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Li M, Luo Q, Zhou Y. BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection. Biomimetics (Basel) 2024; 9:187. [PMID: 38534872 DOI: 10.3390/biomimetics9030187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
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Affiliation(s)
- Mengjun Li
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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Şahin CB. Semantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks. Soft comput 2023. [DOI: 10.1007/s00500-022-07777-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Beheshti Z. BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Mughaid A, Al-Zu’bi S, AL Arjan A, AL-Amrat R, Alajmi R, Zitar RA, Abualigah L. An intelligent cybersecurity system for detecting fake news in social media websites. Soft comput 2022; 26:5577-5591. [PMID: 35469124 PMCID: PMC9021563 DOI: 10.1007/s00500-022-07080-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2022] [Indexed: 02/05/2023]
Abstract
People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
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Affiliation(s)
- Ala Mughaid
- Department of Information Technology, Faculty of prince Al-Hussien bin Abdullah for IT, The Hashemite University, PO Box 330127, Zarqa, 13133 Jordan
| | - Shadi Al-Zu’bi
- Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, Jordan
| | - Ahmed AL Arjan
- Department of Information Technology, Faculty of prince Al-Hussien bin Abdullah for IT, The Hashemite University, PO Box 330127, Zarqa, 13133 Jordan
| | - Rula AL-Amrat
- Department of Information Technology, Faculty of prince Al-Hussien bin Abdullah for IT, The Hashemite University, PO Box 330127, Zarqa, 13133 Jordan
| | - Rathaa Alajmi
- Department of Information Technology, Faculty of prince Al-Hussien bin Abdullah for IT, The Hashemite University, PO Box 330127, Zarqa, 13133 Jordan
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, 38044 United Arab Emirates
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800 Malaysia
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Abualigah L, Al-Okbi NK, Elaziz MA, Houssein EH. Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:16707-16742. [PMID: 35261554 PMCID: PMC8892122 DOI: 10.1007/s11042-022-12001-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/12/2021] [Accepted: 01/04/2022] [Indexed: 05/27/2023]
Abstract
Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method's effectiveness. Several benchmark images are used to validate the proposed algorithm's performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature.
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Affiliation(s)
- Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| | - Nada Khalil Al-Okbi
- Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Mohamed Abd Elaziz
- Faculty of Computer Science & Engineering, Galala University, Suze, 435611 Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346 United Arab Emirates
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519 Egypt
- School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, 634050 Russia
| | - Essam H. Houssein
- Faculty of Computers and Information, Minia University, 61519 Minia, Egypt
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Correlation-based modified long short-term memory network approach for software defect prediction. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09423-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Dey P, Saurabh K, Kumar C, Pandit D, Chaulya SK, Ray SK, Prasad GM, Mandal SK. t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines. Soft comput 2021. [DOI: 10.1007/s00500-021-06261-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model. MATHEMATICS 2021. [DOI: 10.3390/math9182321] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.
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