Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation

Displays, Год журнала: 2024, Номер 84, С. 102740 - 102740

Опубликована: Май 4, 2024

Язык: Английский

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The educational competition optimizer

International Journal of Systems Science, Год журнала: 2024, Номер 55(15), С. 3185 - 3222

Опубликована: Июль 1, 2024

In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created diverse optimization tasks. ECO draws inspiration from competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost efficiency, divides iterative process into three distinct phases: elementary, middle, and high school. Through stepwise approach, gradually narrows down pool of potential solutions, mirroring gradual competition witnessed within systems. strategic approach ensures a smooth resourceful transition between ECO's exploration exploitation phases. The results indicate that attains peak performance when configured with population size 40. Notably, algorithm's efficacy does not exhibit strictly linear correlation size. comprehensively evaluate effectiveness convergence characteristics, we conducted rigorous comparative analysis, comparing against nine state-of-the-art algorithms. remarkable success efficiently addressing problems underscores applicability across domains. additional resources open-source code proposed can be accessed at https://aliasgharheidari.com/ECO.html https://github.com/junbolian/ECO.

Язык: Английский

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Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Applied Sciences, Год журнала: 2024, Номер 14(2), С. 923 - 923

Опубликована: Янв. 22, 2024

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing segmentation, focusing on lung CT scan chest X-ray datasets. Best-cost values reveal PSO algorithm’s performance, HE demonstrating significant stabilization enhanced convergence, particularly complex images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, Jaccard, show substantial improvements preprocessing, emphasizing its impact accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, K-means, confirm competitiveness PSO-HE approach, especially The also underscores positive influence clarity precision. These findings highlight promise approach advancing accuracy reliability pave way further method integration to enhance this critical healthcare application.

Язык: Английский

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Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 1, 2024

Abstract Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method ensuring engineered structures prompt detection cracks. Image threshold segmentation based on machine vision crucial technology for crack detection. Threshold separate area from background, providing convenience more accurate measurement evaluation condition location. The cracks complex scenes challenging task, this goal be achieved by means multilevel thresholding. arithmetic-geometric divergence combines advantages arithmetic mean geometric probability measures, enabling precise capture local features an image processing. In paper, thresholding minimum proposed. To address issue time complexity thresholding, enhanced particle swarm optimization algorithm with stochastic perturbation detection, criterion function adaptively determine thresholds according distribution characteristics pixel values image. proposed increase diversity candidate solutions enhance global convergence performance algorithm. compared seven state-of-the-art methods several metrics, including RMSE, PSNR, SSIM, FSIM, computation time. experimental results show that outperforms competing terms metrics.

Язык: Английский

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Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 96, С. 106492 - 106492

Опубликована: Июнь 7, 2024

Язык: Английский

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Advanced Medical Image Segmentation Enhancement: A Particle Swarm Optimization-Based Histogram Equalization Approach

Опубликована: Янв. 2, 2024

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing segmentation, focusing Lung CT-Scan Chest X-ray datasets. Best Cost values reveal PSO algorithm’s performance, HE demonstrating significant stabilization enhanced convergence, particularly complex images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, Jaccard, show substantial improvements preprocessing, emphasizing its impact accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, K-means, confirm competitiveness PSO-HE approach, especially The also underscores positive influence clarity precision. These findings highlight promise approach advancing accuracy reliability paving way further method integration to enhance this critical healthcare application.

Язык: Английский

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Enhancing model characterization of PEM Fuel cells with human memory optimizer including sensitivity and uncertainty analysis

Ain Shams Engineering Journal, Год журнала: 2024, Номер 15(11), С. 103026 - 103026

Опубликована: Авг. 31, 2024

Язык: Английский

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REBSA: Enhanced backtracking search for multi-threshold segmentation of breast cancer images

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 106, С. 107733 - 107733

Опубликована: Март 11, 2025

Язык: Английский

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Covariance Matrix Adaptation Driven Dynamic Multi-population Colony Predation Optimizer: Insights, Qualitative Analysis, and Constrained Engineering Optimization

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113041 - 113041

Опубликована: Март 1, 2025

Язык: Английский

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Enhanced White Blood Cell and Platelet Segmentation: A Particle Swarm Optimization-based Chromaticity approach

Pertanika journal of science & technology, Год журнала: 2025, Номер 33(3)

Опубликована: Апрель 22, 2025

Microscopic image examination is essential for medical diagnostics to identify anomalies using cell counts based on morphology. Sickle Cell Disease (SCD) an inherited blood condition characterized by defective hemoglobin, leading severe anemia and complications. Detecting sickle cells in smears essential, but the presence of White (WBCs) platelets often leads miscounting as they are classified incorrectly red (RBCs). This study proposed approach segmenting WBCs resembling human color recognition process differentiate regions accurate identification. First, RGB space converted RG chromaticity locate with high pixel chromatic variance. Parametric segmentation applied images appropriate channel probability distribution values. The optimal threshold values have been determined Particle Swarm Optimization (PSO) dynamically narrowing search obtained through manual experimentation ranging from 0.001 1. systematic effectively identifies segments WBCs, ensuring that overlapping accurately segmented. Compared state-of-the-art techniques, achieved accuracy 96.32 %, 96.97% sensitivity, 96.96 % precision 97.46% F- score pixel-wise platelets.

Язык: Английский

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