Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 169, P. 107922 - 107922

Published: Jan. 4, 2024

Language: Английский

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Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(9), P. 13295 - 13332

Published: June 25, 2024

Language: Английский

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Development and application of a comprehensive assessment method of regional flood disaster risk based on a refined random forest model using beluga whale optimization

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130963 - 130963

Published: Feb. 28, 2024

Language: Английский

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Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 160, P. 106966 - 106966

Published: April 24, 2023

Language: Английский

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Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems

Journal of Bionic Engineering, Journal Year: 2023, Volume and Issue: 20(6), P. 2863 - 2895

Published: Sept. 7, 2023

Language: Английский

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An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 85, P. 29 - 48

Published: Nov. 17, 2023

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features select most important ones that affect classification performance. Metaheuristic algorithms are best choice solve this combinatorial problem. Recent researchers invented adapted new algorithms, hybridized or enhanced existing by adding some operators In our paper, we added Coati optimization algorithm (CoatiOA). first operator is adaptive s-best mutation enhance balance between exploration exploitation. second directional rule opens way discover search space thoroughly. final enhancement controlling direction toward global best. We tested proposed mCoatiOA in solving) solving challenging problems from CEC'20 test suite. performance was compared with Dandelion Optimizer (DO), African vultures (AVOA), Artificial gorilla troops optimizer (GTO), whale (WOA), Fick's Law Algorithm (FLA), Particle swarm (PSO), Harris hawks (HHO), Tunicate (TSA). According average fitness, it can be observed method, mCoatiOA, performs better than other on 8 functions. It lower standard deviation values competitive algorithms. Wilcoxon showed results obtained significantly different those rival been as algorithm. Fifteen benchmark various types were collected UCI machine-learning repository. Different evaluation criteria used determine effectiveness method. achieved comparison published methods. mean 75% datasets.

Language: Английский

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An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108329 - 108329

Published: March 19, 2024

Language: Английский

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An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(15), P. 8775 - 8823

Published: March 5, 2024

Language: Английский

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An Enhanced Beluga Whale Optimization Algorithm for Engineering Optimization Problems

Journal of Systems Science and Systems Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: May 31, 2024

Language: Английский

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An effective multiclass skin cancer classification approach based on deep convolutional neural network

Cluster Computing, Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Abstract Skin cancer is one of the most dangerous types due to its immediate appearance and possibility rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in area body, invading other bodily tissues, spreading throughout body. Early detection helps prevent progress reaching critical levels, reducing risk complications need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin diagnosis by extracting intricate features images, enabling an accurate classification lesions. Their role extends early detection, providing a powerful tool dermatologists identify abnormalities their nascent stages, ultimately improving patient outcomes. This study proposes novel deep convolutional network (DCNN) approach classifying The proposed DCNN model evaluated using two unbalanced datasets, namely HAM10000 ISIC-2019. compared with transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, MobileNetV2. Its performance assessed four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, AUC. experimental results demonstrate that outperforms (DL) models utilized these datasets. achieved highest accuracy ISIC-2019 $$98.5\%$$ 98.5 % $$97.1\%$$ 97.1 , respectively. These show how competitive successful overcoming problems caused class imbalance raising accuracy. Furthermore, demonstrates superior performance, particularly excelling terms recent studies utilize same which highlights robustness effectiveness DCNN.

Language: Английский

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