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Tang Z, Tang S, Wang H, Li R, Zhang X, Zhang W, Yuan X, Zang Y, Li Y, Zhou T, Li Y. S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test. IEEE J Biomed Health Inform 2024; 28:4456-4470. [PMID: 38819974 DOI: 10.1109/jbhi.2024.3407881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
BACKGROUND Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. METHODOLOGY In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. RESULTS The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. CONCLUSIONS In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.
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2
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Esmi N, Golshan Y, Asadi S, Shahbahrami A, Gaydadjiev G. A fuzzy fine-tuned model for COVID-19 diagnosis. Comput Biol Med 2023; 153:106483. [PMID: 36621192 PMCID: PMC9811914 DOI: 10.1016/j.compbiomed.2022.106483] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 12/25/2022] [Indexed: 01/06/2023]
Abstract
The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.
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Affiliation(s)
- Nima Esmi
- Faculty of Science and Engineering, University of Groningen, Netherlands.
| | - Yasaman Golshan
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Sara Asadi
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Asadollah Shahbahrami
- Faculty of Science and Engineering, University of Groningen, Netherlands; Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Georgi Gaydadjiev
- Faculty of Science and Engineering, University of Groningen, Netherlands.
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3
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HG-SMA: hierarchical guided slime mould algorithm for smooth path planning. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10398-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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4
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Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G. Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2683-2723. [PMID: 36685136 PMCID: PMC9838547 DOI: 10.1007/s11831-023-09883-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.
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Affiliation(s)
| | - Alaettin Ucan
- Department of Computer Engineering, Osmaniye Korkut Ata University, Osmaniye, Turkey
| | - Turgay Ibrikci
- Department of Software Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Bahman Arasteh
- Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey
| | - Gultekin Isik
- Department of Computer Engineering, Igdir University, Igdir, Turkey
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5
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Wang G, Guo S, Han L, Zhao Z, Song X. COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm. Biomed Signal Process Control 2023; 79:104159. [PMID: 36119901 PMCID: PMC9464590 DOI: 10.1016/j.bspc.2022.104159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/10/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022]
Abstract
Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.
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Affiliation(s)
- Guowei Wang
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Shuli Guo
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Lina Han
- Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Zhilei Zhao
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiaowei Song
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
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6
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Islam Bhuiyan MR, Azam S, Montaha S, Jim RI, Karim A, Khan IU, Brady M, Hasan MZ, De Boer F, Mukta MSH. Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation. Digit Health 2023; 9:20552076231215915. [PMID: 38025114 PMCID: PMC10668574 DOI: 10.1177/20552076231215915] [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: 05/14/2023] [Accepted: 11/06/2023] [Indexed: 12/01/2023] Open
Abstract
COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.
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Affiliation(s)
- Md. Rahad Islam Bhuiyan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, Australia
| | - Sidratul Montaha
- Department of Computer Science, University of Calgary, Calgary, Canada
| | - Risul Islam Jim
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Asif Karim
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, Australia
| | - Inam Ullah Khan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Mark Brady
- School of Law, Faculty of Arts and Society, Charles Darwin University, Casuarina, NT, Australia
| | - Md. Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Friso De Boer
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, Australia
| | - Md. Saddam Hossain Mukta
- Department of Computer Science and Engineering, United International University (UIU), Dhaka, Bangladesh
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7
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Al-Shourbaji I, Kachare PH, Abualigah L, Abdelhag ME, Elnaim B, Anter AM, Gandomi AH. A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images. Pathogens 2022; 12:pathogens12010017. [PMID: 36678365 PMCID: PMC9860560 DOI: 10.3390/pathogens12010017] [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: 11/16/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Pre-trained machine learning models have recently been widely used to detect COVID-19 automatically from X-ray images. Although these models can selectively retrain their layers for the desired task, the output remains biased due to the massive number of pre-trained weights and parameters. This paper proposes a novel batch normalized convolutional neural network (BNCNN) model to identify COVID-19 cases from chest X-ray images in binary and multi-class frameworks with a dual aim to extract salient features that improve model performance over pre-trained image analysis networks while reducing computational complexity. The BNCNN model has three phases: Data pre-processing to normalize and resize X-ray images, Feature extraction to generate feature maps, and Classification to predict labels based on the feature maps. Feature extraction uses four repetitions of a block comprising a convolution layer to learn suitable kernel weights for the features map, a batch normalization layer to solve the internal covariance shift of feature maps, and a max-pooling layer to find the highest-level patterns by increasing the convolution span. The classifier section uses two repetitions of a block comprising a dense layer to learn complex feature maps, a batch normalization layer to standardize internal feature maps, and a dropout layer to avoid overfitting while aiding the model generalization. Comparative analysis shows that when applied to an open-access dataset, the proposed BNCNN model performs better than four other comparative pre-trained models for three-way and two-way class datasets. Moreover, the BNCNN requires fewer parameters than the pre-trained models, suggesting better deployment suitability on low-resource devices.
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Affiliation(s)
- Ibrahim Al-Shourbaji
- Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Pramod H. Kachare
- Department of Electronics & Telecommunication Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai 400706, Maharashtra, India
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- Faculty of Information Technology, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
- Correspondence: (L.A.); (A.H.G.)
| | - Mohammed E. Abdelhag
- Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia
| | - Bushra Elnaim
- Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Riyadh 11671, Saudi Arabia
| | - Ahmed M. Anter
- Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef 62511, Egypt
| | - Amir H. Gandomi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
- University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
- Correspondence: (L.A.); (A.H.G.)
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8
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Wang G, Guo S, Han L, Song X, Zhao Y. Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion. Comput Biol Med 2022; 150:106181. [PMID: 36240596 PMCID: PMC9533636 DOI: 10.1016/j.compbiomed.2022.106181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.
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Affiliation(s)
- Guowei Wang
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Shuli Guo
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.
| | - Lina Han
- Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
| | - Xiaowei Song
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Yuanyuan Zhao
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
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9
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Sanghvi HA, Patel RH, Agarwal A, Gupta S, Sawhney V, Pandya AS. A deep learning approach for classification of COVID and pneumonia using DenseNet-201. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 33:IMA22812. [PMID: 36249091 PMCID: PMC9537800 DOI: 10.1002/ima.22812] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 06/24/2022] [Accepted: 09/09/2022] [Indexed: 05/27/2023]
Abstract
In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.
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Affiliation(s)
| | - Riki H. Patel
- Department of CEECSFlorida Atlantic UniversityBoca RatonFloridaUSA
| | - Ankur Agarwal
- Department of CEECSFlorida Atlantic UniversityBoca RatonFloridaUSA
| | - Shailesh Gupta
- Department of Clinical Trials and ResearchSpecialty Retina CenterCoral SpringsFloridaUSA
| | - Vivek Sawhney
- Department of Clinical Trials and ResearchSpecialty Retina CenterCoral SpringsFloridaUSA
| | - Abhijit S. Pandya
- Department of CEECSFlorida Atlantic UniversityBoca RatonFloridaUSA
- Department of Clinical Trials and ResearchSpecialty Retina CenterCoral SpringsFloridaUSA
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10
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Gao Y, Meng J, Shu J, Liu Y. BIM-based task and motion planning prototype for robotic assembly of COVID-19 hospitalisation light weight structures. AUTOMATION IN CONSTRUCTION 2022; 140:104370. [PMID: 35607382 PMCID: PMC9117582 DOI: 10.1016/j.autcon.2022.104370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 04/25/2022] [Accepted: 05/16/2022] [Indexed: 05/13/2023]
Abstract
Fast transmission of COVID-19 led to mass cancelling of events to contain the virus outbreak. Amid lockdown restrictions, a vast number of construction projects came to a halt. Robotic platforms can perform construction projects in an unmanned manner, thus ensuring the essential construction tasks are not suspended during the pandemic. This research developed a BIM-based prototype, including a task planning algorithm and a motion planning algorithm, to assist in the robotic assembly of COVID-19 hospitalisation light weight structures with prefabricated components. The task planning algorithm can determine the assembly sequence and coordinates for various types of prefabricated components. The motion planning algorithm can generate robots' kinematic parameters for performing the assembly of the prefabricated components. Testing of the prototype finds that it has satisfactory performance in terms of 1) the reasonableness of assembly sequence determined, 2) reachability for the assembly coordinates of prefabricated components, and 3) capability to avoid obstacles.
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Affiliation(s)
- Yifan Gao
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- Center for Balance Architecture, Zhejiang University, Hangzhou 310058, China
- The Architectural Design & Research Institute of Zhejiang University Co. Ltd, Hangzhou 310058, China
| | - Jiawei Meng
- Department of Mechanical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Jiangpeng Shu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
| | - Yuanchang Liu
- Department of Mechanical Engineering, University College London, London WC1E 6BT, United Kingdom
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11
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Anosri S, Panagant N, Bureerat S, Pholdee N. Success history based adaptive multi-objective differential evolution variants with an interval scheme for solving simultaneous topology, shape and sizing truss reliability optimisation. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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12
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Alfadhli J, Jaragh A, Alfailakawi MG, Ahmad I. FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm. Neural Comput Appl 2022; 34:11163-11175. [PMID: 35281623 PMCID: PMC8898343 DOI: 10.1007/s00521-022-07034-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/30/2022] [Indexed: 01/18/2023]
Abstract
In this paper, an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) is proposed. Unlike the original SMA where population size is fixed in every epoch, FP-SMA will adaptively change population size in order to effectively balance exploitation and exploration characteristics of SMA’s different phases. Experimental results on 13 standard and 30 IEEE CEC2014 benchmark functions have shown that FP-SMA can achieve significant reduction in run time while maintaining good solution quality when compared to the original SMA. Typical saving in terms of function evaluations for all benchmarks was between 20 and 30% on average with a maximum being as high as 60% in some cases. Therefore, with its higher computation efficiency, FP-SMA is much more favorable choice as compared to SMA in time stringent applications.
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Affiliation(s)
- Jassim Alfadhli
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait
| | - Ali Jaragh
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait
| | - Mohammad Gh. Alfailakawi
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait
| | - Imtiaz Ahmad
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait
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