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Saha DK. An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows. Heliyon 2024; 10:e34242. [PMID: 39114056 PMCID: PMC11305221 DOI: 10.1016/j.heliyon.2024.e34242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/09/2024] [Accepted: 07/05/2024] [Indexed: 08/10/2024] Open
Abstract
Cow diseases are a major source of concern for people. Some diseases in animals that are discovered in their early stages can be treated while they are still treatable. If lumpy skin disease (LSD) is not properly treated, it can result in significant financial losses for the farm animal industry. Animals like cows that sign this disease have their skin seriously affected. A reduction in milk production, reduced fertility, growth retardation, miscarriage, and occasionally death are all detrimental effects of this disease in cows. Over the past three months, LSD has affected thousands of cattle in nearly fifty districts across Bangladesh, causing cattle farmers to worry about their livelihood. Although the virus is very contagious, after receiving the right care for a few months, the affected cattle can be cured. The goal of this study was to use various deep learning and machine learning models to determine whether or not cows had lumpy disease. To accomplish this work, a Convolution neural network (CNN) based novel architecture is proposed for detecting the illness. The lumpy disease-affected area has been identified using image preprocessing and segmentation techniques. After the extraction of numerous features, our proposed model has been evaluated to classify LSD. Four CNN models, DenseNet, MobileNetV2, Xception, and InceptionResNetV2 were used to classify the framework, and evaluation metrics were computed to determine how well the classifiers worked. MobileNetV2 has been able to achieve 96% classification accuracy and an AUC score of 98% by comparing results with recently published relevant works, which seems both good and promising.
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Affiliation(s)
- Dip Kumar Saha
- American International University-Bangladesh, Department of Computer Science and Engineering, Dhaka, Bangladesh
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2
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Han S, Jeon W, Gong W, Kwak IY. MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning. BIOLOGY 2023; 12:1291. [PMID: 37887001 PMCID: PMC10604338 DOI: 10.3390/biology12101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/22/2023] [Accepted: 09/26/2023] [Indexed: 10/28/2023]
Abstract
In this study, we constructed a model to predict abnormal cardiac sounds using a diverse set of auscultation data collected from various auscultation positions. Abnormal heart sounds were identified by extracting features such as peak intervals and noise characteristics during systole and diastole. Instead of using raw signal data, we transformed them into log-mel 2D spectrograms, which were employed as input variables for the CNN model. The advancement of our model involves integrating a deep learning architecture with feature extraction techniques based on existing knowledge of cardiac data. Specifically, we propose a multi-channel-based heart signal processing (MCHeart) scheme, which incorporates our proposed features into the deep learning model. Additionally, we introduce the ReLCNN model by applying residual blocks and MHA mechanisms to the LCNN architecture. By adding murmur features with a smoothing function and training the ReLCNN model, the weighted accuracy of the model increased from 79.6% to 83.6%, showing a performance improvement of approximately 4% point compared to the LCNN baseline model.
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Affiliation(s)
- Soyul Han
- Department of Applied Statistics, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Woongsun Jeon
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Wuming Gong
- Lillehei Heart Institute, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Il-Youp Kwak
- Department of Applied Statistics, Chung-Ang University, Seoul 06974, Republic of Korea;
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3
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TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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4
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Shahid B, Abbas M, Ur Rehman A, Ul Abideen Z. IAPC2: Improved and Automatic Classification of Polyp for Colorectal Cancer. 2023 INTERNATIONAL CONFERENCE ON BUSINESS ANALYTICS FOR TECHNOLOGY AND SECURITY (ICBATS) 2023. [DOI: 10.1109/icbats57792.2023.10111431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Bisma Shahid
- Riphah International University,Department of Computer Science,Lahore,Pakistan
| | - Maria Abbas
- Riphah International University,Department of Computer Science,Lahore,Pakistan
| | - Abd Ur Rehman
- Riphah International University,Department of Computer Science,Lahore,Pakistan
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5
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ELKarazle K, Raman V, Then P, Chua C. Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:1225. [PMID: 36772263 PMCID: PMC9953705 DOI: 10.3390/s23031225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/08/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.
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Affiliation(s)
- Khaled ELKarazle
- School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia
| | - Valliappan Raman
- Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore 641014, India
| | - Patrick Then
- School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia
| | - Caslon Chua
- Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3122, Australia
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6
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Montalbo FJP. Fusing Compressed Deep ConvNets with a Self-Normalizing Residual Block and Alpha Dropout for a Cost-Efficient Classification and Diagnosis of Gastrointestinal Tract Diseases. MethodsX 2022; 9:101925. [DOI: 10.1016/j.mex.2022.101925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
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Ahmed F, Shimizu M, Wang J, Sakai K, Kiwa T. Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data. MICROMACHINES 2022; 13:1352. [PMID: 36014274 PMCID: PMC9413860 DOI: 10.3390/mi13081352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/10/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 μm microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of 6×10-5 using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters.
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Mahanty C, Kumar R, Mishra BK, Barna C. COVID-19 detection with X-ray images by using transfer learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-219273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score.
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Affiliation(s)
| | - Raghvendra Kumar
- Department of Computer Science and Engineering, GIET University, Odisha, India
| | | | - Cornel Barna
- Faculty of Exact Sciences, “Aurel Vlaicu” University of Arad, Arad, Romania
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Attallah O. An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques. BIOSENSORS 2022; 12:299. [PMID: 35624600 PMCID: PMC9138764 DOI: 10.3390/bios12050299] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2022] [Accepted: 04/24/2022] [Indexed: 06/01/2023]
Abstract
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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10
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Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network. Processes (Basel) 2021. [DOI: 10.3390/pr9081475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.
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11
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Zheng Q, Zhao P, Zhang D, Wang H. MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification. INT J INTELL SYST 2021. [DOI: 10.1002/int.22586] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Qinghe Zheng
- School of Information Science and Engineering Shandong University Qingdao China
| | - Penghui Zhao
- School of Information Science and Engineering Shandong University Qingdao China
| | - Deliang Zhang
- School of Information Science and Engineering Shandong University Qingdao China
| | - Hongjun Wang
- School of Information Science and Engineering Shandong University Qingdao China
- Public (Innovation) Experimental Teaching Center Shandong University Qingdao China
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12
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Use of Generative Adversarial Networks (GAN) for Taphonomic Image Augmentation and Model Protocol for the Deep Learning Analysis of Bone Surface Modifications. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Deep learning models are based on a combination of neural network architectures, optimization parameters and activation functions. All of them provide exponential combinations whose computational fitness is difficult to pinpoint. The intricate resemblance of the microscopic features that are found in bone surface modifications make their differentiation challenging, and determining a baseline combination of optimizers and activation functions for modeling seems necessary for computational economy. Here, we experiment with combinations of the most resolutive activation functions (relu, swish, and mish) and the most efficient optimizers (stochastic gradient descent (SGD) and Adam) for bone surface modification analysis. We show that despite a wide variability of outcomes, a baseline of relu–SGD is advised for raw bone surface modification data. For imbalanced samples, augmented datasets generated through generative adversarial networks are implemented, resulting in balanced accuracy and an inherent bias regarding mark replication. In summary, although baseline procedures are advised, these do not prevent to overcome Wolpert’s “no free lunch” theorem and extend it beyond model architectures.
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Chieng HH, Wahid N, Ong P. PARAMETRIC FLATTEN-T SWISH: AN ADAPTIVE NONLINEAR ACTIVATION FUNCTION FOR DEEP LEARNING. JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY 2020. [DOI: 10.32890/jict.20.1.2021.9267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
QActivation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community. However, ReLU contains several shortcomings that can result in inefficient training of the deep neural networks, these are: 1) the negative cancellation property of ReLU tends to treat negative inputs as unimportant information for the learning, resulting in performance degradation; 2) the inherent predefined nature of ReLU is unlikely to promote additional flexibility, expressivity, and robustness to the networks; 3) the mean activation of ReLU is highly positive and leads to bias shift effect in network layers; and 4) the multilinear structure of ReLU restricts the non-linear approximation power of the networks. To tackle these shortcomings, this paper introduced Parametric Flatten-T Swish (PFTS) as an alternative to ReLU. By taking ReLU as a baseline method, the experiments showed that PFTS improved classification accuracy on SVHN dataset by 0.31%, 0.98%, 2.16%, 17.72%, 1.35%, 0.97%, 39.99%, and 71.83% on DNN-3A, DNN-3B, DNN-4, DNN-5A, DNN-5B, DNN-5C, DNN-6, and DNN-7, respectively. Besides, PFTS also achieved the highest mean rank among the comparison methods. The proposed PFTS manifested higher non-linear approximation power during training and thereby improved the predictive performance of the networks.
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Affiliation(s)
- Hock Hung Chieng
- Faculty of Information Technology and Computer Science, Universiti Tun Hussein Onn Malaysia, Malaysia
| | - Noorhaniza Wahid
- Faculty of Information Technology and Computer Science, Universiti Tun Hussein Onn Malaysia, Malaysia
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14
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Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM. MATHEMATICS 2020. [DOI: 10.3390/math8101784] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Forecasting the demand for container throughput is a critical indicator to measure the development level of a port in global business management and industrial development. Time-series analysis approaches are crucial techniques for forecasting the demand for container throughput. However, accurate demand forecasting for container throughput remains a challenge in time-series analysis approaches. In this study, we proposed a mixed-precision neural architecture to forecasting the demand for container throughput. This study is the first work to use a mixed-precision neural network to forecast the container throughput—the mixed-precision architecture used the convolutional neural network for learning the strength of the features and used long short-term memory to identify the crucial internal representation of time series depending on the strength of the features. The experiments on the demand for container throughput of the five ports in Taiwan were conducted to compare our deep learning architecture with other forecasting approaches. The results indicated that our mixed-precision neural architecture exhibited higher forecasting performance than classic machine learning approaches, including adaptive boosting, random forest regression, and support vector regression. The proposed architecture can effectively predict the demand for port container throughput and effectively reduce the costs of planning and development of ports in the future.
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Wang Y, Zhang M, Wu R, Gao H, Yang M, Luo Z, Li G. Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities. Brain Sci 2020; 10:E442. [PMID: 32664599 PMCID: PMC7407985 DOI: 10.3390/brainsci10070442] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 07/05/2020] [Accepted: 07/08/2020] [Indexed: 11/16/2022] Open
Abstract
Silent speech decoding is a novel application of the Brain-Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles' movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.
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Affiliation(s)
- You Wang
- State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China; (Y.W.); (M.Z.); (R.W.); (H.G.)
| | - Ming Zhang
- State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China; (Y.W.); (M.Z.); (R.W.); (H.G.)
| | - RuMeng Wu
- State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China; (Y.W.); (M.Z.); (R.W.); (H.G.)
| | - Han Gao
- State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China; (Y.W.); (M.Z.); (R.W.); (H.G.)
| | - Meng Yang
- Department of Computer Science and Technology, School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China;
| | - Zhiyuan Luo
- Computer Learning Research Centre, Royal Holloway, University of London, Egham Hill, Egham, Surrey TW20 0EX, UK;
| | - Guang Li
- State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, China; (Y.W.); (M.Z.); (R.W.); (H.G.)
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