UID:
almahu_9949993551402882
Umfang:
1 online resource (249 pages)
Ausgabe:
First edition.
ISBN:
9780443133152
,
0443133158
,
9780443133145
,
044313314X
Anmerkung:
Front Cover -- Metaheuristics Algorithms for Medical Applications -- Copyright Page -- Contents -- 1 Metaheuristic algorithms and medical applications -- 1.1 Introduction -- 1.2 What is the optimization problem -- 1.3 Optimization problems in medical applications -- 1.4 What is metaheuristics -- 1.4.1 Metaheuristics classification -- 1.4.2 Main stages of a metaheuristic -- 1.4.3 Nutcracker optimization algorithm -- 1.4.4 Teaching-learning-based optimization -- 1.4.4.1 Teacher phase -- 1.4.4.2 Learner phase -- 1.4.5 Differential evolution -- 1.4.5.1 Mutation operator -- 1.4.5.2 Crossover operator -- 1.4.5.3 Selection operator -- 1.4.6 Light spectrum optimizer -- 1.4.7 Exploration mechanism -- 1.4.7.1 Exploitation mechanism -- 1.5 Chapter summary -- References -- 2 Wavelet-based image denoising using improved artificial jellyfish search optimizer -- 2.1 Introduction -- 2.2 Wavelet denoising -- 2.2.1 Wavelet transform -- 2.2.2 Principle of wavelet denoising -- 2.3 Artificial jellyfish search optimizer -- 2.4 How to estimate the wavelet coefficients -- 2.4.1 Initialization -- 2.4.2 Objective function -- 2.4.3 Improved JS -- 2.5 Experimental settings -- 2.6 Performance metrics -- 2.7 Practical analysis -- 2.8 Chapter summary -- References -- 3 Artificial gorilla troops optimizer for human activity recognition in IoT-based medical applications -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Deep neural network -- 3.2.2 Artificial gorilla troops optimizer -- 3.2.3 Grey wolf optimizer -- 3.3 Metaheuristics-based DNN's hyperparameters tuning -- 3.3.1 Initialization -- 3.3.2 Constructing DNN -- 3.3.3 Evaluation -- 3.3.3.1 Performance metrics -- 3.3.3.2 Dataset preprocessing -- 3.4 Dataset description and experiment settings -- 3.5 Results and discussion -- 3.6 Chapter summary -- References -- 4 Improved gradient-based optimizer for medical image enhancement.
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4.1 Introduction -- 4.2 Methods -- 4.2.1 Transformation function -- 4.2.2 Objective function -- 4.2.3 Gradient-based optimizer -- 4.2.3.1 Gradient search rule phase -- 4.2.3.2 Local escaping operator (LEO) phase -- 4.3 Metaheuristics-based image enhancement technique -- 4.3.1 Step 1: initialization -- 4.3.2 Step 2: novel self-adaptive strategy (SAS) -- 4.3.3 Step 3: evaluation stage -- 4.3.4 Step 5: pseudocode of the proposed IGNDO -- 4.4 Practical analysis -- 4.5 Chapter summary -- References -- 5 Metaheuristic-based multilevel thresholding segmentation technique for brain magnetic resonance images -- 5.1 Introduction -- 5.2 Techniques for image segmentation -- 5.3 Problem formulation -- 5.3.1 Kapur's entropy -- 5.3.2 Otsu method -- 5.4 How to implement a metaheuristic for the MISP -- 5.5 Practical analysis -- 5.5.1 Evaluation using SD metric -- 5.5.2 Comparison under fitness values -- 5.5.3 Comparison under PSNR values -- 5.5.4 Comparison under SSIM values -- 5.5.5 Comparison under FSIM values -- 5.5.6 Computational cost analysis -- 5.6 Chapter summary -- References -- 6 Metaheuristic algorithm's role for machine learning techniques in medical applications -- 6.1 Introduction -- 6.2 Support vector machine -- 6.3 K-nearest neighbor algorithm -- 6.3.1 Weighted KNN (wKNN) algorithm -- 6.4 Naive Bayes algorithm -- 6.4.1 Why is this algorithm known as naive Bayes? -- 6.5 Random forest -- 6.6 K-means clustering algorithm -- 6.7 Multilayer perceptron -- 6.8 Decision tree induction -- 6.9 Logistic regression -- 6.10 Chapter summary -- References -- 7 Metaheuristic algorithms collaborated with various machine learning models for feature selection in medical data: Compari... -- 7.1 Introduction -- 7.2 Feature selection techniques -- 7.2.1 Filter methods -- 7.2.2 Chi-square feature selection -- 7.2.3 Classical Fisher score -- 7.2.4 Generalized Fisher score.
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7.2.5 Correlation criteria -- 7.2.6 Mutual information -- 7.3 Wrapper-based methods -- 7.3.1 Sequential selection algorithms -- 7.3.2 Metaheuristic-based feature selection -- 7.4 Experiment settings -- 7.5 Performance metrics -- 7.6 Practical analysis -- 7.7 Chapter summary -- References -- 8 Machine learning and improved multiobjective binary generalized normal distribution optimization in feature selection for... -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Multiobjective optimization -- 8.2.2 Generalized normal distribution optimization -- 8.3 Multiobjective improved binary GNDO -- 8.4 Practical analysis -- 8.5 Chapter summary -- References -- 9 Metaheuristics for assisting the deep neural network in classifying the chest X-ray images infected with COVID-19 -- 9.1 Introduction -- 9.2 Deep learning techniques for COVID-19 diagnosis -- 9.3 Metaheuristics for COVID-19 diagnosis -- 9.4 Metaheuristics-assisted deep neural network for COVID-19 diagnosis -- 9.5 Dataset description -- 9.6 Preprocessing step -- 9.7 Experimental settings -- 9.8 Practical findings -- 9.9 Chapter summary -- References -- 10 Metaheuristic algorithms for multimodal image fusion of magnetic resonance and computed tomography brain tumor images: a... -- 10.1 Introduction -- 10.2 Discrete wavelet transform -- 10.3 Image fusion rule -- 10.4 Seagull optimization algorithm -- 10.4.1 Migration behavior: exploration operator -- 10.4.2 Attacking behavior: exploitation operator -- 10.5 Proposed algorithm for multimodal medical image fusion problem -- 10.5.1 Initialization -- 10.5.2 Root mean squared error -- 10.5.3 Improved seagull optimization algorithm -- 10.5.4 Hybridization between TLBO and ISOA -- 10.6 Performance metrics -- 10.7 Practical analysis -- 10.8 Chapter summary -- References -- 11 Metaheuristic algorithms for medical image registration: a comparative study.
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11.1 Introduction -- 11.2 Techniques for image registration -- 11.2.1 Feature-based image registration techniques -- 11.2.2 Intensity-based image registration techniques -- 11.3 Artificial gorilla troops optimizer -- 11.3.1 Exploration operator -- 11.3.2 Exploitation operator -- 11.4 Marine predators algorithm -- 11.5 Proposed algorithms for image registration -- 11.5.1 Initialization -- 11.5.2 Evaluation step -- 11.5.3 Convergence improvement strategy -- 11.5.4 Pseudocode of a studied algorithm -- 11.6 Practical analysis -- 11.7 Chapter summary -- References -- 12 Challenges, opportunities, and future prospects -- 12.1 Introduction -- 12.2 Challenges -- 12.3 Future directions -- References -- Index -- Back Cover.
Weitere Ausg.:
Print version: Abdel-Basset, Mohamed Metaheuristics Algorithms for Medical Applications San Diego : Elsevier Science & Technology,c2023
Weitere Ausg.:
ISBN 044313314X
Weitere Ausg.:
ISBN 9780443133145
Weitere Ausg.:
ISBN 044313314X
Weitere Ausg.:
ISBN 9780443133145
Sprache:
Englisch
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