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Mu G, Ju X, Yan H, Li J, Gao H, Li X. An Enhanced Misinformation Detection Model Based on an Improved Beluga Whale Optimization Algorithm and Cross-Modal Feature Fusion. Biomimetics (Basel) 2025; 10:128. [PMID: 40136782 PMCID: PMC11940676 DOI: 10.3390/biomimetics10030128] [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: 01/14/2025] [Revised: 02/07/2025] [Accepted: 02/19/2025] [Indexed: 03/27/2025] Open
Abstract
The proliferation of multimodal misinformation on social media has become a critical concern. Although detection methods have advanced, feature representation and cross-modal semantic alignment challenges continue to hinder the effective use of multimodal data. Therefore, this paper proposes an IBWO-CASC detection model that integrates an improved Beluga Whale Optimization algorithm with cross-modal attention feature fusion. Firstly, the Beluga Whale Optimization algorithm is enhanced by combining adaptive search mechanisms with batch parallel strategies in the feature space. Secondly, a feature alignment method is designed based on supervised contrastive learning to establish semantic consistency. Then, the model incorporates a Cross-modal Attention Promotion mechanism and global-local interaction learning pattern. Finally, a multi-task learning framework is built based on classification and contrastive objectives. The empirical analysis shows that the proposed IBWO-CASC model achieves a detection accuracy of 97.41% on our self-constructed multimodal misinformation dataset. Compared with the average accuracy of the existing six baseline models, the accuracy of this model is improved by 4.09%. Additionally, it demonstrates enhanced robustness in handling complex multimodal scenarios.
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Affiliation(s)
- Guangyu Mu
- School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China; (G.M.); (X.J.); (J.L.)
- Key Laboratory of Financial Technology of Jilin Province, Changchun 130117, China
| | - Xiaoqing Ju
- School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China; (G.M.); (X.J.); (J.L.)
| | - Hongduo Yan
- Changchun Administration Institute, Changchun 130103, China
| | - Jiaxue Li
- School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China; (G.M.); (X.J.); (J.L.)
| | - He Gao
- School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China; (G.M.); (X.J.); (J.L.)
| | - Xiurong Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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Kumar H, Dwivedi A, Mishra AK, Shukla AK, Sharma BK, Agarwal R, Kumar S. Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm. MethodsX 2024; 13:102839. [PMID: 39105091 PMCID: PMC11298652 DOI: 10.1016/j.mex.2024.102839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 07/02/2024] [Indexed: 08/07/2024] Open
Abstract
Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset. • We use the concepts of LBP and GLCM to extract features from the skin lesion images. • The Gray Wolf Optimization (GWO) algorithm is employed for feature selection. • A decoder based on Transformers is utilized for melanoma classification.
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Affiliation(s)
- Hemant Kumar
- Department of Information Technology, School of Engineering & Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, India
| | - Abhishek Dwivedi
- Department of Computer Applications, School of Engineering & Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, India
| | | | - Arvind Kumar Shukla
- Department of Computer Science & Applications, IFTM University, Moradabad, India
| | - Brajesh Kumar Sharma
- Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, India
| | - Rashi Agarwal
- Department of Computer Science & Engineering, Harcourt Butler Technical University, Kanpur, India
| | - Sunil Kumar
- Department of Information Technology, School of Engineering & Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, India
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Meng RH, Wang ZY, Zeng WH, Guan F, Lei DK, Wu ZJ, Deng SH. Research on low carbon welding scheduling based on production process. Sci Rep 2024; 14:28704. [PMID: 39567572 PMCID: PMC11579454 DOI: 10.1038/s41598-024-79555-0] [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: 02/27/2024] [Accepted: 11/11/2024] [Indexed: 11/22/2024] Open
Abstract
The welding workshop of metal structural parts is highly energy-consuming. To meet the national low-carbon green demand, this paper focus on the welding workshop scheduling problem in production process with considering the carbon footprints such as equipment energy consumption, welding material consumption and shielding gas consumption. Firstly, a bi-objective low-carbon welding scheduling mathematical model is established with minimizing makespan and carbon emission. Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. The grey wolf multi-wandering strategy (first) is proposed to enhance the population diversity. The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. A welding workshop green scheduling case is designed to verify the model and algorithm proposed in this paper. The minimum completion time and carbon emissions obtained by the IGWO algorithm are 842.14 and 3.85E + 05, respectively. This result is better than that obtained by NSGA-II and GWO.The results show that the model effectively reduce the carbon emissions of the workshop, and the algorithm can effectively solve the model.
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Affiliation(s)
- Rong Hua Meng
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, Hubei, China
- Intelligent Manufacturing Innovation Technology Center, China Three Gorges University, Yichang, Hubei, China
| | - Zan Yang Wang
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, Hubei, China
- Intelligent Manufacturing Innovation Technology Center, China Three Gorges University, Yichang, Hubei, China
| | - Wen Hui Zeng
- Hubei Provincial Engineering Research Center of Robotics & Intelligent Manufacturing, Wuhan University of Technology, Wuhan, Hubei, China.
| | - Feng Guan
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, Hubei, China.
- Intelligent Manufacturing Innovation Technology Center, China Three Gorges University, Yichang, Hubei, China.
| | - Ding Kun Lei
- China Three Gorges Materials and Tendering Management Co., Ltd., Chengdu, Sichuan, China
| | - Zheng Jia Wu
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, Hubei, China
- Intelligent Manufacturing Innovation Technology Center, China Three Gorges University, Yichang, Hubei, China
| | - Shao Hua Deng
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, Hubei, China
- Intelligent Manufacturing Innovation Technology Center, China Three Gorges University, Yichang, Hubei, China
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Ochoa-Ornelas R, Gudiño-Ochoa A, García-Rodríguez JA. A Hybrid Deep Learning and Machine Learning Approach with Mobile-EfficientNet and Grey Wolf Optimizer for Lung and Colon Cancer Histopathology Classification. Cancers (Basel) 2024; 16:3791. [PMID: 39594746 PMCID: PMC11593226 DOI: 10.3390/cancers16223791] [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: 10/14/2024] [Revised: 11/08/2024] [Accepted: 11/09/2024] [Indexed: 11/28/2024] Open
Abstract
BACKGROUND Lung and colon cancers are among the most prevalent and lethal malignancies worldwide, underscoring the urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning and machine learning framework for the classification of Colon Adenocarcinoma, Colon Benign Tissue, Lung Adenocarcinoma, Lung Benign Tissue, and Lung Squamous Cell Carcinoma from histopathological images. METHODS Current approaches primarily rely on the LC25000 dataset, which, due to image augmentation, lacks the generalizability required for real-time clinical applications. To address this, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image quality, and 1000 new images from the National Cancer Institute GDC Data Portal were introduced into the Colon Adenocarcinoma, Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma classes, replacing augmented images to increase dataset diversity. A hybrid feature extraction model combining MobileNetV2 and EfficientNetB3 was optimized using the Grey Wolf Optimizer (GWO), resulting in the Lung and Colon histopathological classification technique (MEGWO-LCCHC). Cross-validation and hyperparameter tuning with Optuna were performed on various machine learning models, including XGBoost, LightGBM, and CatBoost. RESULTS The MEGWO-LCCHC technique achieved high classification accuracy, with the lightweight DNN model reaching 94.8%, LightGBM at 93.9%, XGBoost at 93.5%, and CatBoost at 93.3% on the test set. CONCLUSIONS The findings suggest that our approach enhances classification performance and offers improved generalizability for real-world clinical applications. The proposed MEGWO-LCCHC framework shows promise as a robust tool in cancer diagnostics, advancing the application of AI in oncology.
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Affiliation(s)
- Raquel Ochoa-Ornelas
- Systems and Computation Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico
| | - Alberto Gudiño-Ochoa
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico;
| | - Julio Alberto García-Rodríguez
- Centro Universitario del Sur, Departamento de Ciencias Computacionales e Innovación Tecnológica, Universidad de Guadalajara, Ciudad Guzmán 49000, Mexico;
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Yang G, Li W, Xie W, Wang L, Yu K. An improved binary particle swarm optimization algorithm for clinical cancer biomarker identification in microarray data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107987. [PMID: 38157825 DOI: 10.1016/j.cmpb.2023.107987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/04/2023] [Accepted: 12/16/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND AND OBJECTIVE The limited number of samples and high-dimensional features in microarray data make selecting a small number of features for disease diagnosis a challenging problem. Traditional feature selection methods based on evolutionary algorithms are difficult to search for the optimal set of features in a limited time when dealing with the high-dimensional feature selection problem. New solutions are proposed to solve the above problems. METHODS In this paper, we propose a hybrid feature selection method (C-IFBPFE) for biomarker identification in microarray data, which combines clustering and improved binary particle swarm optimization while incorporating an embedded feature elimination strategy. Firstly, an adaptive redundant feature judgment method based on correlation clustering is proposed for feature screening to reduce the search space in the subsequent stage. Secondly, we propose an improved flipping probability-based binary particle swarm optimization (IFBPSO), better applicable to the binary particle swarm optimization problem. Finally, we also design a new feature elimination (FE) strategy embedded in the binary particle swarm optimization algorithm. This strategy gradually removes poorer features during iterations to reduce the number of features and improve accuracy. RESULTS We compared C-IFBPFE with other published hybrid feature selection methods on eight public datasets and analyzed the impact of each improvement. The proposed method outperforms other current state-of-the-art feature selection methods in terms of accuracy, number of features, sensitivity, and specificity. The ablation study of this method validates the efficacy of each component, especially the proposed feature elimination strategy significantly improves the performance of the algorithm. CONCLUSIONS The hybrid feature selection method proposed in this paper helps address the issue of high-dimensional microarray data with few samples. It can select a small subset of features and achieve high classification accuracy on microarray datasets. Additionally, independent validation of the selected features shows that those chosen by C-IFBPFE have strong correlations with disease phenotypes and can identify important biomarkers from data related to biomedical problems.
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Affiliation(s)
- Guicheng Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110000, Liaoning, China.
| | - Wei Li
- Key Laboratory of Intelligent Computing in Medical Image (MIIC), Northeastern University, Ministry of Education, Shenyang, 110000, Liaoning, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, 110819, Liaoning, China.
| | - Weidong Xie
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110000, Liaoning, China.
| | - Linjie Wang
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110000, Liaoning, China.
| | - Kun Yu
- College of Medicine and Bioinformation Engineering, Northeastern University, Shenyang, 110819, Liaoning, China.
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Barrera-García J, Cisternas-Caneo F, Crawford B, Gómez Sánchez M, Soto R. Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications. Biomimetics (Basel) 2023; 9:9. [PMID: 38248583 PMCID: PMC10813816 DOI: 10.3390/biomimetics9010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.
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Affiliation(s)
- José Barrera-García
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (J.B.-G.); (F.C.-C.); (R.S.)
| | - Felipe Cisternas-Caneo
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (J.B.-G.); (F.C.-C.); (R.S.)
| | - Broderick Crawford
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (J.B.-G.); (F.C.-C.); (R.S.)
| | - Mariam Gómez Sánchez
- Departamento de Electrotecnia e Informática, Universidad Técnica Federico Santa María, Federico Santa María 6090, Viña del Mar 2520000, Chile;
| | - Ricardo Soto
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (J.B.-G.); (F.C.-C.); (R.S.)
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Zhang S, Wang Q, Zhang B, Liang Z, Zhang L, Li L, Huang G, Zhang Z, Feng B, Yu T. Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding. Front Neurosci 2023; 17:1292724. [PMID: 38027478 PMCID: PMC10654780 DOI: 10.3389/fnins.2023.1292724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information. Methods In this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers. Results We conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively. Conclusion The experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.
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Affiliation(s)
- Shaorong Zhang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China
| | - Qihui Wang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Benxin Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Zhen Liang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Li Zhang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Linling Li
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Gan Huang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Zhiguo Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
| | - Bao Feng
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China
| | - Tianyou Yu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
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Saleh AI, Hussien SA. Disease Diagnosis Based on Improved Gray Wolf Optimization (IGWO) and Ensemble Classification. Ann Biomed Eng 2023; 51:2579-2605. [PMID: 37452216 DOI: 10.1007/s10439-023-03303-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023]
Abstract
This paper introduces a simple strategy for diagnosing disease, which is called improved gray wolf optimization (IGWO) and ensemble classification. The proposed strategy consists of two sequential phases, which are; (i) Feature Selection Phase (FSP) and (ii) Ensemble Classification Phase (ECP). During the former, the most effective features for diagnosing disease are selected, while during the latter, the actual diagnosis takes place depending on voting of five different classifiers. The main contribution of this paper is a suggested modification for the traditional Gray Wolf Optimization (GWO), which is called Improved Gray Wolf Optimization (IGWO). As an optimization technique, the proposed IGWO is employed in the FSP for selecting the effective features. For evaluating, IGWO has been implemented using recent feature selection techniques as well as the proposed method. To accomplish the classification phase; ensemble classification has been used which uses several classification techniques such as; Naïve Bayes (NB), Support Vector Machines (SVM), Deep Neural Network (DNN), Decision Tree (DT), and K-Nearest Neighbors (KNN). Ensemble classification integrate several classifiers for improving prediction performance. Experimental results have shown that employing IGWO promotes the performance of the diagnosing strategy of different diseases in terms of precision, recall, and accuracy.
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Affiliation(s)
- Ahmed I Saleh
- Computers and Control Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Shaimaa A Hussien
- Delta Higher Institute for Engineering and Technology, Mansoura, Egypt.
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Trojovská E, Dehghani M, Leiva V. Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering. Biomimetics (Basel) 2023; 8:239. [PMID: 37366834 DOI: 10.3390/biomimetics8020239] [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: 04/18/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023] Open
Abstract
Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications.
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Affiliation(s)
- Eva Trojovská
- Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic
| | - Mohammad Dehghani
- Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic
| | - Víctor Leiva
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
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Feature selection based on absolute deviation factor for text classification. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Muzoğlu N, Halefoğlu AM, Avci MO, Kaya Karaaslan M, Yarman BSB. Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods. EXPERT SYSTEMS 2022; 40:e13141. [PMID: 36245832 PMCID: PMC9537791 DOI: 10.1111/exsy.13141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/25/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine-Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.
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Affiliation(s)
- Nedim Muzoğlu
- Department of Biomedical Engineering, Faculty of EngineeringIstanbul University‐CerrahpasaIstanbulTurkey
| | - Ahmet Mesrur Halefoğlu
- Department of RadiologySisli Hamidiye Etfal Training and Research Hospital, Health Sciences UniversityIstanbulTurkey
| | - Muhammed Onur Avci
- Department of Biomedical Engineering, Faculty of EngineeringIstanbul University‐CerrahpasaIstanbulTurkey
| | - Melike Kaya Karaaslan
- Department of Biomedical SciencesFaculty of Engineering, Kocaeli UniversityKocaeliTurkey
| | - Bekir Sıddık Binboğa Yarman
- Department of Electrical‐Electronics Engineering, Faculty of EngineeringIstanbul University‐CerrahpasaIstanbulTurkey
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Qin X, Zhang S, Yin D, Chen D, Dong X. Two-stage feature selection for classification of gene expression data based on an improved Salp Swarm Algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:13747-13781. [PMID: 36654066 DOI: 10.3934/mbe.2022641] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Microarray technology has developed rapidly in recent years, producing a large number of ultra-high dimensional gene expression data. However, due to the huge sample size and dimension proportion of gene expression data, it is very challenging work to screen important genes from gene expression data. For small samples of high-dimensional biomedical data, this paper proposes a two-stage feature selection framework combining Wrapper, embedding and filtering to avoid the curse of dimensionality. The proposed framework uses weighted gene co-expression network (WGCNA), random forest and minimal redundancy maximal relevance (mRMR) for first stage feature selection. In the second stage, a new gene selection method based on the improved binary Salp Swarm Algorithm is proposed, which combines machine learning methods to adaptively select feature subsets suitable for classification algorithms. Finally, the classification accuracy is evaluated using six methods: lightGBM, RF, SVM, XGBoost, MLP and KNN. To verify the performance of the framework and the effectiveness of the proposed algorithm, the number of genes selected and the classification accuracy was compared with the other five intelligent optimization algorithms. The results show that the proposed framework achieves an accuracy equal to or higher than other advanced intelligent algorithms on 10 datasets, and achieves an accuracy of over 97.6% on all 10 datasets. This shows that the method proposed in this paper can solve the feature selection problem related to high-dimensional data, and the proposed framework has no data set limitation, and it can be applied to other fields involving feature selection.
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Affiliation(s)
- Xiwen Qin
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Shuang Zhang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Dongmei Yin
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Dongxue Chen
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Xiaogang Dong
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
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Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1825341. [PMID: 36072739 PMCID: PMC9441366 DOI: 10.1155/2022/1825341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/07/2022] [Accepted: 08/09/2022] [Indexed: 12/02/2022]
Abstract
With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).
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Online group streaming feature selection using entropy-based uncertainty measures for fuzzy neighborhood rough sets. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00763-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractOnline group streaming feature selection, as an essential online processing method, can deal with dynamic feature selection tasks by considering the original group structure information of the features. Due to the fuzziness and uncertainty of the feature stream, some existing methods are unstable and yield low predictive accuracy. To address these issues, this paper presents a novel online group streaming feature selection method (FNE-OGSFS) using fuzzy neighborhood entropy-based uncertainty measures. First, a separability measure integrating the dependency degree with the coincidence degree is proposed and introduced into the fuzzy neighborhood rough sets model to define a new fuzzy neighborhood entropy. Second, inspired by both algebra and information views, some fuzzy neighborhood entropy-based uncertainty measures are investigated and some properties are derived. Furthermore, the optimal features in the group are selected to flow into the feature space according to the significance of features, and the features with interactions are left. Then, all selected features are re-evaluated by the Lasso model to discard the redundant features. Finally, an online group streaming feature selection algorithm is designed. Experimental results compared with eight representative methods on thirteen datasets show that FNE-OGSFS can achieve better comprehensive performance.
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Abstract
AbstractRecently, numerous investors have shifted from active strategies to passive strategies because the passive strategy approach affords stable returns over the long term. Index tracking is a popular passive strategy. Over the preceding year, most researchers handled this problem via a two-step procedure. However, such a method is a suboptimal global-local optimization technique that frequently results in uncertainty and poor performance. This paper introduces a framework to address the comprehensive index tracking problem (IPT) with a joint approach based on metaheuristics. The purpose of this approach is to globally optimize this problem, where optimization is measured by the tracking error and excess return. Sparsity, weights, assets under management, transaction fees, the full share restriction, and investment risk diversification are considered in this problem. However, these restrictions increase the complexity of the problem and make it a nondeterministic polynomial-time-hard problem. Metaheuristics compose the principal process of the proposed framework, as they balance a desirable tradeoff between the computational resource utilization and the quality of the obtained solution. This framework enables the constructed model to fit future data and facilitates the application of various metaheuristics. Competitive results are achieved by the proposed metaheuristic-based framework in the presented simulation.
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