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Jia W, Chen S, Yang L, Liu G, Li C, Cheng Z, Wang G, Yang X. Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer. Comput Biol Med 2024; 175:108440. [PMID: 38701589 DOI: 10.1016/j.compbiomed.2024.108440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 03/20/2024] [Accepted: 04/07/2024] [Indexed: 05/05/2024]
Abstract
The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics-achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.
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Affiliation(s)
- Wenyuan Jia
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China; Scientific and Technological Innovation Center of Health Products and Medical Materials with Characteristic Resources of Jilin Province, China.
| | - Shu Chen
- Department of Thoracic Surgery, The Second Hospital of Jilin University, Changchun, 130041, China.
| | - Lili Yang
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China.
| | - Guomin Liu
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China; Scientific and Technological Innovation Center of Health Products and Medical Materials with Characteristic Resources of Jilin Province, China.
| | - Chiyu Li
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China.
| | - Zhiqiang Cheng
- Scientific and Technological Innovation Center of Health Products and Medical Materials with Characteristic Resources of Jilin Province, China; College of Resources and Environment, Jilin Agriculture University, Changchun, 130118, China.
| | - Guoqing Wang
- Zhejiang Suosi Technology Co. Ltd, Wenzhou, 325000, Zhejiang, China.
| | - Xiaoyu Yang
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China.
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2
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Kang N, Wang M, Pang C, Lan R, Li B, Guan J, Wang H. Cross-patch feature interactive net with edge refinement for retinal vessel segmentation. Comput Biol Med 2024; 174:108443. [PMID: 38608328 DOI: 10.1016/j.compbiomed.2024.108443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/14/2024] [Accepted: 04/07/2024] [Indexed: 04/14/2024]
Abstract
Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting clinical doctors in diagnosing retinal diseases. However, existing methods often produce mis-segmentation when dealing with low contrast images and thin blood vessels, which affects the continuity and integrity of the vessel skeleton. In addition, existing deep learning methods tend to lose a lot of detailed information during training, which affects the accuracy of segmentation. To address these issues, we propose a novel dual-decoder based Cross-patch Feature Interactive Net with Edge Refinement (CFI-Net) for end-to-end retinal vessel segmentation. In the encoder part, a joint refinement down-sampling method (JRDM) is proposed to compress feature information in the process of reducing image size, so as to reduce the loss of thin vessels and vessel edge information during the encoding process. In the decoder part, we adopt a dual-path model based on edge detection, and propose a Cross-patch Interactive Attention Mechanism (CIAM) in the main path to enhancing multi-scale spatial channel features and transferring cross-spatial information. Consequently, it improve the network's ability to segment complete and continuous vessel skeletons, reducing vessel segmentation fractures. Finally, the Adaptive Spatial Context Guide Method (ASCGM) is proposed to fuse the prediction results of the two decoder paths, which enhances segmentation details while removing part of the background noise. We evaluated our model on two retinal image datasets and one coronary angiography dataset, achieving outstanding performance in segmentation comprehensive assessment metrics such as AUC and CAL. The experimental results showed that the proposed CFI-Net has superior segmentation performance compared with other existing methods, especially for thin vessels and vessel edges. The code is available at https://github.com/kita0420/CFI-Net.
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Affiliation(s)
- Ning Kang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Maofa Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Cheng Pang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China; Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin, 541004, China
| | - Rushi Lan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China; Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin, 541004, China
| | - Bingbing Li
- Department of Pathology, Ganzhou Municipal Hospital, Ganzhou, 341000, China
| | - Junlin Guan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
| | - Huadeng Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China; Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin, 541004, China
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3
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Zhang L, Yu R, Chen K, Zhang Y, Li Q, Chen Y. Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model. Comput Biol Med 2024; 173:108294. [PMID: 38537565 DOI: 10.1016/j.compbiomed.2024.108294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/21/2024] [Accepted: 03/12/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes. METHODS This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability. RESULTS Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%. CONCLUSIONS The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.
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Affiliation(s)
- Lufang Zhang
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Renyue Yu
- Cardiac Care Unit, Sir RUN RUN Shaw Hospital, Hangzhou, 310000, China.
| | - Keya Chen
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Ying Zhang
- Wenzhou Medical University School of Nursing, 325000, Wenzhou, 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Cixi, 315300, China.
| | - Qiang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Yu Chen
- Nursing Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Li Y, Zhao D, Ma C, Escorcia-Gutierrez J, Aljehane NO, Ye X. CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images. Comput Biol Med 2024; 169:107838. [PMID: 38171259 DOI: 10.1016/j.compbiomed.2023.107838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 11/28/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024]
Abstract
To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Chao Ma
- School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China.
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.
| | - Nojood O Aljehane
- Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Kingdom of Saudi Arabia.
| | - Xia Ye
- School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou, 325000, China.
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Hongliang G, Zhiyao Z, Ahmadianfar I, Escorcia-Gutierrez J, Aljehane NO, Li C. Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization. Comput Biol Med 2024; 169:107888. [PMID: 38157778 DOI: 10.1016/j.compbiomed.2023.107888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/28/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.
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Affiliation(s)
- Guo Hongliang
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Zhang Zhiyao
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Iman Ahmadianfar
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de La Costa, CUC, Barranquilla, 080002, Colombia.
| | - Nojood O Aljehane
- Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia, Tabuk University, KSA.
| | - Chengye Li
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Ranjbarzadeh R, Zarbakhsh P, Caputo A, Tirkolaee EB, Bendechache M. Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm. Comput Biol Med 2024; 168:107723. [PMID: 38000242 DOI: 10.1016/j.compbiomed.2023.107723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/21/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.
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Affiliation(s)
- Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Payam Zarbakhsh
- Electrical and Electronic Engineering Department, Cyprus International University, Via Mersin 10, Nicosia, Northern Cyprus, Turkey.
| | - Annalina Caputo
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Erfan Babaee Tirkolaee
- Department of Industrial Engineering, Istinye University, Istanbul, Turkey; Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan; Department of Industrial and Mechanical Engineering, Lebanese American University, Byblos, Lebanon.
| | - Malika Bendechache
- Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
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Zhao L, Zhou H, Xu L, Yuan W, Shi M, Zhang J, Xue Z. Parameter optimization of the spiral fertiliser discharger for mango orchards based on the discrete element method and genetic algorithm. FRONTIERS IN PLANT SCIENCE 2023; 14:1169091. [PMID: 38510832 PMCID: PMC10952000 DOI: 10.3389/fpls.2023.1169091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 10/16/2023] [Indexed: 03/22/2024]
Abstract
Introduction In order to solve the problems of inaccurate fertilization, unstable fertilization and low fertiliserutilization rate in mango orchard. Methods A small spiral fertiliser discharger was designed based on the agronomic characteristics of fertilization in mango orchard. The fertilizing performance test and parameter optimization of thespiral fertiliser discharger were carried out by combining bench and simulation test. Firstly, the main influencing factors of the fertilizing performance of the spiral fertiliser discharger were analyzed by theoretical calculation formula, and the range of its value was preliminarily determined. At the same time, the digital and discrete element models of the spiral fertiliser discharger were established. Then,the discrete element model of granular fertiliser was established on the basis of the physical and related mechanical simulation parameters of granular fertiliser obtained by experimental statistics.Taking the variable coefficient of fertilizing stability as the response value, the method of singlefactor simulation fertilizing test was used to explore the parameters that have a significant influence on the variable coefficient of fertilizing stability. The response surface method (RSM) was used tosimulate the fertilizing performance of three significant parameters. Based on the quadraticregression orthogonal rotation combination design test, a second-order regression mathematicalmodel between the variable coefficient of fertilizing stability and the significant parameters wasestablished. The variable coefficient of fertilizing stability was as small as possible. The geneticalgorithm (GA) was used to optimize the regression model. Finally, the verification test of thefluidity and applicability of different fertilisers was carried out. Results The results of single factor test showed that the diameter of spiral blade, pitch and rotationalspeed of fertilizing shaft have significant influence on the variable coefficient of fertilizing stability.The optimal parameter combination of the spiral fertiliser discharger was obtained: 98.44 mm for thediameter of spiral blade, 54.8 mm for the pitch, and 24.43 r/min for the rotational speed of fertilizingshaft. The verification results showed that the average relative error of the test was small, and themass flow rate of different fertilisers and the variable coefficient of fertilizing stability could meetthe agronomic requirements of fertilization in mango orchards. The reliability of the discrete elementsimulation test results and research methods of the spiral fertiliser discharger was verified. Conclusion The results and methods of this study can provide reference for the development of mangoorchard fertilization machinery and related fertilizing performance test.
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Affiliation(s)
- Liang Zhao
- College of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
| | - Hongping Zhou
- College of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
| | - Linyun Xu
- College of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
| | - Weidong Yuan
- College of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
| | - Minghong Shi
- College of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
| | - Jian Zhang
- College of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
| | - Zhong Xue
- South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, 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] [Abstract] [Key Words] [MESH Headings] [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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9
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Zhu W, Fang L, Ye X, Medani M, Escorcia-Gutierrez J. IDRM: Brain tumor image segmentation with boosted RIME optimization. Comput Biol Med 2023; 166:107551. [PMID: 37832284 DOI: 10.1016/j.compbiomed.2023.107551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/13/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023]
Abstract
Timely diagnosis of medical conditions can significantly mitigate the risks they pose to human life. Consequently, there is an urgent demand for an effective auxiliary model that assists physicians in accurately diagnosing medical conditions based on imaging data. While multi-threshold image segmentation models have garnered considerable attention due to their simplicity and ease of implementation, the selection of threshold combinations greatly influences the segmentation performance. Traditional optimization algorithms often require substantial time to address multi-threshold image segmentation problems, and their segmentation accuracy is frequently unsatisfactory. As a result, metaheuristic algorithms have been employed in this domain. However, several algorithms suffer from drawbacks such as premature convergence and inadequate exploration of the solution space when it comes to threshold selection. For instance, the recently proposed optimization algorithm RIME, inspired by the physical phenomenon of rime-ice, falls short in terms of avoiding local optima and fully exploring the solution space. Therefore, this study introduces an enhanced version of RIME, called IDRM, which incorporates an interactive mechanism and Gaussian diffusion strategy. The interactive mechanism facilitates information exchange among agents, enabling them to evolve towards more promising directions and increasing the likelihood of discovering the optimal solution. Additionally, the Gaussian diffusion strategy enhances the agents' local exploration capabilities and expands their search within the solution space, effectively preventing them from becoming trapped in local optima. Experimental results on 30 benchmark test functions demonstrate that IDRM exhibits favorable optimization performance across various optimization functions, showcasing its robustness and convergence properties. Furthermore, the algorithm is applied to select threshold combinations for brain tumor image segmentation, and the results are evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The overall findings consistently highlight the exceptional performance of this approach, further validating the effectiveness of IDRM in addressing image segmentation problems.
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Affiliation(s)
- Wei Zhu
- School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
| | - Liming Fang
- School of Humanities and Communication, Zhejiang Gongshang University, Hangzhou, 310000, China.
| | - Xia Ye
- School of the 1st Clinical Medical Sciences(School of Information and Engineering), Wenzhou Medical University, Wenzhou, 325000, China.
| | - Mohamed Medani
- Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil Aseer, 62529, Saudi Arabia.
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.
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10
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Zhu W, Li Z, Heidari AA, Wang S, Chen H, Zhang Y. An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines. SENSORS (BASEL, SWITZERLAND) 2023; 23:8787. [PMID: 37960486 PMCID: PMC10648578 DOI: 10.3390/s23218787] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023]
Abstract
Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining.
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Affiliation(s)
- Wei Zhu
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China; (W.Z.); (Z.L.)
| | - Zhihui Li
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China; (W.Z.); (Z.L.)
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, Iran;
| | - Shuihua Wang
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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11
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Jiang X, Ding Y, Liu M, Wang Y, Li Y, Wu Z. BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT & MRI. Comput Biol Med 2023; 165:107326. [PMID: 37619324 DOI: 10.1016/j.compbiomed.2023.107326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/14/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
Gastrointestinal (GI) cancer is a malignancy affecting the digestive organs. During radiation therapy, the radiation oncologist must precisely aim the X-ray beam at the tumor while avoiding unaffected areas of the stomach and intestines. Consequently, accurate, automated GI image segmentation is urgently needed in clinical practice. While the fully convolutional network (FCN) and U-Net framework have shown impressive results in medical image segmentation, their ability to model long-range dependencies is constrained by the convolutional kernel's restricted receptive field. The transformer has a robust capacity for global modeling owing to its inherent global self-attention mechanism. The TransUnet model leverages the strengths of both the convolutional neural network (CNN) and transformer models through a hybrid CNN-transformer encoder. However, the concatenation of high- and low-level features in the decoder is ineffective in fusing global and local information. To overcome this limitation, we propose an innovative transformer-based medical image segmentation architecture called BiFTransNet, which introduces a BiFusion module into the decoder stage, enabling effective global and local feature fusion by enabling feature integration from various modules. Further, a multilevel loss (ML) strategy is introduced to oversee the learning process of each decoder layer and optimize the use of globally and locally fused contextual features at different scales. Our method achieved a Dice score of 89.51% and an intersection-over-union (IoU) score of 86.54% on the UW-Madison Gastrointestinal Segmentation dataset. Moreover, our method attained a Dice score of 78.77% and a Hausdorff distance (HD) of 27.94% on the Synapse Multi-organ Segmentation dataset. Compared with the state-of-the-art methods, our proposed method achieves superior segmentation performance in gastrointestinal segmentation tasks. More significantly, our method can be easily extended to medical segmentation in different modalities such as CT and MRI. Our method achieves clinical multimodal medical segmentation and provides decision supports for clinical radiotherapy plans.
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Affiliation(s)
- Xin Jiang
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China; Department of Radiology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| | - Yizhou Ding
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China.
| | - Mingzhe Liu
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China.
| | - Yong Wang
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China.
| | - Yan Li
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China.
| | - Zongda Wu
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China.
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12
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Yu X, Qin W, Lin X, Shan Z, Huang L, Shao Q, Wang L, Chen M. Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Comput Biol Med 2023; 165:107408. [PMID: 37672924 DOI: 10.1016/j.compbiomed.2023.107408] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/19/2023] [Accepted: 08/27/2023] [Indexed: 09/08/2023]
Abstract
Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH.
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Affiliation(s)
- Xiaoming Yu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Wenxiang Qin
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Xiao Lin
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Zhuohan Shan
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Liyao Huang
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China.
| | - Qike Shao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China.
| | - Liangxing Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Mayun Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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13
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Houssein EH, Oliva D, Samee NA, Mahmoud NF, Emam MM. Liver Cancer Algorithm: A novel bio-inspired optimizer. Comput Biol Med 2023; 165:107389. [PMID: 37678138 DOI: 10.1016/j.compbiomed.2023.107389] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/04/2023] [Accepted: 08/25/2023] [Indexed: 09/09/2023]
Abstract
This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Diego Oliva
- Depto. Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico.
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Noha F Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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14
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Chen Z, Xinxian L, Guo R, Zhang L, Dhahbi S, Bourouis S, Liu L, Wang X. Dispersed differential hunger games search for high dimensional gene data feature selection. Comput Biol Med 2023; 163:107197. [PMID: 37390761 DOI: 10.1016/j.compbiomed.2023.107197] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/02/2023]
Abstract
The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.
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Affiliation(s)
- Zhiqing Chen
- School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou, 325035, China.
| | - Li Xinxian
- Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China.
| | - Ran Guo
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China; College of Information Engineering, Yangzhou University, Yangzhou, 225127, China; Research and Development Center for E-Learning, Ministry of Education, Beijing, 100039, China.
| | - Sami Dhahbi
- Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil, Aseer, 62529, Saudi Arabia.
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Xianchuan Wang
- Information Technology Center, Wenzhou Medical University, Wenzhou, 325035, China.
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15
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Zhang X, Lu B, Zhang L, Pan Z, Liao M, Shen H, Zhang L, Liu L, Li Z, Hu Y, Gao Z. An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction. Comput Biol Med 2023; 163:107166. [PMID: 37364530 DOI: 10.1016/j.compbiomed.2023.107166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/25/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.
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Affiliation(s)
- Xiang Zhang
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| | - Bin Lu
- Wenzhou City Bureau of Justice, Wenzhou, Zhejiang, 325000, China.
| | - Lyuzheng Zhang
- B-soft Co.,Ltd., B-soft Wisdom Building, No.92 Yueda Lane, Binjiang District, Hangzhou, 310052, China.
| | - Zhifang Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Minjie Liao
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| | - Huihui Shen
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| | - Li Zhang
- Wenzhou Hongsheng Intellectual Property Agency (General Partnership), Wenzhou, Zhejiang, 325000, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Zuxiang Li
- Organization Department of the Party Committee, Wenzhou University, Wenzhou, 325000, China.
| | - YiPao Hu
- Wenzhou Health Commission, Wenzhou, Zhejiang, 325000, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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16
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Zheng K, Wu J, Yuan Y, Liu L. From single to multiple: Generalized detection of Covid-19 under limited classes samples. Comput Biol Med 2023; 164:107298. [PMID: 37573722 DOI: 10.1016/j.compbiomed.2023.107298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/13/2023] [Accepted: 07/28/2023] [Indexed: 08/15/2023]
Abstract
Amid the unfolding Covid-19 pandemic, there is a critical need for rapid and accurate diagnostic methods. In this context, the field of deep learning-based medical image diagnosis has witnessed a swift evolution. However, the prevailing methodologies often rely on large amounts of labeled data and require comprehensive medical knowledge. Both of these prerequisites pose significant challenges in real clinical settings, given the high cost of data labeling and the complexities of disease representations. Addressing this gap, we propose a novel problem setting, the Open-Set Single-Domain Generalization for Medical Image Diagnosis (OSSDG-MID). In OSSDG-MID, our aim is to train a model exclusively on a single source domain, so it can classify samples from the target domain accurately, designating them as 'unknown' if they don't belong to the source domain sample category space. Our innovative solution, the Multiple Cross-Matching method (MCM), enhances the identification of these 'unknown' categories by generating auxiliary samples that fall outside the category space of the source domain. Experimental evaluations on two diverse cross-domain image classification tasks demonstrate that our approach outperforms existing methodologies in both single-domain generalization and open-set image classification.
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Affiliation(s)
- Kaihui Zheng
- Department of Intensive Care Unit, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jianhua Wu
- Department of Intensive Care Unit, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Youjun Yuan
- Department of Emergency, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
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17
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Peng Z, Wang L, Tong L, Zou H, Liu D, Zhang C. Multi-threshold image segmentation of 2D OTSU inland ships based on improved genetic algorithm. PLoS One 2023; 18:e0290750. [PMID: 37624785 PMCID: PMC10456136 DOI: 10.1371/journal.pone.0290750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Waterway transportation is a crucial mode of transportation, but ensuring navigational safety in waterways requires effective guidance of ships by the Water Resources Bureau. However, supervisors may only be interested in the ship portion of a complex image and need to quickly obtain relevant ship information. Therefore, this paper proposes a two-dimensional OTSU inland ships multi-threshold image segmentation algorithm based on the improved genetic algorithm. The improved algorithm enhances search accuracy and efficiency, improving image thresholding accuracy and reducing algorithm time complexity. Experimental verification shows the algorithm has excellent evaluation indexes and can achieve real-time segmentation of complex images. This method can not only address the challenges of complex inland navigation environments and difficult acquisition of target data sets, but also be applied to optimization problems in other fields by combining various metaheuristic algorithms.
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Affiliation(s)
- Zhongbo Peng
- School of shipping and naval architecture, Chongqing Jiaotong University, Chongqing, China
| | - Lumeng Wang
- School of shipping and naval architecture, Chongqing Jiaotong University, Chongqing, China
| | - Liang Tong
- School of shipping and naval architecture, Chongqing Jiaotong University, Chongqing, China
| | - Han Zou
- School of shipping and naval architecture, Chongqing Jiaotong University, Chongqing, China
| | - Dan Liu
- School of shipping and naval architecture, Chongqing Jiaotong University, Chongqing, China
| | - Chunyu Zhang
- School of shipping and naval architecture, Chongqing Jiaotong University, Chongqing, China
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18
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Lijuan Y, Yanhu Z. A face recognition algorithm based on the combine of image feature compensation and improved PSO. Sci Rep 2023; 13:12372. [PMID: 37524837 PMCID: PMC10390551 DOI: 10.1038/s41598-023-39607-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/27/2023] [Indexed: 08/02/2023] Open
Abstract
Face recognition systems have been widely applied in various scenarios in people's daily lives. The recognition rate and speed of face recognition systems have always been the two key technical factors that researchers focus on. Many excellent recognition algorithms achieve high recognition rates or good recognition speeds. However, more research is needed to develop algorithms that can effectively balance these two indicators. In this study, we introduce an improved particle swarm optimization algorithm into a face recognition algorithm based on image feature compensation techniques. This allows the system to achieve high recognition rates while simultaneously enhancing the recognition efficiency, aiming to strike a balance between the two aspects. This approach provides a new perspective for the application of image feature compensation techniques in face recognition systems. It helps achieve a broader range of applications for face recognition technology by reducing the recognition speed as much as possible while maintaining a satisfactory recognition rate. Ultimately, this leads to an improved user experience.
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Affiliation(s)
- Yan Lijuan
- Guangdong Songshan Polytechnic, Shaoguan, 512126, Guangdong, China
| | - Zhang Yanhu
- Guangdong Songshan Polytechnic, Shaoguan, 512126, Guangdong, China.
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19
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Dokeroglu T. A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients. PeerJ Comput Sci 2023; 9:e1430. [PMID: 37346714 PMCID: PMC10280461 DOI: 10.7717/peerj-cs.1430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/18/2023] [Indexed: 06/23/2023]
Abstract
Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.
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20
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Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-47. [PMID: 37359740 PMCID: PMC10220350 DOI: 10.1007/s11831-023-09928-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
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Affiliation(s)
- Mohammad H. Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006 Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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21
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Pan JS, Yue L, Chu SC, Hu P, Yan B, Yang H. Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem. ENTROPY (BASEL, SWITZERLAND) 2023; 25:314. [PMID: 36832680 PMCID: PMC9955014 DOI: 10.3390/e25020314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/27/2023] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
Inspired by the bamboo growth process, Chu et al. proposed the Bamboo Forest Growth Optimization (BFGO) algorithm. It incorporates bamboo whip extension and bamboo shoot growth into the optimization process. It can be applied very well to classical engineering problems. However, binary values can only take 0 or 1, and for some binary optimization problems, the standard BFGO is not applicable. This paper firstly proposes a binary version of BFGO, called BBFGO. By analyzing the search space of BFGO under binary conditions, the new curve V-shaped and Taper-shaped transfer function for converting continuous values into binary BFGO is proposed for the first time. A long-mutation strategy with a new mutation approach is presented to solve the algorithmic stagnation problem. Binary BFGO and the long-mutation strategy with a new mutation are tested on 23 benchmark test functions. The experimental results show that binary BFGO achieves better results in solving the optimal values and convergence speed, and the variation strategy can significantly enhance the algorithm's performance. In terms of application, 12 data sets derived from the UCI machine learning repository are selected for feature-selection implementation and compared with the transfer functions used by BGWO-a, BPSO-TVMS and BQUATRE, which demonstrates binary BFGO algorithm's potential to explore the attribute space and choose the most significant features for classification issues.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Longkang Yue
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Pei Hu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Bin Yan
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Hongmei Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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