Classification of breast cancer using a manta-ray foraging optimized transfer learning framework DOI Creative Commons
Nadiah A. Baghdadi, Amer Malki, Hossam Magdy Balaha

и другие.

PeerJ Computer Science, Год журнала: 2022, Номер 8, С. e1054 - e1054

Опубликована: Авг. 8, 2022

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast survival chances can be improved by early detection diagnosis. For medical image analyzers, diagnosing tough, time-consuming, routine, repetitive. Medical analysis could useful method for detecting such Recently, artificial intelligence technology has been utilized help radiologists identify more rapidly reliably. Convolutional neural networks, among other technologies, are promising recognition classification tools. This study proposes framework automatic reliable based on histological ultrasound data. The system built CNN employs transfer learning metaheuristic optimization. Manta Ray Foraging Optimization (MRFO) approach deployed improve the framework's adaptability. Using Cancer Dataset (two classes) Ultrasound (three-classes), eight modern pre-trained architectures examined apply technique. uses MRFO performance of optimizing their hyperparameters. Extensive experiments have recorded parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, cosine similarity. proposed scored 97.73% histopathological data 99.01% in terms accuracy. experimental results show that superior state-of-the-art approaches literature review.

Язык: Английский

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges DOI Open Access
Kanchan Rajwar, Kusum Deep, Swagatam Das

и другие.

Artificial Intelligence Review, Год журнала: 2023, Номер 56(11), С. 13187 - 13257

Опубликована: Апрель 9, 2023

Язык: Английский

Процитировано

249
Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review DOI Open Access
Mehrdad Kaveh, Mohammad Saadi Mesgari

Neural Processing Letters, Год журнала: 2022, Номер 55(4), С. 4519 - 4622

Опубликована: Окт. 31, 2022

Язык: Английский

Процитировано

135
Multiclass feature selection with metaheuristic optimization algorithms: a review DOI Open Access
Olatunji Akinola, Absalom E. Ezugwu, Jeffrey O. Agushaka

и другие.

Neural Computing and Applications, Год журнала: 2022, Номер 34(22), С. 19751 - 19790

Опубликована: Авг. 30, 2022

Язык: Английский

Процитировано

127
Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm DOI Open Access

Tianqing Hu,

Mohammad Khishe, Mokhtar Mohammadi

и другие.

Biomedical Signal Processing and Control, Год журнала: 2021, Номер 68, С. 102764 - 102764

Опубликована: Май 11, 2021

Язык: Английский

Процитировано

125
Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images DOI Open Access
Chengfeng Cai,

Bingchen Gou,

Mohammad Khishe

и другие.

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 119206 - 119206

Опубликована: Ноя. 4, 2022

Язык: Английский

Процитировано

123
Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems DOI Creative Commons
M. Premkumar, Garima Sinha,

R. Manjula Devi

и другие.

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

Опубликована: Март 5, 2024

Abstract This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve optimization capabilities of conventional optimizer in order address problem data clustering. The process that groups similar items within dataset into non-overlapping groups. Grey hunting behaviour served as model for however, it frequently lacks exploration and exploitation are essential efficient work mainly focuses on enhancing using weight factor concepts increase variety avoid premature convergence. Using partitional clustering-inspired fitness function, was extensively evaluated ten numerical functions multiple real-world datasets with varying levels complexity dimensionality. methodology is based incorporating concept purpose refining initial solutions adding diversity during phase. results show performs much better than standard discovering optimal clustering solutions, indicating higher capacity effective solution space. found able produce high-quality cluster centres fewer iterations, demonstrating its efficacy efficiency various datasets. Finally, demonstrates robustness dependability resolving issues, which represents significant advancement over techniques. In addition addressing shortcomings algorithm, incorporation innovative establishes further metaheuristic algorithms. performance around 34% original both test problems problems.

Язык: Английский

Процитировано

30
Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems DOI Creative Commons
Kanak Kalita, Janjhyam Venkata Naga Ramesh, Róbert Čep

и другие.

Heliyon, Год журнала: 2024, Номер 10(5), С. e26665 - e26665

Опубликована: Март 1, 2024

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by growth and proliferation patterns of liver tumors. MOLCA emulates evolutionary tendencies tumors, leveraging their expansion dynamics as model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with Random Opposition-Based Learning (ROBL) strategy, optimizing both local global search capabilities. Further enhancement is achieved through integration elitist non-dominated sorting (NDS), information feedback mechanism (IFM) Crowding Distance (CD) selection method, which collectively aim to efficiently identify Pareto optimal front. performance rigorously assessed using comprehensive set standard test benchmarks, including ZDT, DTLZ various Constraint (CONSTR, TNK, SRN, BNH, OSY KITA) real-world design like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss Welded beam. Its efficacy benchmarked against prominent algorithms such grey wolf optimizer (NSGWO), multiobjective multi-verse (MOMVO), (NSGA-II), decomposition-based (MOEA/D) marine predator (MOMPA). Quantitative analysis conducted GD, IGD, SP, SD, HV RT metrics represent convergence distribution, while qualitative aspects are presented graphical representations fronts. source code available at: https://github.com/kanak02/MOLCA.

Язык: Английский

Процитировано

18
An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems DOI Creative Commons
Mohammad H. Nadimi-Shahraki, Ali Fatahi, Hoda Zamani

и другие.

Entropy, Год журнала: 2021, Номер 23(12), С. 1637 - 1637

Опубликована: Дек. 6, 2021

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward light source is an effective approach to solve global problems. However, MFO suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration exploitation. In this study, therefore, improved moth-flame (I-MFO) proposed cope with canonical MFO's locating trapped in optimum via defining memory for each moth. The tend escape taking advantage adapted wandering around search (AWAS) strategy. efficiency I-MFO evaluated CEC 2018 benchmark functions compared against other well-known metaheuristic algorithms. Moreover, obtained results are statistically analyzed Friedman test on 30, 50, 100 dimensions. Finally, ability find best optimal solutions mechanical engineering problems three latest test-suite 2020. experimental statistical demonstrate that significantly superior contender algorithms it successfully upgrades shortcomings MFO.

Язык: Английский

Процитировано

67
A new optimization algorithm based on mimicking the voting process for leader selection DOI Creative Commons
Pavel Trojovský, Mohammad Dehghani

PeerJ Computer Science, Год журнала: 2022, Номер 8, С. e976 - e976

Опубликована: Май 13, 2022

Stochastic-based optimization algorithms are effective approaches to addressing challenges. In this article, a new algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics voting process select leader. The fundamental inspiration of EBOA process, selection leader, and impact public awareness level on population is guided by search space under guidance elected EBOA’s mathematically modeled in two phases: exploration exploitation. efficiency has been investigated solving thirty-three objective functions variety unimodal, high-dimensional multimodal, fixed-dimensional CEC 2019 types. implementation results show its high ability global search, exploitation local as well strike proper balance between which led proposed approach optimizing providing appropriate solutions. Our analysis shows provides an and, therefore, better more competitive performance than ten other it compared.

Язык: Английский

Процитировано

65
RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images DOI Creative Commons
Chao Wu, Mohammad Khishe, Mokhtar Mohammadi

и другие.

Soft Computing, Год журнала: 2021, Номер 27(6), С. 3307 - 3326

Опубликована: Май 10, 2021

Язык: Английский

Процитировано

58