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Zhou X, Chen Y, Gui W, Heidari AA, Cai Z, Wang M, Chen H, Li C. Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis. Artif Intell Med 2024; 153:102886. [PMID: 38749310 DOI: 10.1016/j.artmed.2024.102886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 03/17/2024] [Accepted: 04/27/2024] [Indexed: 06/11/2024]
Abstract
Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.
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Affiliation(s)
- Xinsen Zhou
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Yi Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Wenyong Gui
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Mingjing Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Chengye Li
- Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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Ben N, Qiqi D, Hong W, Jing L. Simplified bacterial foraging optimization with quorum sensing for global optimization. INT J INTELL SYST 2021. [DOI: 10.1002/int.22396] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Niu Ben
- College of Management Shenzhen University Shenzhen China
| | - Duan Qiqi
- College of Management Shenzhen University Shenzhen China
| | - Wang Hong
- College of Management Shenzhen University Shenzhen China
- Department of Mechanical Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong
| | - Liu Jing
- School of Engineering and Information Technology, University of New South Wales Canberra Australian Capital Territory Australia
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Abstract
In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the interaction dynamics. Once the decoupler is designed properly, two PID controllers are tuned for two non-interacting loops by minimizing the integral-square-error (ISE). The ISEs for two loops are obtained in terms of alpha and beta parameters to simplify the simulation. Thus designed ISEs are minimized using SLSSO algorithm. In order to show the effectiveness of the proposed algorithm, the controller tuning is also accomplished using some state-of-the-art algorithms. Further, statistical analysis is presented to prove the effectiveness of SLSSO. In addition, the time domain specifications are presented for different test cases. The step responses are also shown for fixed and variable reference inputs for two loops. The quantitative and qualitative results presented show the effectiveness of SLSSO for the DROP system.
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Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:2630104. [PMID: 32565769 PMCID: PMC7273473 DOI: 10.1155/2020/2630104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/05/2020] [Accepted: 05/13/2020] [Indexed: 11/18/2022]
Abstract
Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.
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Eken A, Çolak B, Bal NB, Kuşman A, Kızılpınar SÇ, Akaslan DS, Baskak B. Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity. J Neural Eng 2019; 17:016012. [DOI: 10.1088/1741-2552/ab50b2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00295-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Cai Z, Gu J, Wen C, Zhao D, Huang C, Huang H, Tong C, Li J, Chen H. An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:2396952. [PMID: 30034509 PMCID: PMC6032994 DOI: 10.1155/2018/2396952] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 04/02/2018] [Accepted: 05/21/2018] [Indexed: 11/17/2022]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.
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Affiliation(s)
- Zhennao Cai
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jianhua Gu
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
| | - Chunyu Huang
- College of Computer Science and Technology, Changchun University of Science Technology, Changchun 130032, China
| | - Hui Huang
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Changfei Tong
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Jun Li
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Huiling Chen
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
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