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Bian X, Liu Y, Zhang R, Sun H, Liu P, Tan X. Rapid quantification of grapeseed oil multiple adulterations using near-infrared spectroscopy coupled with a novel double ensemble modeling method. Spectrochim Acta A Mol Biomol Spectrosc 2024; 311:124016. [PMID: 38354676 DOI: 10.1016/j.saa.2024.124016] [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: 09/21/2023] [Revised: 02/04/2024] [Accepted: 02/07/2024] [Indexed: 02/16/2024]
Abstract
As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality vegetable oils. A novel ensemble modeling method is proposed for quantitative analysis of grapeseed oil adulterations combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo (MC) sampling and whale optimization algorithm (WOA) to build numerous partial least squares (PLS) sub-models, named MC-WOA-PLS. A total of 80 adulterated grapeseed oil samples were prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil, and corn oil with the designed mass percentages. NIR spectra of the 80 samples were measured in a transmittance mode in the range of 12,000-4000 cm-1. Parameters in MC-WOA-PLS including the number of latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), variable importance in projection-PLS (VIP-PLS), and WOA-PLS, MC-WOA-PLS achieves the best prediction accuracy and stability for quantification of the five pure oils in adulterated grapeseed oil samples.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China; NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Shandong University, Jinan 250012, PR China.
| | - Yuxia Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Rongling Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Hao Sun
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
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Ji C, Zhang C, Suo L, Liu Q, Peng T. Swarm intelligence based deep learning model via improved whale optimization algorithm and Bi-directional long short-term memory for fault diagnosis of chemical processes. ISA Trans 2024; 147:227-238. [PMID: 38443273 DOI: 10.1016/j.isatra.2024.02.014] [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: 03/12/2023] [Revised: 02/19/2024] [Accepted: 02/19/2024] [Indexed: 03/07/2024]
Abstract
The chemical production process typically possesses complexity and high risks. Effective fault diagnosis is a key technology for ensuring the reliability and safety of chemical production processes. In this study, a comprehensive fault diagnosis method based on time-varying filtering empirical mode decomposition (TVF-EMD), kernel principal component analysis (KPCA), and an improved whale optimization algorithm (WOA) to optimize bi-directional long short-term memory (BiLSTM) is proposed. This research utilizes TVF-EMD and KPCA to analyze and preprocess the raw data, eliminating noise and and reducing the dimensions of the fault data. Subsequently, BiLSTM is employed for fault data classification. To address the hyperparameters within BiLSTM, the enhanced WOA is used for optimization. Finally, the efficacy and superiority of this approach are validated through two fault diagnosis examples.
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Affiliation(s)
- Chunlei Ji
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China
| | - Chu Zhang
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China; Jiangsu Permanent Magnet Motor Engineering Research Center, Huaiyin Institute of Technology, Huai'an 223003, China.
| | - Leiming Suo
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China
| | - Qianlong Liu
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China
| | - Tian Peng
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an 223003, China; Jiangsu Permanent Magnet Motor Engineering Research Center, Huaiyin Institute of Technology, Huai'an 223003, China
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Qiu W, Wang B, Hu X. Rolling bearing fault diagnosis based on RQA with STD and WOA-SVM. Heliyon 2024; 10:e26141. [PMID: 38420432 PMCID: PMC10900947 DOI: 10.1016/j.heliyon.2024.e26141] [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: 09/07/2023] [Revised: 02/08/2024] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and Whale Optimization Algorithm Support Vector Machine (WOA-SVM) is proposed. Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Deviation (STD) are extracted by recursive quantitative analysis method to generate feature vectors; after that, in order to construct the optimal support vector machine model, the Whale Optimization Algorithm is used to optimize the c and g parameters. Finally, both Recursive Quantitative Analysis and standard deviation are combined with the WOA-SVM model to perform fault diagnosis of rolling bearings. The rolling bearing datasets from Case Western Reserve University and Jiangnan University were used for example analysis, and the fault identification accuracy reached 100% and 95.00%, respectively. Compared to other methods, the method proposed in this paper has higher diagnostic accuracy and wide practical applicability, and the risk of accidents can be reduced through accurate fault diagnosis, which is also important for safety and environmental policies. This research originated in the field of mechanical fault diagnosis to solve the problem of fault diagnosis of rolling bearings in industrial production, it builds on previous research and explores new methods and techniques to fill some gaps in the field of mechanical fault diagnosis.
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Affiliation(s)
- Wentao Qiu
- Shanghai Maritime University, Shanghai 201306, China
| | - Bing Wang
- Shanghai Maritime University, Shanghai 201306, China
| | - Xiong Hu
- Shanghai Maritime University, Shanghai 201306, China
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Xie S, Lei L, Sun J, Xu J. [Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2024; 41:1-8. [PMID: 38403598 PMCID: PMC10894732 DOI: 10.7507/1001-5515.202303010] [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] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
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Affiliation(s)
- Songyun Xie
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China
| | - Lingjun Lei
- Medical Research Institute, Northwestern Polytechnical University, Xi'an 710129, P. R. China
| | - Jiang Sun
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China
| | - Jian Xu
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China
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Guo X, Hu J, Yu H, Wang M, Yang B. A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection. Comput Biol Med 2023; 166:107538. [PMID: 37857136 DOI: 10.1016/j.compbiomed.2023.107538] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/06/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary Whale Optimization Algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks.
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Affiliation(s)
- Xuanming Guo
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Jiao Hu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Helong Yu
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Mingjing Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China.
| | - Bo Yang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
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Wang H, Ma X, Zhao X, Wang W. Scheduling optimization of wind-thermal interconnected low-carbon power system integrated with hydrogen storage. Environ Sci Pollut Res Int 2023; 30:109354-109371. [PMID: 37924171 DOI: 10.1007/s11356-023-29977-6] [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] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 09/15/2023] [Indexed: 11/06/2023]
Abstract
To improve the consumption of wind energy and reduce carbon emission, this paper proposes a wind-thermal interconnected low-carbon power system integrated with hydrogen storage. An energy scheduling optimization model aiming at minimizing the daily operation cost of the system is constructed considering environmental operation cost quantification, and whale optimization algorithm is used to optimize multiple variables. Finally, in simulation example, various scenarios are set considering the application way of hydrogen and the scenarios with and without the carbon capture and storage (CCS) are optimized, respectively. The horizontal comparison results show that the system with hydrogen production (S2) and the system with hydrogen fuel cell (S3) have higher economic operation cost than that of wind-thermal interconnected power system only (S1), but the environmental cost is reduced; the system's daily operating costs are reduced. The wind curtailment rate decreases from 11.0% (S1) to 3.8% (S2 and S3) without CCS, and from 9.0% (S1) to 2.1% (S2 and S3) with CCS. The longitudinal comparison shows that the thermal power output is reduced and the wind power consumption is improved with CCS. The addition of CCS increases total operating costs but significantly reduces environmental costs. Configuring hydrogen storage system in the wind-thermal interconnected power system can effectively promote the consumption of wind energy and reduce the system operation cost; however, the utilization of CCS is economic unfriendly at present.
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Affiliation(s)
- Haifeng Wang
- Department of Economics and Management, North China Electric Power University, No. 689, Huadian Road, Baoding, 071003, China
| | - Xiaoran Ma
- Department of Economics and Management, North China Electric Power University, No. 689, Huadian Road, Baoding, 071003, China.
| | - Xingyu Zhao
- Department of System Consulting, Jiangsu Keneng Power Engineering Consulting Co., Ltd, Nanjing, China
| | - Weijun Wang
- Department of Economics and Management, North China Electric Power University, No. 689, Huadian Road, Baoding, 071003, China
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Cuevas E, Rodríguez A, Perez M, Murillo-Olmos J, Morales-Castañeda B, Alejo-Reyes A, Sarkar R. Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes. Appl Math Model 2023; 121:506-523. [PMID: 37234701 PMCID: PMC10199305 DOI: 10.1016/j.apm.2023.05.012] [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] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023]
Abstract
A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk.
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Affiliation(s)
- Erik Cuevas
- Electronics department, University of Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, Jal C.P 44430, Mexico
| | - Alma Rodríguez
- Electronics department, University of Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, Jal C.P 44430, Mexico
- Software Development, Industrial Technical Education Center, Colomos. Calle Nueva Escocia 1885, Providencia 5a Sección, Guadalajara, Jal C.P. 44638, Mexico
| | - Marco Perez
- Electronics department, University of Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, Jal C.P 44430, Mexico
| | - Jesús Murillo-Olmos
- Electronics department, University of Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, Jal C.P 44430, Mexico
| | - Bernardo Morales-Castañeda
- Electronics department, University of Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, Jal C.P 44430, Mexico
| | - Avelina Alejo-Reyes
- Faculty of Engineering, Panamerican University, Prolongación Calzada Circunvalación Poniente 49, Zapopan, Jalisco 45010, Mexico
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
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Nandhini K, Tamilpavai G. An Optimal Stacked ResNet-BiLSTM-Based Accurate Detection and Classification of Genetic Disorders. Neural Process Lett 2023:1-22. [PMID: 37359129 PMCID: PMC10196306 DOI: 10.1007/s11063-023-11195-3] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 06/28/2023]
Abstract
Gene is located inside the nuclease and the genetic data is contained in deoxyribonucleic acid (DNA). A person's gene count ranges from 20,000 to 30,000. Even a minor alteration to the DNA sequence can be harmful if it affects the cell's fundamental functions. As a result, the gene begins to act abnormally. The sorts of genetic abnormalities brought on by mutation include chromosomal disorders, complex disorders, and single-gene disorders. Therefore, a detailed diagnosis method is required. Thus, we proposed an Elephant Herd Optimization-Whale Optimization Algorithm (EHO-WOA) optimized Stacked ResNet-Bidirectional Long Term Short Memory (ResNet-BiLSTM) model for detecting genetic disorders. Here, a hybrid EHO-WOA algorithm is presented to assess the Stacked ResNet-BiLSTM architecture's fitness. The ResNet-BiLSTM design uses the genotype and gene expression phenotype as input data. Furthermore, the proposed method identifies rare genetic disorders such as Angelman Syndrome, Rett Syndrome, and Prader-Willi Syndrome. It demonstrates the effectiveness of the developed model with greater accuracy, recall, specificity, precision, and f1-score. Thus, a wide range of DNA deficiencies including Prader-Willi syndrome, Marfan syndrome, Early Onset Morbid Obesity, Rett syndrome, and Angelman syndrome are predicted accurately.
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Affiliation(s)
- K. Nandhini
- Department of Computer Science and Engineering, Anna University, Chennai, India
| | - G. Tamilpavai
- Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, India
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Zhang M, Wu Q, Chen H, Heidari AA, Cai Z, Li J, Md Abdelrahim E, Mansour RF. Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction. Biomed Signal Process Control 2023; 83:104638. [PMID: 36741073 PMCID: PMC9889265 DOI: 10.1016/j.bspc.2023.104638] [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: 08/01/2022] [Revised: 12/01/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.
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Affiliation(s)
- Meilin Zhang
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Qianxi Wu
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Ali Asghar Heidari
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Jiaren Li
- Wenzhou People's Hospital, Wenzhou, Zhejiang 325099, China
| | - Elsaid Md Abdelrahim
- Faculty of Science, Northern Border University, Arar, Saudi Arabia.,Faculty of Science, Tanta University, Tanta, Egypt
| | - Romany F Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
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沈 胤, 张 畅, 杨 林, 李 元, 郑 秀. [Research on eye movement data classification using support vector machine with improved whale optimization algorithm]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:335-342. [PMID: 37139766 PMCID: PMC10162935 DOI: 10.7507/1001-5515.202204066] [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] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 02/28/2023] [Indexed: 05/05/2023]
Abstract
When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
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Affiliation(s)
- 胤宏 沈
- 四川大学 电气工程学院 (成都 610065)College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - 畅 张
- 四川大学 电气工程学院 (成都 610065)College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - 林 杨
- 四川大学 电气工程学院 (成都 610065)College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - 元媛 李
- 四川大学 电气工程学院 (成都 610065)College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - 秀娟 郑
- 四川大学 电气工程学院 (成都 610065)College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China
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Kottath R, Singh P, Bhowmick A. Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting. Soft comput 2023; 27:1-32. [PMID: 37362291 PMCID: PMC10008129 DOI: 10.1007/s00500-023-07928-0] [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] [Accepted: 02/11/2023] [Indexed: 03/13/2023]
Abstract
In this work, we intend to propose multiple hybrid algorithms with the idea of giving a choice to the particles of a swarm to update their position for the next generation. To implement this concept, Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Whale Optimization Algorithm (WOA) have been utilized. Exhaustive possible combinations of these algorithms are developed and benchmarked against the base algorithms. These hybrid algorithms have been validated on twenty-four well-known unimodal and multimodal benchmarks functions, and detailed analysis with varying dimensions and population size is discussed for the same. Further, the efficacy of these algorithms has been tested on short-term electricity load and price forecasting applications. For this purpose, the algorithms have been combined with Artificial Neural Networks (ANNs) to evaluate their performance on the ISO New Pool England dataset. The results demonstrate that hybrid optimization algorithms perform superior to their base algorithms in most test cases. Furthermore, the results show that the performance of CSA-GWO is significantly better than other algorithms.
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Affiliation(s)
- Rahul Kottath
- Digital Tower, Bentley Systems India Private Limited, Pune, India
- School of Electrical and Electronics Engineering, VIT Bhopal University, Bhopal, MP 466114 India
| | - Priyanka Singh
- Department of Computer Science and Engineering, SRM University-AP, Amaravati, AP 522502 India
| | - Anirban Bhowmick
- School of Electrical and Electronics Engineering, VIT Bhopal University, Bhopal, MP 466114 India
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12
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Jiao S, Shi J, Wang Y, Wang R. A novel image noise reduction method for composite multistable stochastic resonance systems. Heliyon 2023; 9:e14431. [PMID: 36950586 PMCID: PMC10025155 DOI: 10.1016/j.heliyon.2023.e14431] [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: 12/14/2022] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance of stochastic resonance theory proves that noise can be used to enhance signal, which brings new inspiration to image processing. The classical bistable stochastic resonance model has the problems of high potential barrier and easy saturation, which is not conducive to the improvement of image denoising effect. In this paper, a novel type of stochastic resonance potential well model is quoted, which solves the above shortcomings of the bistable stochastic resonance model, and then combines it with the Gaussian model to propose a composite multistable stochastic resonance model. The dynamic principle of the model in signal detection is described, and the influence of system parameters on image noise reduction is analyzed. The whale optimization algorithm is used to optimize the model parameters, and an adaptive compound multistable stochastic resonance system is established to process pictures and measured radar images under different noise backgrounds. The simulation experiment and engineering application show that the model proposed in this paper solves the problem of high potential barrier and easy saturation of the bistable model, and has better image noise reduction ability compared with Wiener filter, median filter, classical bistable stochastic resonance system and novel type of stochastic resonance potential well system.
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Affiliation(s)
- Shangbin Jiao
- School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, 710048, China
- Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an, 710048, China
- Corresponding author. School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, 710048, China.
| | - Jiaqiang Shi
- School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, 710048, China
| | - Yi Wang
- School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, 710048, China
- Department of Aeronautical Engineering, Shaanxi Polytechnic Institute, Xianyang, 712000, China
| | - Ruijie Wang
- School of Mathematics and Statistics, Ankang University, Ankang, 725000, China
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13
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Wang H, Zhao X, Wang W. Fault diagnosis and prediction of wind turbine gearbox based on a new hybrid model. Environ Sci Pollut Res Int 2023; 30:24506-24520. [PMID: 36344885 DOI: 10.1007/s11356-022-23893-x] [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] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
Gearbox is an important part of wind turbine. Diagnosing and forecasting gearbox faults of wind turbines can effectively reduce the costs of operation and maintenance and improve the reliability of gearbox operation. Due to high dimensionality and nonlinearity of system parameters, the paper uses the grey relation analysis to select features related to gearbox oil temperature. Features with a relational degree above 0.7 are selected as input data related to oil temperature, including wind speed, ambient temperature, power, and gearbox shaft temperature. Then, a new extreme learning machine with kernel improved by the whale optimization algorithm is established to forecast gearbox oil temperature. Through the residuals between gearbox oil temperature predicted by the proposed model and monitored by the SCADA, whether the gearbox exists faults can be diagnosed. In the case study, the test data was divided into two groups (the test data with and without faults). In the data test without faults, compared with three other models, the proposed model has the smallest false-negative rate (0.211%) and mean absolute percentage error (2.812%). In the data test with faults, the proposed model can diagnose gearbox faults earlier (160 min in advance) than the other three benchmark models. The results show that the proposed hybrid model performs well in the fault diagnosis and prediction of wind turbine gearbox.
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Affiliation(s)
- Haifeng Wang
- Department of Economics and Management, North China Electric Power University, No. 689 Huadian Road, Baoding, 071003, China
| | - Xingyu Zhao
- Department of Economics and Management, North China Electric Power University, No. 689 Huadian Road, Baoding, 071003, China.
| | - Weijun Wang
- Department of Economics and Management, North China Electric Power University, No. 689 Huadian Road, Baoding, 071003, China
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14
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Bian X, Zhang R, Liu P, Xiang Y, Wang S, Tan X. Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil. Spectrochim Acta A Mol Biomol Spectrosc 2023; 284:121788. [PMID: 36058170 DOI: 10.1016/j.saa.2022.121788] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Sichuan 644000, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China.
| | - Rongling Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Yang Xiang
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
| | - Shuyu Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
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15
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Liu L, Kuang F, Li L, Xu S, Liang Y. An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer. Comput Biol Med 2022; 151:106227. [PMID: 36368112 DOI: 10.1016/j.compbiomed.2022.106227] [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: 09/12/2022] [Revised: 10/06/2022] [Accepted: 10/16/2022] [Indexed: 12/27/2022]
Abstract
Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.
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Affiliation(s)
- Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fangjun Kuang
- School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Lingzhi Li
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, 315020, China.
| | - Suling Xu
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, 315020, China.
| | - Yingqi Liang
- Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
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16
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Ji C, Zhang C, Hua L, Ma H, Nazir MS, Peng T. A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction. Environ Res 2022; 215:114228. [PMID: 36084674 DOI: 10.1016/j.envres.2022.114228] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/15/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities.
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Affiliation(s)
- Chunlei Ji
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China.
| | - Chu Zhang
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China; Jiangsu Permanent Magnet Motor Engineering Research Center, Huaiyin Institute of Technology, Huai'an, 223003, China.
| | - Lei Hua
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China
| | - Huixin Ma
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China
| | | | - Tian Peng
- Faculty of Automation, Huaiyin Institute of Technology, Huai'an, 223003, China; Jiangsu Permanent Magnet Motor Engineering Research Center, Huaiyin Institute of Technology, Huai'an, 223003, China
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17
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Hu J, Lv S, Zhou T, Chen H, Xiao L, Huang X, Wang L, Wu P. Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators. J Bionic Eng 2022; 20:762-781. [PMID: 36466726 PMCID: PMC9703443 DOI: 10.1007/s42235-022-00292-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
Pulmonary Hypertension (PH) is a global health problem that affects about 1% of the global population. Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease. The present study proposes a Kernel Extreme Learning Machine (KELM) model based on an improved Whale Optimization Algorithm (WOA) for predicting PH mouse models. The experimental results showed that the selected blood indicators, including Haemoglobin (HGB), Hematocrit (HCT), Mean, Platelet Volume (MPV), Platelet distribution width (PDW), and Platelet-Large Cell Ratio (P-LCR), were essential for identifying PH mouse models using the feature selection method proposed in this paper. Remarkably, the method achieved 100.0% accuracy and 100.0% specificity in classification, demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.
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Affiliation(s)
- Jiao Hu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 People’s Republic of China
| | - Shushu Lv
- Department of Dermatology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730 People’s Republic of China
| | - Tao Zhou
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 People’s Republic of China
| | - Lei Xiao
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 People’s Republic of China
| | - Xiaoying Huang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
| | - Liangxing Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
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18
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Xing J, Zhao H, Chen H, Deng R, Xiao L. Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation. J Bionic Eng 2022; 20:797-818. [PMID: 36466725 PMCID: PMC9707266 DOI: 10.1007/s42235-022-00297-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s42235-022-00297-8.
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Affiliation(s)
- Jie Xing
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Hanli Zhao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Ruoxi Deng
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
| | - Lei Xiao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China
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19
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Wang G, Guo S, Han L, Song X, Zhao Y. Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion. Comput Biol Med 2022; 150:106181. [PMID: 36240596 PMCID: PMC9533636 DOI: 10.1016/j.compbiomed.2022.106181] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.
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Affiliation(s)
- Guowei Wang
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Shuli Guo
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.
| | - Lina Han
- Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
| | - Xiaowei Song
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Yuanyuan Zhao
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
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20
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Reddy K, Saha AK. A modified Whale Optimization Algorithm for exploitation capability and stability enhancement. Heliyon 2022; 8:e11027. [PMID: 36276751 DOI: 10.1016/j.heliyon.2022.e11027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/17/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022] Open
Abstract
Swarm-based Metaheuristic Optimization Techniques (MOT) are the dominant among all techniques, particularly owing to their simple nature and robust performance. The Whale Optimization Algorithm (WOA), a swarm-based MOT inspired by the hunting strategy of the humpback whale, has thus far shown promising results. However, like all MOT, the WOA is not without drawbacks. These demerits are a slow convergence rate and poor exploitation capability. This may prove to be problematic when applied to optimization problems requiring high precision results. Over the past few years, there has been proposed modifications to the conventional algorithm. However, experimental analysis highlights the need to further enhance the properties of the algorithm. This work proposes an enhanced WOA for exploitation capability and stability enhancement. The proposed algorithm introduces various modifications to the position update equations of the conventional algorithm, as well as a modified algorithm structure. The proposed algorithm was compared to various state-of-the-art MOT, as well as modified WOA proposed in recent literature. When applied to the CEC 2019 benchmark functions, the proposed algorithm produced the best result in 7 of the 10 test and had the most superior overall placement. When applied to practical problems, the algorithm once again demonstrated superiority. In addition, it was observed that the proposed algorithm exhibited a superior convergence rate to the other compared techniques.
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21
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Guo Z, Zhang L, Chen Q, Han M, Liu W. Monophenolase assay using excitation-emission matrix fluorescence and ELMAN neural network assisted by whale optimization algorithm. Anal Biochem 2022; 655:114838. [PMID: 35961401 DOI: 10.1016/j.ab.2022.114838] [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: 06/22/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 11/01/2022]
Abstract
Tyrosinase plays a vital role for melanogenesis and inherently involves both monophenolase activity and diphenolase activity. Monophenolase catalyzes hydroxylation of tyrosine to l-DOPA (L-3,4-dihydroxyphenylalanine). Real-time monophenolase assay method is of outstanding interest for both scientific research and industrial application. A combined strategy of three-dimensional excitation-emission matrix (EEM) fluorescence spectra and artificial neural network was developed to determine monophenolase activity. A quantitation system for tyrosine in presence of l-DOPA was designed based on ELMAN neural network. Principal component analysis (PCA) was conducted to reduce the dimensionality of fluorescence spectra. Four principal components was used as input variables. Whale optimization algorithm (WOA) was implemented to optimize the initial weights and threshold network. Real-time concentration of tyrosine in monophenolase reaction was monitored to calculate the initial velocity for tyrosine consumption. The exclusive monophenolase activity without interference from diphenolase reaction was determined. Limit of detection (LOD) for monophenolase assay is 0.0113 U mL-1. Using the proposed method, enzyme kinetics for monophenolase was investigate. Km was calculated as 14.16 μM. Inhibitor for monophenolase was screened by using model molecule kojic acid with IC50 of 3.49 μM. The assay method exhibited a promising prospect to characterize the kinetics and inhibitor of monophenolase.
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Affiliation(s)
- Zhenyu Guo
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Ling Zhang
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Qinfei Chen
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Mengqi Han
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Wenbin Liu
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China.
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22
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Arya Azar N, Kayhomayoon Z, Ghordoyee Milan S, Zarif Sanayei H, Berndtsson R, Nematollahi Z. A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources. Environ Sci Pollut Res Int 2022; 29:56828-56844. [PMID: 35347629 DOI: 10.1007/s11356-022-19762-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/13/2022] [Indexed: 06/14/2023]
Abstract
Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater. The surface and groundwater system was simulated by MODFLOW to reflect groundwater transport and aquifer conditions. The important Marvdasht aquifer in the south of Iran was used as an experimental study area to test the methodology. In this context, we developed an optimal conjunctive exploitation model for dry and wet years using two new evolutionary algorithms, i.e., whale optimization algorithm (WOA) and firefly algorithm (FA). These were used in combination with the group method of data handling (GMDH) and least squares support vector machine (LS-SVM) to estimate sustainable groundwater withdrawal. The results show that the FA is more efficient in calculating optimal conjunctive water supply so that about 61% of water needs were met in the worst scenario for surface water resources, while it was 52% using the WOA. By applying the optimal conjunctive model during the simulation period, the groundwater level increased by about 0.4 and 0.55 m using the WOA and FA, respectively. The results of Taylor's diagram, box plot diagram, and rock diagram with error evaluation criteria, i.e., root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), showed that the GMDH (RMSE = 6.04 MCM, MAE = 3.89 MCM, and NSE = 0.99) was slightly better than LS-SVM (RMSE = 6.36 MCM, MAE = 4.50 MCM, and NSE = 0.98) to estimate optimal groundwater use. The results show that machine learning models are cost- and time-effective solutions to estimate optimal exploitation of groundwater resources in complex combined surface and groundwater supply problems. The methodology can be used to better estimate sustainable exploitation of groundwater resources by water resources managers.
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Affiliation(s)
- Naser Arya Azar
- Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | | | - Sami Ghordoyee Milan
- Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran.
| | - HamedReza Zarif Sanayei
- Civil Engineering, Faculty of Technology and Engineering, Shahre Kord University, Shahre Kord, Iran
| | - Ronny Berndtsson
- Division of Water Resources Engineering & Centre for Advanced Middle Eastern Studies, Lund University, Lund, Sweden
| | - Zahra Nematollahi
- Civil Engineering, Faculty of Technology and Engineering, Shahre Kord University, Shahre Kord, Iran
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23
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Abdel-Basset M, Mohamed R, Abouhawwash M. A new fusion of whale optimizer algorithm with Kapur's entropy for multi-threshold image segmentation: analysis and validations. Artif Intell Rev 2022; 55:6389-6459. [PMID: 35342218 PMCID: PMC8935268 DOI: 10.1007/s10462-022-10157-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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] [Indexed: 12/02/2022]
Abstract
The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshold are computationally expensive, tedious, and may be inaccurate. Hence, this paper proposes an Improved Whale Optimization Algorithm (IWOA) based on Kapur’s entropy for solving multi-threshold segmentation of the gray level image. Also, IWOA supports its performance using linearly convergence increasing and local minima avoidance technique (LCMA), and ranking-based updating method (RUM). LCMA technique accelerates the convergence speed of the solutions toward the optimal solution and tries to avoid the local minima problem that may fall within the optimization process. To do that, it updates randomly the positions of the worst solutions to be near to the best solution and at the same time randomly within the search space according to a certain probability to avoid stuck into local minima. Because of the randomization process used in LCMA for updating the solutions toward the best solutions, a huge number of the solutions around the best are skipped. Therefore, the RUM is used to replace the unbeneficial solution with a novel updating scheme to cover this problem. We compare IWOA with another seven algorithms using a set of well-known test images. We use several performance measures, such as fitness values, Peak Signal to Noise Ratio, Structured Similarity Index Metric, Standard Deviation, and CPU time.
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Affiliation(s)
- Mohamed Abdel-Basset
- Zagazig Univesitry, Shaibet an Nakareyah, Zagazig 2, Zagazig, 44519 Ash Sharqia Governorate Egypt
| | - Reda Mohamed
- Zagazig Univesitry, Shaibet an Nakareyah, Zagazig 2, Zagazig, 44519 Ash Sharqia Governorate Egypt
| | - Mohamed Abouhawwash
- Department of Mathematics Faculty of Science, Mansoura University, Mansoura, 35516 Egypt.,Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI 48824 USA
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24
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Wang X, Gong C, Khishe M, Mohammadi M, Rashid TA. Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer. Wirel Pers Commun 2021; 124:1355-1374. [PMID: 34873379 PMCID: PMC8635480 DOI: 10.1007/s11277-021-09410-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/14/2021] [Indexed: 06/12/2023]
Abstract
The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT's procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.
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Affiliation(s)
- Xusheng Wang
- Xi’an University of Technology, Xi’an, 710048 Shaanxi China
| | - Cunqi Gong
- Department of Clinical Laboratory, Jining No.1 People’s Hospital, Jining, 272011 Shandong China
| | - Mohammad Khishe
- Department of Electronic Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region Iraq
| | - Tarik A. Rashid
- Computer Science and Engineering Department, Science and Engineering School, University of Kurdistan Hewler, Erbil, KRG Iraq
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Chakraborty S, Saha AK, Nama S, Debnath S. COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction. Comput Biol Med 2021; 139:104984. [PMID: 34739972 PMCID: PMC8556692 DOI: 10.1016/j.compbiomed.2021.104984] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [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: 08/09/2021] [Revised: 10/09/2021] [Accepted: 10/23/2021] [Indexed: 12/11/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.
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Affiliation(s)
- Sanjoy Chakraborty
- Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura, India.
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology, Agartala, Tripura, India.
| | - Sukanta Nama
- Department of Applied Mathematics, Maharaja Bir Bikram University, Agartala, Tripura, India.
| | - Sudhan Debnath
- Department of Chemistry, Maharaja Bir Bikram College, Agartala, Tripura, India.
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Murugan R, Goel T, Mirjalili S, Chakrabartty DK. WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images. Biocybern Biomed Eng 2021; 41:1702-1718. [PMID: 34720309 PMCID: PMC8536521 DOI: 10.1016/j.bbe.2021.10.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 05/14/2021] [Revised: 10/02/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022]
Abstract
Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.
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Affiliation(s)
- R Murugan
- Bio-Medical Imaging Laboratory(BIOMIL), Department of Electronics and communication Engineering, National Institute Of Technology Silchar, Assam 788010, India
| | - Tripti Goel
- Bio-Medical Imaging Laboratory(BIOMIL), Department of Electronics and communication Engineering, National Institute Of Technology Silchar, Assam 788010, India
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
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Rafigh P, Akbari AA, Bidhandi HM, Kashan AH. Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic. Environ Sci Pollut Res Int 2021:10.1007/s11356-021-16077-6. [PMID: 34519990 PMCID: PMC8438288 DOI: 10.1007/s11356-021-16077-6] [Citation(s) in RCA: 3] [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] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.
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Affiliation(s)
- Parisa Rafigh
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Ali Akbar Akbari
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Hadi Mohammadi Bidhandi
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
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Naderipour A, Abdul-Malek Z, Davoodkhani IF, Kamyab H, Ali RR. Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller. Environ Sci Pollut Res Int 2021; 30:10.1007/s11356-021-14799-1. [PMID: 34241794 DOI: 10.1007/s11356-021-14799-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/04/2021] [Indexed: 06/13/2023]
Abstract
Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.
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Affiliation(s)
- Amirreza Naderipour
- Institute of High Voltage & High Current, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.
| | - Zulkurnain Abdul-Malek
- Institute of High Voltage & High Current, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
| | - Iraj Faraji Davoodkhani
- Department of Electrical Engineering, Islamic Azad University, Khalkhal Branch, Khalkhal, Iran
| | - Hesam Kamyab
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia
| | - Roshafima Rasit Ali
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia
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29
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Abbas S, Jalil Z, Javed AR, Batool I, Khan MZ, Noorwali A, Gadekallu TR, Akbar A. BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm. PeerJ Comput Sci 2021; 7:e390. [PMID: 33817036 PMCID: PMC7959601 DOI: 10.7717/peerj-cs.390] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [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: 12/16/2020] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.
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Affiliation(s)
- Shafaq Abbas
- Department of Computer Science, Air University, Islamabad, Pakistan
| | - Zunera Jalil
- Department of Cyber Security, Air University, Islamabad, Pakistan
| | | | - Iqra Batool
- Department of Computer Science, Air University, Islamabad, Pakistan
| | - Mohammad Zubair Khan
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
| | | | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology University, Tamil Nadu, India
| | - Aqsa Akbar
- Department of Computer Science, Air University, Islamabad, Pakistan
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30
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Kumar R, Singh R, Ashfaq H, Singh SK, Badoni M. Power system stability enhancement by damping and control of Sub-synchronous torsional oscillations using Whale optimization algorithm based Type-2 wind turbines. ISA Trans 2021; 108:240-256. [PMID: 32888728 DOI: 10.1016/j.isatra.2020.08.037] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 08/08/2020] [Accepted: 08/24/2020] [Indexed: 06/11/2023]
Abstract
This paper is aimed to demonstrate the merits of a metaheuristic swarm-based optimization technique, WOA (Whale optimization algorithm), in alleviating the low-frequency torsional oscillations called SSR (Sub-synchronous resonance). The demonstration has been performed using the modified IEEE FBM (IEEE first benchmark model) aggregated with Type-2 WPP (Wind power plant). The Plant is further interlinked to the grid with series compensated lines. Use of WOA for the optimal tuning of the controller suggested in the literature to control one of the degrees of freedom, i.e., Pitch angle and external resistance connected to the rotor, has been demonstrated. The effectiveness of the proposed WOA based controller has been examined using a time-domain approach based on the dynamic response of the different segments of the test system using the Matlab software for the three different cases viz., with Type-2 WPP only, Type-2 WPP with the controller suggested in the literature and with the proposed WOA based controller. The eigenvalues, together with simulation results, reveal the potential of the proposed WOA based controller in damping the low-frequency torsional oscillations using Type-2 wind turbines.
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Affiliation(s)
- Rajeev Kumar
- Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India; Electrical and Electronics Engineering Department, Krishna Institute of Engineering and Technology, Ghaziabad, India.
| | - Rajveer Singh
- Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India
| | - Haroon Ashfaq
- Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India
| | - Sudhir Kumar Singh
- Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India; Electrical and Electronics Engineering Department, Krishna Institute of Engineering and Technology, Ghaziabad, India
| | - Manoj Badoni
- Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering & Technology, Patiala, India
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31
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Abd Elaziz M, Nabil N, Moghdani R, Ewees AA, Cuevas E, Lu S. Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm. Multimed Tools Appl 2021; 80:12435-12468. [PMID: 33456315 PMCID: PMC7797715 DOI: 10.1007/s11042-020-10313-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 08/26/2020] [Accepted: 12/22/2020] [Indexed: 05/31/2023]
Abstract
Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
| | - Neggaz Nabil
- Faculté des mathématiques et informatique - Département d’Informatique- Laboratoire SIMPA, Université des Sciences et de la Technologie d’Oran Mohammed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000 Oran, Algeria
| | - Reza Moghdani
- Industrial Management Department, Persian Gulf University, Boushehr, Iran
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta, Egypt
| | - Erik Cuevas
- Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
| | - Songfeng Lu
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074 China
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Sun W, Huang C. A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction. Environ Pollut 2020; 266:115216. [PMID: 32763723 DOI: 10.1016/j.envpol.2020.115216] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/29/2020] [Accepted: 07/07/2020] [Indexed: 06/11/2023]
Abstract
Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO2 and SO2. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R2 and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R2 and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R2 can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored.
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Affiliation(s)
- Wei Sun
- Department of Economic Management, North China Electric Power University, Baoding 071000, PR China
| | - Chenchen Huang
- Department of Economic Management, North China Electric Power University, Baoding 071000, PR China.
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33
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Abdel-Basset M, Chang V, Mohamed R. HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Appl Soft Comput 2020; 95:106642. [PMID: 32843887 PMCID: PMC7439973 DOI: 10.1016/j.asoc.2020.106642] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [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: 07/20/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 01/28/2023]
Abstract
Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 - sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics.
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Affiliation(s)
| | - Victor Chang
- School of Computing, Engineering and Digital Technologies, Teesside University, UK
| | - Reda Mohamed
- Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt
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Memarzadeh R, Ghayoumi Zadeh H, Dehghani M, Riahi-Madvar H, Seifi A, Mortazavi SM. A novel equation for longitudinal dispersion coefficient prediction based on the hybrid of SSMD and whale optimization algorithm. Sci Total Environ 2020; 716:137007. [PMID: 32036132 DOI: 10.1016/j.scitotenv.2020.137007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/21/2020] [Accepted: 01/28/2020] [Indexed: 06/10/2023]
Abstract
The purpose of the present paper is improving the accuracy of existing formulas for the longitudinal dispersion coefficient (LDC) prediction based on a novel and simple meta-heuristic optimization method called Whale Optimization Algorithm (WOA). Although several existing formulas calculate LDC in the rivers based on the hydraulic and hydrodynamic specifications, most of them have significant errors in confronting extensive field data. In this study, comprehensive field data, including the geometrical and hydraulic properties of different rivers in the world, were adopted to build a reliable model. Statistical error measures were used to evaluate and compare the results with other studies. Furthermore, the Subset Selection of Maximum Dissimilarity (SSMD) method was utilized for a reputable selection of data for training and testing the WOA model. Subset selection is a critical factor in artificial intelligence (AI) computations. Finally, an integrated model based on the SSMD method and WOA technique has been proposed to develop the high accuracy formulas for the prediction of LDC. According to the results, the developed formulas are competitive or superior to the previous formulas for LDC estimation. Results also indicated that the WOA algorithm could be applied to improve the performance of the predictive equations in other fields of studies by finding the optimum values of coefficients.
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Affiliation(s)
- Rasoul Memarzadeh
- Department of Civil Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rasanjan, Iran
| | - Hossein Ghayoumi Zadeh
- Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rasanjan, Iran
| | - Majid Dehghani
- Department of Civil Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rasanjan, Iran.
| | - Hossien Riahi-Madvar
- Department of Water Engineering, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rasanjan, Iran
| | - Akram Seifi
- Department of Water Engineering, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rasanjan, Iran
| | - Seyed Mostafa Mortazavi
- Department of Civil Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rasanjan, Iran
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35
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Muthulakshmi M, Kavitha G. An integrated multi-objective whale optimized support vector machine and local texture feature model for severity prediction in subjects with cardiovascular disorder. Int J Comput Assist Radiol Surg 2020; 15:601-615. [PMID: 32152831 DOI: 10.1007/s11548-020-02133-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 10/24/2019] [Accepted: 02/27/2020] [Indexed: 11/25/2022]
Abstract
PURPOSE The left ventricle (LV) myocardium undergoes deterioration with the reduction in ejection fraction (EF). The analysis of its texture pattern plays a major role in diagnosis of heart muscle disease severity. Hence, a classification framework with co-occurrence of local ternary pattern feature (COALTP) and whale optimization algorithm has been attempted to improve the prediction accuracy of disease severity level. METHODS This analysis is carried out on 600 slices of 76 participants from Kaggle challenge that include subjects with normal and reduced EF. The myocardium of LV is segmented using optimized edge-based local Gaussian distribution energy (LGE)-based level set, and end-diastolic and end-systolic volumes were calculated. COALTP is extracted for two distance levels (d = 1 and 2). The t-test has been performed between the features of individual binary classes. The features are ranked using feature ranking methods. The experiments have been performed to analyze the performance of various percentages of features in each combination of bin for fivefold cross-validation. An integrated whale optimized feature selection and multi-classification framework is developed to classify the normal and pathological subjects using CMR images, and DeLong test has been performed to compare the ROCs. RESULTS The optimized edge embedded to level set has produced better segmented myocardium that correlates with R = 0.98 with gold standard volume. The t-test shows that texture features extracted from severe subjects with distance level "1" are more statistically significant with a p value (< 0.00004) compared to other pathologies. This approach has produced an overall multi-class accuracy of 75% [confidence interval (CI) 63.74-84.23%] and effective subclass specificity of 70% (CI 55.90-81.22%). CONCLUSION The obtained results show that the multi-objective whale optimized multi-class support vector machine framework can effectively discriminate the healthy and patients with reduced ejection fraction and potentially support the treatment process.
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Affiliation(s)
- M Muthulakshmi
- Department of Electronics Engineering, MIT Campus, Anna University, Chromepet, Chennai, Tamilnadu, 600044, India.
| | - G Kavitha
- Department of Electronics Engineering, MIT Campus, Anna University, Chromepet, Chennai, Tamilnadu, 600044, India
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36
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Zhang L, Gao HJ, Zhang J, Badami B. Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer. Open Med (Wars) 2020; 15:27-37. [PMID: 32099900 PMCID: PMC7026744 DOI: 10.1515/med-2020-0006] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [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: 02/27/2019] [Accepted: 10/03/2019] [Indexed: 12/20/2022] Open
Abstract
Convolutional neural networks (CNNs) are a branch of deep learning which have been turned into one of the popular methods in different applications, especially medical imaging. One of the significant applications in this category is to help specialists make an early detection of skin cancer in dermoscopy and can reduce mortality rate. However, there are a lot of reasons that affect system diagnosis accuracy. In recent years, the utilization of computer-aided technology for this purpose has been turned into an interesting category for scientists. In this research, a meta-heuristic optimized CNN classifier is applied for pre-trained network models for visual datasets with the purpose of classifying skin cancer images. However there are different methods about optimizing the learning step of neural networks, and there are few studies about the deep learning based neural networks and their applications. In the present work, a new approach based on whale optimization algorithm is utilized for optimizing the weight and biases in the CNN models. The new method is then compared with 10 popular classifiers on two skin cancer datasets including DermIS Digital Database Dermquest Database. Experimental results show that the use of this optimized method performs with better accuracy than other classification methods.
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Affiliation(s)
- Long Zhang
- Department of medical equipment, People’s hospital of Zhengzhou University, Zhengzhou, 450001, China
| | - Hong Jie Gao
- Department of medical equipment, People’s hospital of Zhengzhou University, Zhengzhou, 450001, China
| | - Jianhua Zhang
- Institute of Medical Engineering Technology and Data Mining, Zhengzhou University, Zhengzhou, 450001, China
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37
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Zhang N, Cai YX, Wang YY, Tian YT, Wang XL, Badami B. Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med 2019; 102:101756. [PMID: 31980095 DOI: 10.1016/j.artmed.2019.101756] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [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: 03/04/2019] [Revised: 10/01/2019] [Accepted: 11/05/2019] [Indexed: 12/11/2022]
Abstract
Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.
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Affiliation(s)
- Ni Zhang
- Department of Throracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei 430030, China
| | - Yi-Xin Cai
- Department of Throracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei 430030, China
| | - Yong-Yong Wang
- Department of Throracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei 430030, China
| | - Yi-Tao Tian
- Department of Throracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, Hubei 430030, China
| | - Xiao-Li Wang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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38
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Zhao D, Liu H, Zheng Y, He Y, Lu D, Lyu C. Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis. J Biomed Inform 2019; 92:103124. [PMID: 30796977 DOI: 10.1016/j.jbi.2019.103124] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [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: 10/17/2018] [Revised: 01/15/2019] [Accepted: 02/04/2019] [Indexed: 12/17/2022]
Abstract
Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.
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Affiliation(s)
- Dandan Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China.
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China
| | - Yanlin He
- School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China
| | - Dianjie Lu
- School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China
| | - Chen Lyu
- School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China
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