduction
7.2 Literature Survey
7.3 Proposed Methodology
7.3.1 Preprocessing
7.3.2 Fuzzy C-Means Clustering
7.3.3 Defuzzification X-Cut
7.3.4 Generate the Base Summaries
7.3.5 Nature Inspired Optimization
7.4 Experimental Results
7.4.1 Experimental Setup
7.4.2 Performance Evaluation W.r.t ROUGE
7.4.3 Compare Performance with Different Summarising Approaches W.r.t ROUGE
7.5 Conclusion and Future Direction
References
8 An Optimistic Bayesian Optimization Based Extreme Learning Machine for Polycystic Ovary Syndrome Diagnosis
8.1 Introduction
8.2 Related Work
8.3 Proposed Work
8.3.1 Extreme Learning Machine
8.3.2 Bayesian Optimization (BO)
8.3.3 Proposed ELM + BO Method
8.4 Discussion of Result Analysis and Simulation Setup
8.4.1 Dataset Overview and Environmental Setup
8.4.2 Result Analysis
8.5 Conclusion
References
9 Diabetes Twitter Classification Using Hybrid GSA
9.1 Introduction
9.2 Related Works
9.3 Tweets Extraction
9.4 Methodology
9.4.1 GSA
9.4.2 CNN
9.4.3 GRU
9.4.4 LSTM
9.4.5 Embedding Layer
9.4.6 Dropout Layer
9.4.7 Maxpooling
9.4.8 Output Layer
9.5 Data Collection
9.6 Data Pre-processing
9.6.1 Conversion and Correction
9.6.2 Tokenization
9.6.3 Stop Words
9.6.4 Lemmatization
9.6.5 Word Stemming
9.6.6 Word Representation
9.6.7 Bag of Word
9.6.8 TF-IDF
9.6.9 Word2Vec
9.7 Proposed Capsule Network with GSA
9.7.1 Capsule Network
9.7.2 Capsule Network with GSA Algorithm
9.8 Conclusion
References
10 Advance Machine Learning and Nature-Inspired Optimization in Heart Failure Clinical Records Dataset
10.1 Introduction
10.1.1 Cardiovascular Disease (CVD)
10.2 Related Works
10.3 Tree Based Algorithms
10.4 Natured Inspired Optimization (NIO)
10.5 Basic Preliminaries of Optimization Techniques
10.5.1 Bat Algorithm
10.5.2 Hybrid Bat Algorithm
10.5.3 Hybrid Self Adaptive Bat Algorithm
10.5.4 Firefly Algorithm
10.5.5 Grey Wolf Algorithm
10.6 Experiment Setup and Datasets Descriptions
10.6.1 System Environment
10.6.2 Heart Failure Clinical Records Data Set
10.7 Results
10.8 Conclusion and Future Work
References
11 Early Detection of Chronic Obstructive Pulmonary Disease Using LSTM-Firefly Based Deep Learning Model
11.1 Introduction
11.2 Literature Study
11.3 Proposed Method
11.3.1 Firefly Optimization Algorithm
11.3.2 Long Short-Term Memory (LSTM)
11.3.3 LSTM + Firefly Methodology
11.4 Experimental Setup
11.4.1 Dataset
11.4.2 Simulation Environment
11.4.3 Performance Measures
11.5 Result Analysis
11.6 Critical Discussion
11.7 Conclusion
References
12 GACO: A Genetic Algorithm with Ant Colony Optimization—Based Feature Selection for Breast Cancer Diagnosis
12.1 Introduction
12.2 Related Work
12.3 Preliminaries
12.3.1 Data Normalization
12.3.2 Principal Component Analysis (PCA)
12.3.3 Genetic Algorithm (GA)
12.3.4 Ant Colony Optimization (ACO)
12.3.5 Random Forest Algorithm (RF)
12.4 Proposed System
12.4.1 Data Preprocessing Component (DPC)
12.4.2 Evolutionary Algorithm-Based Feature Selection Component (EAFSC)
12.4.3 Proposed GACO_RF Component (GRC)
12.5 Results and Discussions
12.5.1 Dataset Description
12.5.2 Data Preprocessing
12.5.3 GA Parameters Estimation
12.5.4 GACO Parameter Estimation
12.5.5 Performance Comparison of Evolutionary Feature Selection Methods
12.5.6 Performance Metric
12.5.7 Performance Comparison of Proposed GACO_RF Model
12.6 Conclusion
References