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Amiri MH, Mehrabi Hashjin N, Montazeri M, Mirjalili S, Khodadadi N. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci Rep 2024; 14:5032. [PMID: 38424229 PMCID: PMC10904400 DOI: 10.1038/s41598-024-54910-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/18/2024] [Indexed: 03/02/2024] Open
Abstract
The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .
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Affiliation(s)
| | | | - Mohsen Montazeri
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Adelaide, Australia
- Research and Innovation Center, Obuda University, Budapest, 1034, Hungary
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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Al-Tashi Q, Saad MB, Sheshadri A, Wu CC, Chang JY, Al-Lazikani B, Gibbons C, Vokes NI, Zhang J, Lee JJ, Heymach JV, Jaffray D, Mirjalili S, Wu J. SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. Patterns (N Y) 2023; 4:100777. [PMID: 37602223 PMCID: PMC10435962 DOI: 10.1016/j.patter.2023.100777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/18/2023] [Accepted: 05/26/2023] [Indexed: 08/22/2023]
Abstract
Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.
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Affiliation(s)
- Qasem Al-Tashi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Joe Y. Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bissan Al-Lazikani
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Christopher Gibbons
- Section of Patient-Centered Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Rezaei F, Safavi HR, Abd Elaziz M, Mirjalili S. GMO: geometric mean optimizer for solving engineering problems. Soft comput 2023; 27:10571-10606. [DOI: 10.1007/s00500-023-08202-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 09/01/2023]
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Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. Arch Comput Methods Eng 2023; 30:1-47. [PMID: 37359740 PMCID: PMC10220350 DOI: 10.1007/s11831-023-09928-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
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Affiliation(s)
- Mohammad H. Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006 Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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Khalid AM, Hamza HM, Mirjalili S, Hosny KM. MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems. Neural Comput Appl 2023; 35:1-29. [PMID: 37362577 PMCID: PMC10153059 DOI: 10.1007/s00521-023-08587-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/05/2023] [Indexed: 06/28/2023]
Abstract
A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p (Δ P ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.
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Affiliation(s)
- Asmaa M. Khalid
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt
| | - Hanaa M. Hamza
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia
| | - Khaid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt
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Al-Tashi Q, Saad MB, Muneer A, Qureshi R, Mirjalili S, Sheshadri A, Le X, Vokes NI, Zhang J, Wu J. Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review. Int J Mol Sci 2023; 24:7781. [PMID: 37175487 PMCID: PMC10178491 DOI: 10.3390/ijms24097781] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/10/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023] Open
Abstract
The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.
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Affiliation(s)
- Qasem Al-Tashi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amgad Muneer
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rizwan Qureshi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Al-Betar MA, Awadallah MA, Makhadmeh SN, Alyasseri ZAA, Al-Naymat G, Mirjalili S. Marine Predators Algorithm: A Review. Arch Comput Methods Eng 2023; 30:3405-3435. [PMID: 37260911 PMCID: PMC10115392 DOI: 10.1007/s11831-023-09912-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/05/2023] [Indexed: 06/02/2023]
Abstract
Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.
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Affiliation(s)
- Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
- Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Irbid, Jordan
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Zaid Abdi Alkareem Alyasseri
- Information Technology Research and Development Center (ITRDC), University of Kufa, An Najaf, 54001 Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbalä’, Iraq
| | - Ghazi Al-Naymat
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Seyedali Mirjalili
- Center for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia
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Khodadadi N, Abualigah L, Al-Tashi Q, Mirjalili S. Multi-objective chaos game optimization. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08432-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
AbstractThe Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.
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Alhenawi E, Al-Sayyed R, Hudaib A, Mirjalili S. Improved intelligent water drop-based hybrid feature selection method for microarray data processing. Comput Biol Chem 2023; 103:107809. [PMID: 36696844 DOI: 10.1016/j.compbiolchem.2022.107809] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 12/13/2022] [Accepted: 12/30/2022] [Indexed: 01/15/2023]
Abstract
Classifying microarray datasets, which usually contains many noise genes that degrade the performance of classifiers and decrease classification accuracy rate, is a competitive research topic. Feature selection (FS) is one of the most practical ways for finding the most optimal subset of genes that increases classification's accuracy for diagnostic and prognostic prediction of tumor cancer from the microarray datasets. This means that we always need to develop more efficient FS methods, that select only optimal or close-to-optimal subset of features to improve classification performance. In this paper, we propose a hybrid FS method for microarray data processing, that combines an ensemble filter with an Improved Intelligent Water Drop (IIWD) algorithm as a wrapper by adding one of three local search (LS) algorithms: Tabu search (TS), Novel LS algorithm (NLSA), or Hill Climbing (HC) in each iteration from IWD, and using a correlation coefficient filter as a heuristic undesirability (HUD) for next node selection in the original IWD algorithm. The effects of adding three different LS algorithms to the proposed IIWD algorithm have been evaluated through comparing the performance of the proposed ensemble filter-IIWD-based wrapper without adding any LS algorithms named (PHFS-IWD) FS method versus its performance when adding a specific LS algorithm from (TS, NLSA or HC) in FS methods named, (PHFS-IWDTS, PHFS-IWDNLSA, and PHFS-IWDHC), respectively. Naïve Bayes(NB) classifier with five microarray datasets have been deployed for evaluating and comparing the proposed hybrid FS methods. Results show that using LS algorithms in each iteration from the IWD algorithm improves F-score value with an average equal to 5% compared with PHFS-IWD. Also, PHFS-IWDNLSA improves the F-score value with an average of 4.15% over PHFS-IWDTS, and 5.67% over PHFS-IWDHC while PHFS-IWDTS outperformed PHFS-IWDHC with an average of increment equal to 1.6%. On the other hand, the proposed hybrid-based FS methods improve accuracy with an average equal to 8.92% in three out of five datasets and decrease the number of genes with a percentage of 58.5% in all five datasets compared with six of the most recent state-of-the-art FS methods.
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Affiliation(s)
- Esra'a Alhenawi
- Software Engineering Department, Al-Ahliyya Amman University, Amman, Jordan; King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
| | - Rizik Al-Sayyed
- King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
| | - Amjad Hudaib
- King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
| | - Seyedali Mirjalili
- Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia; University Research and Innovation Center, Obuda University, Budapest, Hungary.
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Cai C, Gou B, Khishe M, Mohammadi M, Rashidi S, Moradpour R, Mirjalili S. Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images. Expert Syst Appl 2023; 213:119206. [PMID: 36348736 PMCID: PMC9633109 DOI: 10.1016/j.eswa.2022.119206] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 05/11/2023]
Abstract
Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
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Affiliation(s)
- Chengfeng Cai
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bingchen Gou
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Mohammad Khishe
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Shima Rashidi
- Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
| | - Reza Moradpour
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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Cai C, Gou B, Khishe M, Mohammadi M, Rashidi S, Moradpour R, Mirjalili S. Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images. Expert Syst Appl 2023; 213:119206. [PMID: 36348736 DOI: 10.1016/j.eswa.2020.113338] [Citation(s) in RCA: 161] [Impact Index Per Article: 161.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 05/25/2023]
Abstract
Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
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Affiliation(s)
- Chengfeng Cai
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bingchen Gou
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Mohammad Khishe
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Shima Rashidi
- Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
| | - Reza Moradpour
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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13
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Abdullah JM, Rashid TA, Maaroof BB, Mirjalili S. Multi-objective fitness-dependent optimizer algorithm. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08332-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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14
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Romeh AE, Mirjalili S. Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic Coordinated Multi-Robot Exploration. Sensors (Basel) 2023; 23:s23042156. [PMID: 36850750 PMCID: PMC9967542 DOI: 10.3390/s23042156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/25/2023] [Accepted: 02/10/2023] [Indexed: 06/12/2023]
Abstract
Multi-robot exploration means constructing a finite map using a group of robots in an obstacle chaotic space. Uncertainties are reduced by distributing search tasks to robots and computing the best action in real time. Many previous methods are based on deterministic or meta-heuristic algorithms, but limited work has combined both techniques to consolidate both classes' benefits and alleviate their drawbacks. This paper proposes a new hybrid method based on deterministic coordinated multi-robot exploration (CME) and the meta-heuristic salp swarm algorithm (SSA) to perform the search of a space. The precedence of adjacent cells around a robot is determined by deterministic CME using cost and utility. Then, the optimization process of the search space, improving the overall solution, is achieved utilizing the SSA. Three performance measures are considered to evaluate the performance of the proposed method: run time, percentage of the explored area, and the number of times when a method failed to continue a complete run. Experimental results compared four different methods, CME-GWO, CME-GWOSSA, CME-SCA, and CME, over seven maps with extra complexity varying from simple to complex. The results demonstrate how the proposed CME-SSA can outperform the four other methods. Moreover, the simulation results demonstrate that the proposed CME-SSA effectively distributes the robots over the search space to run successfully and obtain the highest exploration rate in less time.
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Affiliation(s)
- Ali El Romeh
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane 4006, Australia
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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15
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Shami TM, Mirjalili S, Al-Eryani Y, Daoudi K, Izadi S, Abualigah L. Velocity pausing particle swarm optimization: a novel variant for global optimization. Neural Comput Appl 2023. [DOI: 10.1007/s00521-022-08179-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
AbstractParticle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso.
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Ryalat MH, Dorgham O, Tedmori S, Al-Rahamneh Z, Al-Najdawi N, Mirjalili S. Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. Neural Comput Appl 2023; 35:6855-6873. [PMID: 36471798 PMCID: PMC9714421 DOI: 10.1007/s00521-022-08078-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 11/22/2022] [Indexed: 12/04/2022]
Abstract
Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.
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Affiliation(s)
- Mohammad Hashem Ryalat
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Osama Dorgham
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan ,grid.461585.b0000 0004 1762 8208School of Information Technology, Skyline University College, Sharjah, United Arab Emirates
| | - Sara Tedmori
- grid.29251.3d0000 0004 0404 9637King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, 11941 Jordan
| | - Zainab Al-Rahamneh
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Nijad Al-Najdawi
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Seyedali Mirjalili
- grid.449625.80000 0004 4654 2104Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, SA 5000 Australia ,grid.15444.300000 0004 0470 5454Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
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17
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Nadimi-Shahraki MH, Taghian S, Zamani H, Mirjalili S, Elaziz MA. MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems. PLoS One 2023; 18:e0280006. [PMID: 36595557 PMCID: PMC9810208 DOI: 10.1371/journal.pone.0280006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/19/2022] [Indexed: 01/04/2023] Open
Abstract
Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE. It introduces novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP) that can effectively collaborate with canonical MKE (MKE-TVP) using a multi-trial vector approach to tackle various real-world optimization problems with diverse challenges. It is expected that the proposed MMKE can improve the global search capability, strike a balance between exploration and exploitation, and prevent the original MKE algorithm from converging prematurely during the optimization process. The performance of the MMKE was assessed using CEC 2018 test functions, and the results were compared with eight metaheuristic algorithms. As a result of the experiments, it is demonstrated that the MMKE algorithm is capable of producing competitive and superior results in terms of accuracy and convergence rate in comparison to comparative algorithms. Additionally, the Friedman test was used to examine the gained experimental results statistically, proving that MMKE is significantly superior to comparative algorithms. Furthermore, four real-world engineering design problems and the optimal power flow (OPF) problem for the IEEE 30-bus system are optimized to demonstrate MMKE's real applicability. The results showed that MMKE can effectively handle the difficulties associated with engineering problems and is able to solve single and multi-objective OPF problems with better solutions than comparative algorithms.
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Affiliation(s)
- Mohammad H. Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Adelaide, Australia
- * E-mail: ,
| | - Shokooh Taghian
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Adelaide, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
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18
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Alburaikan A, Abd El-Wahed Khalifa H, Kumar P, Mirjalili S, Mekawy I. Mathematical Modeling and Evaluation of Reliability Parameters Based on Survival Possibilities under Uncertain Environment. Computer Modeling in Engineering & Sciences 2023; 134:1943-1956. [DOI: 10.32604/cmes.2022.021815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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19
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Ahmed R, Rangaiah GP, Mahadzir S, Mirjalili S, Hassan MH, Kamel S. Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction technique. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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20
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Chatterjee B, Acharya S, Bhattacharyya T, Mirjalili S, Sarkar R. Stock market prediction using Altruistic Dragonfly Algorithm. PLoS One 2023; 18:e0282002. [PMID: 37058503 PMCID: PMC10104289 DOI: 10.1371/journal.pone.0282002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 01/30/2023] [Indexed: 04/15/2023] Open
Abstract
Stock market prediction is the process of determining the value of a company's shares and other financial assets in the future. This paper proposes a new model where Altruistic Dragonfly Algorithm (ADA) is combined with Least Squares Support Vector Machine (LS-SVM) for stock market prediction. ADA is a meta-heuristic algorithm which optimizes the parameters of LS-SVM to avoid local minima and overfitting, resulting in better prediction performance. Experiments have been performed on 12 datasets and the obtained results are compared with other popular meta-heuristic algorithms. The results show that the proposed model provides a better predictive ability and demonstrate the effectiveness of ADA in optimizing the parameters of LS-SVM.
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Affiliation(s)
- Bitanu Chatterjee
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Sayan Acharya
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Trinav Bhattacharyya
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Fortitude Valley, Queensland, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
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21
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Bhattacharjee A, Murugan R, Goel T, Mirjalili S. Pulmonary nodule segmentation framework based on fine-tuned and pre-trained deep neural network using CT images. IEEE Trans Radiat Plasma Med Sci 2023. [DOI: 10.1109/trpms.2023.3236719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Ananya Bhattacharjee
- Bio-Medical Imaging Laboratory (BIOMIL), National Institute of Technology Silchar, Assam, India
| | - R Murugan
- Bio-Medical Imaging Laboratory (BIOMIL), National Institute of Technology Silchar, Assam, India
| | - Tripti Goel
- Bio-Medical Imaging Laboratory (BIOMIL), National Institute of Technology Silchar, Assam, India
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD, Australia
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22
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Pramanik P, Mukhopadhyay S, Mirjalili S, Sarkar R. Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms. Neural Comput Appl 2023; 35:5479-5499. [PMID: 36373132 PMCID: PMC9638217 DOI: 10.1007/s00521-022-07895-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 09/25/2022] [Indexed: 11/06/2022]
Abstract
Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.
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Affiliation(s)
- Payel Pramanik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | | | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006 Australia ,Yonsei Frontier Lab, Yonsei University, Seoul, South Korea ,University Research and Innovation Center, Óbuda University, Budapest, 1034 Hungary
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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23
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Hosny KM, Khalid AM, Hamza HM, Mirjalili S. Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function. Neural Comput Appl 2023; 35:855-886. [PMID: 36187233 PMCID: PMC9510310 DOI: 10.1007/s00521-022-07718-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/09/2022] [Indexed: 01/11/2023]
Abstract
Image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.
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Affiliation(s)
- Khalid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt
| | - Asmaa M. Khalid
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt
| | - Hanaa M. Hamza
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia
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Qaraad M, Amjad S, Hussein NK, Mirjalili S, Elhosseini MA. An innovative time-varying particle swarm-based Salp algorithm for intrusion detection system and large-scale global optimization problems. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10322-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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25
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Rezaei F, Safavi HR, Abd Elaziz M, Abualigah L, Mirjalili S, Gandomi AH. Diversity-Based Evolutionary Population Dynamics: A New Operator for Grey Wolf Optimizer. Processes (Basel) 2022; 10:2615. [DOI: 10.3390/pr10122615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB-EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD. In this algorithm, the three most diversified individuals are first identified at each iteration, and then half of the best-fitted individuals are forced to be eliminated and repositioned around these diversified agents with equal probability. This process can free the merged best individuals located in a closed populated region and transfer them to the diversified and, thus, less-densely populated regions in the search space. This approach is frequently employed to make the search agents explore the whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shifted classical benchmark functions as well as 29 test problems included in the CEC2017 test suite, and four constrained engineering problems. The results obtained by the proposal upon implemented on the classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newly proposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). The experiments demonstrate the significant superiority of the proposed algorithm when applied to a majority of the test functions, recommending the application of the proposed EPD operator to any other meta-heuristic whenever decided to ameliorate their performance.
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Sumit SS, Rambli DRA, Mirjalili S, Miah MSU, Ejaz MM. ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy. MethodsX 2022; 10:101936. [PMID: 36578294 PMCID: PMC9791404 DOI: 10.1016/j.mex.2022.101936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Human detection is an important task in computer vision. It is one of the most important tasks in global security and safety monitoring. In recent days, Deep Learning has improved human detection technology. Despite modern techniques, there are very few optimal techniques to construct networks with a small size, deep architecture, and fast training time while maintaining accuracy. ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets. ReSTiNet is 10.7 MB, five times smaller than Tiny-YOLO. On Tesla k80, mAP is 27.3% for MS COCO and 63.74% for PASCAL VOC. The validation of the proposed ReSTiNet model has been done on INRIA person dataset using the Tesla K80.•All the necessary steps, algorithms, and mathematical formulas for building the net- work are provided.•The network is small in size but has a faster detection speed with high accuracy.
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Affiliation(s)
- Shahriar Shakir Sumit
- Department of Computer & Information Sciences, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak 32610, Malaysia,Corresponding author.
| | - Dayang Rohaya Awang Rambli
- Department of Computer & Information Sciences, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak 32610, Malaysia
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia,Yonsei Frontier Lab, Yonsei University, Seoul, South Korea,University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| | - M. Saef Ullah Miah
- Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan 26600, Malaysia
| | - Muhammad Mudassir Ejaz
- Electrical & Electronics Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak 32610, Malaysia
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Ahmed RM, Rashid TA, Fattah P, Alsadoon A, Bacanin N, Mirjalili S, Vimal S, Chhabra A. Kurdish Handwritten character recognition using deep learning techniques. Gene Expr Patterns 2022; 46:119278. [PMID: 36195308 DOI: 10.1016/j.gep.2022.119278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/04/2022]
Abstract
Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.
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Affiliation(s)
- Rebin M Ahmed
- IT Department, Faculty of Aplied Science, Tishk International University, Erbil, Iraq.
| | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq.
| | - Polla Fattah
- Software and Informatics Engineering, Salahaddin University-Erbil, Erbil, Iraq.
| | - Abeer Alsadoon
- School of Computing Engineering and Mathematics, Western Sydney University, Sydney City Campus, Australia; Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia; Kent Institute Australia, Information Technology Department, Sydney, Australia.
| | | | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
| | - S Vimal
- Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, North Venganallur Village, Rajapalayam 626 117, Virudhunagar District, Tamilnadu, India.
| | - Amit Chhabra
- Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar India.
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Singh N, Houssein EH, Mirjalili S, Cao Y, Selvachandran G. An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large‐scale optimization applications. INT J INTELL SYST 2022. [DOI: 10.1002/int.23091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Narinder Singh
- Department of Mathematics Punjabi University Patiala Punjab India
| | | | - Seyedali Mirjalili
- Institute for Integrated and Intelligent Systems Griffith University Nathan Queensland Australia
| | - Yankai Cao
- Faculty of Applied Science, UBC Chemical and Biological Engineering The University of British Columbia Vancouver British Columbia Canada
| | - Ganeshsree Selvachandran
- Department of Actuarial Science and Applied Statistics, Faculty of Business and Management UCSI University, Jalan Menara Gading Cheras Kuala Lumpur Malaysia
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Rezaei F, Safavi HR, aziz MAE, Mirjalili S. GMO: Geometric Mean Optimizer for Solving Engineering Problems.. [DOI: 10.21203/rs.3.rs-2052464/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Abstract
This paper introduces a new meta-heuristic technique, named Geometric Mean Optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent’s opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly-proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems.
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Raj A, Brablik J, Kahankova R, Jaros R, Barnova K, Snasel V, Mirjalili S, Martinek R. Nature inspired method for noninvasive fetal ECG extraction. Sci Rep 2022; 12:20159. [PMID: 36418487 PMCID: PMC9684417 DOI: 10.1038/s41598-022-24733-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022] Open
Abstract
This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women's body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1-14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.
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Affiliation(s)
- Akshaya Raj
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Jindrich Brablik
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Radana Kahankova
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Rene Jaros
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Katerina Barnova
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Vaclav Snasel
- grid.440850.d0000 0000 9643 2828Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Seyedali Mirjalili
- grid.449625.80000 0004 4654 2104Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, 90 Bowen Terrace, Brisbane, QLD 4006 Australia
| | - Radek Martinek
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
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Khafaga DS, Ibrahim A, El-Kenawy ESM, Abdelhamid AA, Karim FK, Mirjalili S, Khodadadi N, Lim WH, Eid MM, Ghoneim ME. An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease. Diagnostics (Basel) 2022; 12:diagnostics12112892. [PMID: 36428952 PMCID: PMC9689640 DOI: 10.3390/diagnostics12112892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/04/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework's efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.
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Affiliation(s)
- Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - El-Sayed M. El-Kenawy
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
| | - Nima Khodadadi
- Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USA
| | - Wei Hong Lim
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia
| | - Marwa M. Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - Mohamed E. Ghoneim
- Department of Mathematical Sciences, Faculty of Applied Science, Umm Al-Qura University, Makkah 21955, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Damietta University, Damietta 34511, Egypt
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32
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Tirkolaee EB, Goli A, Mirjalili S. Circular economy application in designing sustainable medical waste management systems. Environ Sci Pollut Res Int 2022; 29:79667-79668. [PMID: 35578082 PMCID: PMC9110080 DOI: 10.1007/s11356-022-20740-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
| | - Alireza Goli
- Department of Industrial Engineering and Future Studies Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
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Hosny KM, Khalid AM, Hamza HM, Mirjalili S. Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm. Comput Biol Med 2022; 150:106003. [PMID: 36228462 PMCID: PMC9398848 DOI: 10.1016/j.compbiomed.2022.106003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/10/2022] [Accepted: 08/14/2022] [Indexed: 12/01/2022]
Abstract
Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitness functions to determine the optimum threshold values. The proposed algorithm applies the hybridization concept between the recent Coronavirus Optimization Algorithm (COVIDOA) and Harris Hawks Optimization Algorithm (HHOA) to benefit from both algorithms' strengths and overcome their limitations. The improved performance of the proposed algorithm over COVIDOA and HHOA algorithms is demonstrated by solving 5 test problems from IEEE CEC 2019 benchmark problems. Medical image segmentation is tested using two groups of images, including 2D medical images and volumetric (3D) medical images, to demonstrate its superior performance. The utilized test images are from different modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray images. The proposed algorithm is compared with seven well-known metaheuristic algorithms, where the performance is evaluated using four different metrics, including the best fitness values, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation Coefficient (NCC). The experimental results demonstrate the superior performance of the proposed algorithm in terms of convergence to the global optimum and making a good balance between exploration and exploitation properties. Moreover, the quality of the segmented images using the proposed algorithm at different threshold levels is better than the other methods according to PSNR, SSIM, and NCC values. Additionally, the Wilcoxon rank-sum test is conducted to prove the statistical significance of the proposed algorithm.
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Affiliation(s)
- Khalid M Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt.
| | - Asmaa M Khalid
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| | - Hanaa M Hamza
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, 4006, QLD, Australia
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Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mirjalili S. Evolutionary mating algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07761-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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35
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Pramanik R, Dey S, Malakar S, Mirjalili S, Sarkar R. TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images. Sci Rep 2022; 12:15409. [PMID: 36104401 PMCID: PMC9471038 DOI: 10.1038/s41598-022-18463-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
The novel coronavirus (COVID-19), has undoubtedly imprinted our lives with its deadly impact. Early testing with isolation of the individual is the best possible way to curb the spread of this deadly virus. Computer aided diagnosis (CAD) provides an alternative and cheap option for screening of the said virus. In this paper, we propose a convolution neural network (CNN)-based CAD method for COVID-19 and pneumonia detection from chest X-ray images. We consider three input types for three identical base classifiers. To capture maximum possible complementary features, we consider the original RGB image, Red channel image and the original image stacked with Robert's edge information. After that we develop an ensemble strategy based on the technique for order preference by similarity to an ideal solution (TOPSIS) to aggregate the outcomes of base classifiers. The overall framework, called TOPCONet, is very light in comparison with standard CNN models in terms of the number of trainable parameters required. TOPCONet achieves state-of-the-art results when evaluated on the three publicly available datasets: (1) IEEE COVID-19 dataset + Kaggle Pneumonia Dataset, (2) Kaggle Radiography dataset and (3) COVIDx.
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Nenavath H, Ashwini K, Jatoth RK, Mirjalili S. Intelligent Trigonometric Particle Filter for visual tracking. ISA Trans 2022; 128:460-476. [PMID: 34610870 DOI: 10.1016/j.isatra.2021.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers.
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Affiliation(s)
- Hathiram Nenavath
- Department of Electronics and Communication Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, 501218, India.
| | - K Ashwini
- Department of Electronics and Communication Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, 501218, India.
| | | | - Seyedali Mirjalili
- Torrens University Australia, Fortitude Valley Brisbane, QLD, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
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Khalid AM, Hosny KM, Mirjalili S. COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle. Neural Comput Appl 2022; 34:22465-22492. [PMID: 36043205 PMCID: PMC9411047 DOI: 10.1007/s00521-022-07639-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 07/18/2022] [Indexed: 11/13/2022]
Abstract
This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics.
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Khodadadi N, Soleimanian Gharehchopogh F, Mirjalili S. MOAVOA: a new multi-objective artificial vultures optimization algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07557-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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39
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Ahmed S, Sheikh KH, Mirjalili S, Sarkar R. Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets. Expert Syst Appl 2022; 200:116834. [PMID: 36034050 PMCID: PMC9396289 DOI: 10.1016/j.eswa.2022.116834] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/25/2022] [Accepted: 03/03/2022] [Indexed: 05/04/2023]
Abstract
Classification accuracy achieved by a machine learning technique depends on the feature set used in the learning process. However, it is often found that all the features extracted by some means for a particular task do not contribute to the classification process. Feature selection (FS) is an imperative and challenging pre-processing technique that helps to discard the unnecessary and irrelevant features while reducing the computational time and space requirement and increasing the classification accuracy. Generalized Normal Distribution Optimizer (GNDO), a recently proposed meta-heuristic algorithm, can be used to solve any optimization problem. In this paper, a hybrid version of GNDO with Simulated Annealing (SA) called Binary Simulated Normal Distribution Optimizer (BSNDO) is proposed which uses SA as a local search to achieve higher classification accuracy. The proposed method is evaluated on 18 well-known UCI datasets and compared with its predecessor as well as some popular FS methods. Moreover, this method is tested on high dimensional microarray datasets to prove its worth in real-life datasets. On top of that, it is also applied to a COVID-19 dataset for classification purposes. The obtained results prove the usefulness of BSNDO as a FS method. The source code of this work is publicly available at https://github.com/ahmed-shameem/Feature_selection.
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Affiliation(s)
- Shameem Ahmed
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Khalid Hassan Sheikh
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Seyedali Mirjalili
- King Abdulaziz University, Jeddah, Saudi Arabia
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
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Oyelade ON, Ezugwu AE, Venter HS, Mirjalili S, Gandomi AH. Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images. Comput Biol Med 2022; 149:105943. [PMID: 35986967 DOI: 10.1016/j.compbiomed.2022.105943] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/17/2022] [Accepted: 08/06/2022] [Indexed: 12/29/2022]
Abstract
The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architectures is proof of the outstanding performance reported in the literature. However, localizing abnormalities in regions of images that can support the confidence of classification continues to attract research interest. The difficulty of using digital histopathology images for this task is another drawback, which needs high-level deep learning models to address the situation. Successful pathology localization automation will support automatic acquisition planning and post-imaging analysis. In this paper, we address issues related to the combination of classification with image localization and detection through a dual branch deep learning framework that uses two different configurations of convolutional neural networks (CNN) architectures. Whole-image based CNN (WCNN) and region-based CNN (RCNN) architectures are systematically combined to classify and localize abnormalities in samples. A multi-class classification and localization of abnormalities are achieved using the method with no annotation-dependent images. In addition, seamless confidence and explanation mechanism is provided so that outcomes from WCNN and RCNN are mapped together for further analysis. Using images from both BACH and BreakHis databases, an exhaustive set of experiments was carried out to validate the performance of the proposed method in achieving classification and localization simultaneously. Obtained results showed that the system achieved a classification accuracy of 97.08%, a localization accuracy of 94%, and an area under the curve (AUC) of 0.10 for classification. Further findings from this study revealed that a multi-neural network approach could provide a suitable method for addressing the combinatorial problem of classification and localization anomalies in digital medical images. Lastly, the study's outcome offers means for automating the annotation of histopathology images and the support for human pathologists in locating abnormalities.
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Affiliation(s)
- Olaide N Oyelade
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
| | - Absalom E Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
| | - Hein S Venter
- Department of Computer Science, University of Pretoria, Pretoria, 0028, South Africa.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia.
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
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Islam MS, Awal MA, Laboni JN, Pinki FT, Karmokar S, Mumenin KM, Al-Ahmadi S, Rahman MA, Hossain MS, Mirjalili S. HGSORF: Henry Gas Solubility Optimization-based Random Forest for C-Section prediction and XAI-based cause analysis. Comput Biol Med 2022; 147:105671. [DOI: 10.1016/j.compbiomed.2022.105671] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 01/02/2023]
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42
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Mohammadi D, Abd Elaziz M, Moghdani R, Demir E, Mirjalili S. Quantum Henry gas solubility optimization algorithm for global optimization. Engineering with Computers 2022; 38:2329-2348. [DOI: 10.1007/s00366-021-01347-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/10/2021] [Indexed: 09/02/2023]
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43
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44
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Abualigah L, Mirjalili S, Otair M, Sumari P, Elaziz MA, Jia H, Gandomi AH. Boosting Moth-Flame Optimization Algorithm by Arithmetic Optimization Algorithm for Data Clustering. Handbook of Moth-Flame Optimization Algorithm 2022:209-237. [DOI: 10.1201/9781003205326-14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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45
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Khalid AM, Hamza HM, Mirjalili S, Hosny KM. BCOVIDOA: A Novel Binary Coronavirus Disease Optimization Algorithm for Feature Selection. Knowl Based Syst 2022; 248:108789. [PMID: 35464666 PMCID: PMC9014647 DOI: 10.1016/j.knosys.2022.108789] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 04/08/2022] [Accepted: 04/08/2022] [Indexed: 11/20/2022]
Abstract
The increased use of digital tools such as smart phones, Internet of Things devices, cameras, and microphones, has led to the produuction of big data. Large data dimensionality, redundancy, and irrelevance are inherent challenging problems when it comes to big data. Feature selection is a necessary process to select the optimal subset of features when addressing such problems. In this paper, the authors propose a novel Binary Coronavirus Disease Optimization Algorithm (BCOVIDOA) for feature selection, where the Coronavirus Disease Optimization Algorithm (COVIDOA) is a new optimization technique that mimics the replication mechanism used by Coronavirus when hijacking human cells. The performance of the proposed algorithm is evaluated using twenty-six standard benchmark datasets from UCI Repository. The results are compared with nine recent wrapper feature selection algorithms. The experimental results demonstrate that the proposed BCOVIDOA significantly outperforms the existing algorithms in terms of accuracy, best cost, the average cost (AVG), standard deviation (STD), and size of selected features. Additionally, the Wilcoxon rank-sum test is calculated to prove the statistical significance of the results.
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Affiliation(s)
- Asmaa M Khalid
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
| | - Hanaa M Hamza
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane 4006, QLD, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
| | - Khalid M Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
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46
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Abualigah L, Mirjalili S, Elaziz MA, Jia H, Şahin CB, Khalifeh A, Gandomi AH. Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization. Handbook of Moth-Flame Optimization Algorithm 2022:177-208. [DOI: 10.1201/9781003205326-13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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47
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Nadimi-Shahraki MH, Zamani H, Mirjalili S. Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Comput Biol Med 2022; 148:105858. [PMID: 35868045 DOI: 10.1016/j.compbiomed.2022.105858] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 06/15/2022] [Accepted: 07/08/2022] [Indexed: 01/01/2023]
Abstract
The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.
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Affiliation(s)
- Mohammad H Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia.
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
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Ramasamy Rajammal R, Mirjalili S, Ekambaram G, Palanisamy N. Binary Grey Wolf Optimizer with Mutation and Adaptive K-nearest Neighbour for Feature Selection in Parkinson’s Disease Diagnosis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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50
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Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN Comput Sci 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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