An enhanced spider wasp optimization algorithm for multilevel thresholding-based medical image segmentation DOI

Mohamed Abdel-Basset,

Reda Mohamed, Ibrahim M. Hezam

и другие.

Evolving Systems, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling DOI
Taimoor Ali Khan, Naveed Ishtiaq Chaudhary, Chung-Chian Hsu

и другие.

Chaos Solitons & Fractals, Год журнала: 2024, Номер 185, С. 115111 - 115111

Опубликована: Июнь 15, 2024

Язык: Английский

Процитировано

7

Optimizing cancer diagnosis: A hybrid approach of genetic operators and Sinh Cosh Optimizer for tumor identification and feature gene selection DOI

Marwa M. Emam,

Essam H. Houssein, Nagwan Abdel Samee

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 180, С. 108984 - 108984

Опубликована: Авг. 10, 2024

Язык: Английский

Процитировано

6

An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems DOI Creative Commons
Fatma A. Hashim, Abdelazim G. Hussien, Anas Bouaouda

и другие.

Alexandria Engineering Journal, Год журнала: 2024, Номер 93, С. 142 - 188

Опубликована: Март 15, 2024

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO a math-inspired optimizer that has many limitations in handling complex multi-modal tries solve these drawbacks using 2 operators: phasor operator for diversity enhancement adaptive p-best mutation strategy preventing it converging local optima. To validate effectiveness suggested optimizer, comprehensive set comparative experiments CEC'2020 test suite was conducted. The experimental results consistently prove technique outperforms its counterparts terms both convergence speed accuracy. Moreover, algorithm applied multi-threshold method with Otsu's entropy, providing further evidence performance. evaluated by comparing those existing well-known algorithms at various threshold levels. proposed attains exceptional

Язык: Английский

Процитировано

5

Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm DOI Creative Commons
Mohamed Abd Elaziz, Mohammed A. A. Al‐qaness, Mohammed Azmi Al‐Betar

и другие.

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 12, 2025

Abstract To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial preventative healthcare. Timely identification significantly improves prognosis and treatment outcomes colorectal patients. Image segmentation in medical image analysis accurate planning. Therefore, this study, we present an alternative multilevel thresholding polyp method (MPOA) enhance of images. The proposed based on enhancing planet optimization algorithm (POA) by integrating operators from reptile search (RSA). evaluation developed MPOA tested with different images compared other approaches. results highlight superior capability MPOA, as evidenced various performance measures effectively segmenting Furthermore, metrics such peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), fitness values demonstrate that outperforms basic version POA methods. underscore significant impact RSA

Язык: Английский

Процитировано

0

Multi-threshold medical image segmentation based on the enhanced walrus optimizer DOI
Jie Li,

Ruicheng Lu,

Biqing Zeng

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(4)

Опубликована: Фев. 17, 2025

Язык: Английский

Процитировано

0

Hybrid Adaptive Crayfish Optimization with Differential Evolution for Color Multi-Threshold Image Segmentation DOI Creative Commons
Honghua Rao, Heming Jia, Xinyao Zhang

и другие.

Biomimetics, Год журнала: 2025, Номер 10(4), С. 218 - 218

Опубликована: Апрель 2, 2025

To better address the issue of multi-threshold image segmentation, this paper proposes a hybrid adaptive crayfish optimization algorithm with differential evolution for color segmentation (ACOADE). Due to insufficient convergence ability in later stages, it is challenging find more optimal solution optimization. ACOADE optimizes maximum foraging quantity parameter p and introduces an adjustment strategy enhance randomness algorithm. Furthermore, core formula (DE) incorporated balance ACOADE’s exploration exploitation capabilities better. validate performance ACOADE, IEEE CEC2020 test function was selected experimentation, eight other algorithms were chosen comparison. verify effectiveness threshold Kapur entropy method Otsu used as objective functions compared algorithms. Subsequently, peak signal-to-noise ratio (PSNR), feature similarity index measure (FSIM), structural (SSIM), Wilcoxon employed evaluate quality segmented images. The results indicated that exhibited significant advantages terms value, metrics, convergence, robustness.

Язык: Английский

Процитировано

0

Application and Optimization of Intelligent Image Processing Technology in Cross-Border E-commerce DOI

W.G. Liang,

Jiahui Liang

Learning and analytics in intelligent systems, Год журнала: 2025, Номер unknown, С. 401 - 412

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Optimized design and integration of an off-grid solar PV-biomass-battery hybrid energy system using an enhanced educational competition algorithm for cost-effective rural electrification DOI

Marwa M. Emam,

Hoda Abd El-Sattar, Essam H. Houssein

и другие.

Journal of Energy Storage, Год журнала: 2025, Номер 120, С. 116381 - 116381

Опубликована: Апрель 9, 2025

Язык: Английский

Процитировано

0

Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning DOI

Mosa E. Hosney,

Essam H. Houssein,

Mohammed R. Saad

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 182, С. 109175 - 109175

Опубликована: Сен. 24, 2024

Язык: Английский

Процитировано

3

An Improved Shuffled Frog-Leaping Algorithm to Solving 0–1 Knapsack Problem DOI Creative Commons

Jianhao Zhang,

Wei Jiang, Kang Zhao

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 148155 - 148166

Опубликована: Янв. 1, 2024

To address the problems of slow convergence, low search accuracy, and easy fall into local optimum, generating a large number infeasible solutions when solving 0-1 Knapsack Problem, which makes it difficult to obtain optimal solution scheme, in this paper, we present greedy induced mutation method for locally solutions, namely, an improved Shuffled Frog Leaping Algorithm (Shuffled based on Greedy Mutation, SFLA-GA-M). First, proposed algorithm adjusts population generation individuals SFLA avoid thereby optimizing update strategy. Secondly, mechanism that incorporates both algorithms genetic is introduced enhance accuracy algorithm. Finally, ten classical Problem cases were selected prove feasibility robustness SFLA-GA-M by comparing with other two variants, are MDSFLA, DSFLA. Meanwhile, further verify performance SFLA-GA-M, KPs multi-dimensional test compared five algorithm, including: DEA, PSO, GA, BMA IFMA. The experimental results show has achieved stronger convergence stability displayed better efficiency superior global capability than IFMA large-sized Problem.

Язык: Английский

Процитировано

2