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Fong S, Fortino G, Ghista D, Piccialli F. Special issue on deep learning and big data analytics for medical e-diagnosis/AI-based e-diagnosis. Neural Comput Appl 2023; 35:1-5. [PMID: 37362576 PMCID: PMC10224755 DOI: 10.1007/s00521-023-08689-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
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2
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Mao X, Zhou D, Lin K, Zhang B, Gao J, Ling F, Zhu L, Yu S, Chen P, Zhang C, Zhang C, Ye G, Fong S, Chen G, Luo W. Single-cell and spatial transcriptome analyses revealed cell heterogeneity and immune environment alternations in metastatic axillary lymph nodes in breast cancer. Cancer Immunol Immunother 2023; 72:679-695. [PMID: 36040519 DOI: 10.1007/s00262-022-03278-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/12/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Tumor heterogeneity plays essential roles in developing cancer therapies, including therapies for breast cancer (BC). In addition, it is also very important to understand the relationships between tumor microenvironments and the systematic immune environment. METHODS Here, we performed single-cell, VDJ sequencing and spatial transcriptome analyses on tumor and adjacent normal tissue as well as axillar lymph nodes (LNs) and peripheral blood mononuclear cells (PBMCs) from 8 BC patients. RESULTS We found that myeloid cells exhibited environment-dependent plasticity, where a group of macrophages with both M1 and M2 signatures possessed high tumor specificity spatially and was associated with worse patient survival. Cytotoxic T cells in tumor sites evolved in a separate path from those in the circulatory system. T cell receptor (TCR) repertoires in metastatic LNs showed significant higher consistency with TCRs in tumor than those in nonmetastatic LNs and PBMCs, suggesting the existence of common neo-antigens across metastatic LNs and primary tumor cites. In addition, the immune environment in metastatic LNs had transformed into a tumor-like status, where pro-inflammatory macrophages and exhausted T cells were upregulated, accompanied by a decrease in B cells and neutrophils. Finally, cell interactions showed that cancer-associated fibroblasts (CAFs) contributed most to shaping the immune-suppressive microenvironment, while CD8+ cells were the most signal-responsive cells. CONCLUSIONS This study revealed the cell structures of both micro- and macroenvironments, revealed how different cells diverged in related contexts as well as their prognostic capacities, and displayed a landscape of cell interactions with spatial information.
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Affiliation(s)
- Xiaofan Mao
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China.,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China
| | - Dan Zhou
- Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China
| | - Kairong Lin
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China.,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China
| | - Beiying Zhang
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China.,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China
| | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, China
| | - Fei Ling
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Lewei Zhu
- Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China
| | - Sifei Yu
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China.,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China
| | - Peixian Chen
- Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China
| | - Chuling Zhang
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China.,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China
| | - Chunguo Zhang
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China.,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China
| | - Guolin Ye
- Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | - Guoqiang Chen
- Department of Rheumatology and Immunology, The First People's Hospital of Foshan, Foshan, China.
| | - Wei Luo
- Clinical Research Institute, The First People's Hospital of Foshan, Foshan, China. .,Medical Engineering Technology Research and development center of Immune Repertoire in Foshan, The First People's Hospital of Foshan, Foshan, China.
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Liu XX, Yang J, Fong S, Dey N, Millham RC, Fiaidhi J. All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing. Int J Environ Res Public Health 2022; 19:ijerph191710959. [PMID: 36078679 PMCID: PMC9518365 DOI: 10.3390/ijerph191710959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 05/13/2023]
Abstract
The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the "Delta" virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.
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Affiliation(s)
- Xian-Xian Liu
- Department of Computer and Information Science, University of Macau, Taipa, Macau SAR 519000, China
| | - Jie Yang
- Chongqing Industry & Trade Polytechnic, Chongqing 408000, China
- Correspondence: (J.Y.); (S.F.)
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau SAR 519000, China
- Correspondence: (J.Y.); (S.F.)
| | - Nilanjan Dey
- Department of Computer Science and Engineering, JIS University, Kolkata 700109, India
| | - Richard C. Millham
- ICT & Society Group, Durban University of Technology, Durban 4001, South Africa
| | - Jinan Fiaidhi
- e-Health Research Group, Computer Science Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
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5
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Song Q, Li T, Fong S, Wu F. An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm. Math Biosci Eng 2021; 18:9076-9093. [PMID: 34814336 DOI: 10.3934/mbe.2021447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.
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Affiliation(s)
- Qun Song
- College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China
| | - Tengyue Li
- Department of Computer and Information Science, University of Macao, Macao SAR, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macao, Macao SAR, China
| | - Feng Wu
- Zhuhai Institute of Advanced Technology (ZIAT), Chinese Academy of Science, Zhuhai, China
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Lan K, Li G, Jie Y, Tang R, Liu L, Fong S. Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification. Math Biosci Eng 2021; 18:5573-5591. [PMID: 34517501 DOI: 10.3934/mbe.2021281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures.
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Affiliation(s)
- Kun Lan
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China
- DACC Laboratory, Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences, Zhuhai 519080, China
| | - Gloria Li
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China
- DACC Laboratory, Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences, Zhuhai 519080, China
| | - Yang Jie
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China
- DACC Laboratory, Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences, Zhuhai 519080, China
| | - Rui Tang
- Department of Management and Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China
| | - Liansheng Liu
- Department of Medical Imaging, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Simon Fong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China
- DACC Laboratory, Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences, Zhuhai 519080, China
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Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. Int J Biol Sci 2021; 17:1581-1587. [PMID: 33907522 PMCID: PMC8071762 DOI: 10.7150/ijbs.58855] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/06/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.
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Affiliation(s)
- Shigao Huang
- Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau 999078, Macau SAR, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau 999078, Macau SAR, China
- Chongqing Industry & Trade Polytechnic 408000, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau 999078, Macau SAR, China
| | - Qi Zhao
- Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau 999078, Macau SAR, China
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Lin W, Yu T, Gao C, Liu F, Li T, Fong S, Wang Y. A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.09.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Tang J, Su Q, Su B, Fong S, Cao W, Gong X. Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition. Comput Methods Programs Biomed 2020; 197:105622. [PMID: 32629293 DOI: 10.1016/j.cmpb.2020.105622] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 06/15/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion. METHODS First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting. RESULTS By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%. CONCLUSION In summary, the proposed approach greatly outperforms other competitive methods.
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Affiliation(s)
- Jialin Tang
- Beijing Institute of Technology, Zhuhai 519088, China; City University of Macau, Macau, China.
| | | | - Binghua Su
- Beijing Institute of Technology, Zhuhai 519088, China.
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Macau, China.
| | - Wei Cao
- Beijing Institute of Technology, Zhuhai 519088, China.
| | - Xueyuan Gong
- Beijing Institute of Technology, Zhuhai 519088, China.
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Li T, Fong S, Siu SWI, Yang XS, Liu LS, Mohammed S. White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment. Comput Methods Programs Biomed 2020; 197:105724. [PMID: 32877817 DOI: 10.1016/j.cmpb.2020.105724] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure. DL functions like a black box. As a result, many medical practitioners are reductant to use deep learning as the only tool for critical machine learning application, such as aiding tool for cancer diagnosis. METHODS In this paper, a framework of white learning is being proposed which takes advantages of both black box learning and white box learning. Usually, black box learning will give a high standard of accuracy and white box learning will provide an explainable direct acyclic graph. According to our design, there are 3 stages of White Learning, loosely coupled WL, semi coupled WL and tightly coupled WL based on degree of fusion of the white box learning and black box learning. In our design, a case of loosely coupled WL is tested on breast cancer dataset. This approach uses deep learning and an incremental version of Naïve Bayes network. White learning is largely defied as a systemic fusion of machine learning models which result in an explainable Bayes network which could find out the hidden relations between features and class and deep learning which would give a higher accuracy of prediction than other algorithms. We designed a series of experiments for this loosely coupled WL model. RESULTS The simulation results show that using WL compared to standard black-box deep learning, the levels of accuracy and kappa statistics could be enhanced up to 50%. The performance of WL seems more stable too in extreme conditions such as noise and high dimensional data. The relations by Bayesian network of WL are more concise and stronger in affinity too. CONCLUSION The experiments results deliver positive signals that WL is possible to output both high classification accuracy and explainable relations graph between features and class.
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Affiliation(s)
- Tengyue Li
- Department of Computer and Information Science, University of Macau, Macau SAR.
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Macau SAR.
| | - Shirley W I Siu
- Department of Computer and Information Science, University of Macau, Macau SAR.
| | - Xin-She Yang
- Department of Design Engineering and Mathematics, Middlesex University, London, UK.
| | - Lian-Sheng Liu
- Department of Radiology, First Affiliated Hospital of Guangzhou University of TCM, China.
| | - Sabah Mohammed
- Department of Computer Science, Lakehead University, Thunder Bay, Canada.
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Wang Y, Wu Q, Dey N, Fong S, Ashour AS. Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.05.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Song W, Zhang L, Tian Y, Fong S, Liu J, Gozho A. CNN-based 3D object classification using Hough space of LiDAR point clouds. Hum Cent Comput Inf Sci 2020. [DOI: 10.1186/s13673-020-00228-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, the characteristics of LiDAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3D objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3D objects. In addition, a semi-automatic 3D object labeling tool is developed to build a LiDAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.
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Toor AA, Usman M, Younas F, M. Fong AC, Khan SA, Fong S. Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems. Sensors (Basel) 2020; 20:s20072131. [PMID: 32283841 PMCID: PMC7180875 DOI: 10.3390/s20072131] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/04/2020] [Accepted: 04/07/2020] [Indexed: 12/02/2022]
Abstract
With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.
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Affiliation(s)
- Affan Ahmed Toor
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan; (A.A.T.); (M.U.); (F.Y.)
| | - Muhammad Usman
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan; (A.A.T.); (M.U.); (F.Y.)
| | - Farah Younas
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan; (A.A.T.); (M.U.); (F.Y.)
| | - Alvis Cheuk M. Fong
- Department of Computing, Western Michigan University, Gladstone, MI 49837, USA
- Correspondence: ; Tel.: +1-269-2763-110
| | - Sajid Ali Khan
- Department of Software Engineering, Foundation University Islamabad, Islambad 44000, Pakistan;
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Macau 999078, China;
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Fong IH, Li T, Fong S, Wong RK, Tallón-Ballesteros AJ. Predicting concentration levels of air pollutants by transfer learning and recurrent neural network. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105622] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Lan K, Liu L, Li T, Chen Y, Fong S, Marques JAL, Wong RK, Tang R. Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04769-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Yeung CHT, Fong S, Malik PRV, Edginton AN. Quantifying breast milk intake by term and preterm infants for input into paediatric physiologically based pharmacokinetic models. Matern Child Nutr 2020; 16:e12938. [PMID: 31965755 PMCID: PMC7083422 DOI: 10.1111/mcn.12938] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 11/13/2019] [Accepted: 12/15/2019] [Indexed: 12/18/2022]
Abstract
Despite the many benefits of breast milk, mothers taking medication are often uncertain about the risks of drug exposure to their infants and decide not to breastfeed. Physiologically based pharmacokinetic models can contribute to drug‐in‐milk safety assessments by predicting the infant exposure and subsequently, risk for toxic effects that would result from continuous breastfeeding. This review aimed to quantify breast milk intake feeding parameters in term and preterm infants using literature data for input into paediatric physiologically based pharmacokinetic models designed for drug‐in‐milk risk assessment. Ovid MEDLINE and Embase were searched up to July 2, 2019. Key study reference lists and grey literature were reviewed. Title, abstract and full text were screened in nonduplicate. Daily weight‐normalized human milk intake (WHMI) and feeding frequency by age were extracted. The review process retrieved 52 studies. A nonlinear regression equation was constructed to describe the WHMI of exclusively breastfed term infants from birth to 1 year of age. In all cases, preterm infants fed with similar feeding parameters to term infants on a weight‐normalized basis. Maximum WHMI was 152.6 ml/kg/day at 19.7 days, and weighted mean feeding frequency was 7.7 feeds/day. Existing methods for approximating breast milk intake were refined by using a comprehensive set of literature data to describe WHMI and feeding frequency. Milk feeding parameters were quantified for preterm infants, a vulnerable population at risk for high drug exposure and toxic effects. A high‐risk period of exposure at 2–4 weeks of age was identified and can inform future drug‐in‐milk risk assessments.
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Affiliation(s)
- Cindy H T Yeung
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Simon Fong
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Paul R V Malik
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Andrea N Edginton
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
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Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett 2019; 471:61-71. [PMID: 31830558 DOI: 10.1016/j.canlet.2019.12.007] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 02/06/2023]
Abstract
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
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Affiliation(s)
- Shigao Huang
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, China.
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China.
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Zhong Y, Fong S, Hu S, Wong R, Lin W. A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation. Sensors (Basel) 2019; 19:s19204536. [PMID: 31635371 PMCID: PMC6832605 DOI: 10.3390/s19204536] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 09/25/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.
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Affiliation(s)
- Yan Zhong
- Department of Big Data and Cloud Computing, Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences, Zhuhai 519000, China.
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa 999078, Macau.
| | - Shimin Hu
- Department of Computer and Information Science, University of Macau, Taipa 999078, Macau.
| | - Raymond Wong
- School of Computer Science & Engineering, University of New South Wales, Sydney 2052, Australia.
| | - Weiwei Lin
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
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Chen Y, Wu F, Wu Y, Li J, Yue P, Deng Y, Lamb KV, Fong S, Liu Y, Zhang Y. Development of interventions for an intelligent and individualized mobile health care system to promote healthy diet and physical activity: using an intervention mapping framework. BMC Public Health 2019; 19:1311. [PMID: 31623589 PMCID: PMC6798431 DOI: 10.1186/s12889-019-7639-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 09/16/2019] [Indexed: 12/25/2022] Open
Abstract
Background The mortality of coronary heart disease can be largely reduced by modifying unhealthy lifestyles. However, the long-term effectiveness of interventions for modifying unhealthy diet and physical inactivity of patients with coronary heart disease remain unsatisfactory worldwide. This study aims to systematically design a set of theory-based and evidence-based, individualized, and intelligent interventions for promoting the adoption and maintenance of a healthy diet and physical activity level in patients with coronary heart disease. Methods The interventions will be delivered by a mobile health care system called Individualized, Intelligent and Integrated Cardiovascular Application for Risk Elimination. Three steps of the intervention mapping framework were used to systematically develop the interventions. Step 1: needs assessment, which was carried out by a literature review, in-depth interviews and focus group discussions. Step 2: development of objective matrix for diet and physical activity changes, based on the intersection of objectives and determinants from the Contemplation-Action-Maintenance behavior change model. Step 3: formulation of evidence-based methods and strategies, and practical applications, through a systematic review of existing literature, research team discussions, and consultation with multidisciplinary expert panels. Results Three needs relevant to content of the intervention, one need relevant to presentation modes of the intervention, and four needs relevant to functional features of the application were identified. The objective matrix includes three performance objectives, and 24 proximal performance objectives. The evidence-based and theory-based interventions include 31 strategies, 61 evidence-based methods, and 393 practical applications. Conclusions This article describes the development of theory-based and evidence-based interventions of the mobile health care system for promoting the adoption and maintenance of a healthy diet and physical activity level in a structured format. The results will provide a theoretical and methodological basis to explore the application of intervention mapping in developing effective behavioral mobile health interventions for patients with coronary heart disease. Trial registration Chinese Clinical Trial Registry: ChiCTR-INR-16010242. Registered 24 December 2016. http://www.chictr.org.cn/index.aspx
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Affiliation(s)
- Yuling Chen
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
| | - Fangqin Wu
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
| | - Ying Wu
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China.
| | - Jia Li
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
| | - Peng Yue
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
| | - Ying Deng
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
| | - Karen V Lamb
- Department of Adult Health Gerontological Nursing Rush University IL, Chicago, CA, 60613, USA
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Macau, China
| | - Yisi Liu
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
| | - Yan Zhang
- School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China
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Chu PM, Cho S, Park J, Fong S, Cho K. Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems. Hum Cent Comput Inf Sci 2019. [DOI: 10.1186/s13673-019-0178-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
Ground segmentation is an important step for any autonomous and remote-controlled systems. After separating ground and nonground parts, many works such as object tracking and 3D reconstruction can be performed. In this paper, we propose an efficient method for segmenting the ground data of point clouds acquired from multi-channel Lidar sensors. The goal of this study is to completely separate ground points and nonground points in real time. The proposed method segments ground data efficiently and accurately in various environments such as flat terrain, undulating/rugged terrain, and mountainous terrain. First, the point cloud in each obtained frame is divided into small groups. We then focus on the vertical and horizontal directions separately, before processing both directions concurrently. Experiments were conducted, and the results showed the effectiveness of the proposed ground segment method. For flat and sloping terrains, the accuracy is over than 90%. Besides, the quality of the proposed method is also over than 80% for bumpy terrains. On the other hand, the speed is 145 frames per second. Therefore, in both simple and complex terrains, we gained good results and real-time performance.
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Lan K, Fong S, Liu LS, Wong RK, Dey N, Millham RC, Wong KK. A clustering based variable sub-window approach using particle swarm optimisation for biomedical sensor data monitoring. ENTERP INF SYST-UK 2019. [DOI: 10.1080/17517575.2019.1597388] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Kun Lan
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Lian-Sheng Liu
- First Affiliated Hospital of Guangzhou University of TCM, Guangzhou, Guangdong, China
| | - Raymond K. Wong
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India
| | - Richard C. Millham
- Department of Information Technology, Durban University of Technology, Durban, South Africa
| | - Kelvin K.L. Wong
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, Fujian, China
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Fong S, Wang D, Fiaidhi J, Mohammed S, Chen L, Ling L. Clinical Pathways Inference from Decision Rules by Hybrid Stream Mining and Fuzzy Unordered Rule Induction Strategy in Diabetes Treatment. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190306153302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Simon Fong
- Department of Computer and Information Science University of Macau, Macau SAR, Macao
| | - Dana Wang
- Department of Computer and Information Science University of Macau, Macau SAR, Macao
| | - Jinan Fiaidhi
- Department of Computer Science Lakehead University, Thunder Bay, Canada
| | - Sabah Mohammed
- Department of Computer Science Lakehead University, Thunder Bay, Canada
| | - Libo Chen
- Department of Endocrinology Guangdong Medical College Affiliated Shenzhen Nanshan Hospital Shenzhen 518052, China
| | - Li Ling
- Department of Endocrinology, Guangdong Medical College Affiliated Shenzhen Nanshan Hospital, No.89 Taoyuan Road, Nanshan District, Shenzhen 518052, China
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Song W, Zou S, Tian Y, Fong S, Cho K. Classifying 3D objects in LiDAR point clouds with a back-propagation neural network. Hum Cent Comput Inf Sci 2018. [DOI: 10.1186/s13673-018-0152-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
AbstractDue to object recognition accuracy limitations, unmanned ground vehicles (UGVs) must perceive their environments for local path planning and object avoidance. To gather high-precision information about the UGV’s surroundings, Light Detection and Ranging (LiDAR) is frequently used to collect large-scale point clouds. However, the complex spatial features of these clouds, such as being unstructured, diffuse, and disordered, make it difficult to segment and recognize individual objects. This paper therefore develops an object feature extraction and classification system that uses LiDAR point clouds to classify 3D objects in urban environments. After eliminating the ground points via a height threshold method, this describes the 3D objects in terms of their geometrical features, namely their volume, density, and eigenvalues. A back-propagation neural network (BPNN) model is trained (over the course of many iterations) to use these extracted features to classify objects into five types. During the training period, the parameters in each layer of the BPNN model are continually changed and modified via back-propagation using a non-linear sigmoid function. In the system, the object segmentation process supports obstacle detection for autonomous driving, and the object recognition method provides an environment perception function for terrain modeling. Our experimental results indicate that the object recognition accuracy achieve 91.5% in outdoor environment.
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Tahmassebi A, Gandomi AH, Fong S, Meyer-Baese A, Foo SY. Multi-stage optimization of a deep model: A case study on ground motion modeling. PLoS One 2018; 13:e0203829. [PMID: 30231077 PMCID: PMC6145533 DOI: 10.1371/journal.pone.0203829] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 08/28/2018] [Indexed: 11/18/2022] Open
Abstract
In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challenging real-world problem, the modeling of the spectral acceleration experienced by a particle during earthquakes. This approach has three main stages to optimize the deep model topology, the hyper-parameters, and its performance, respectively. This pipeline optimizes the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity in multiple stages, while simultaneously solving the unknown parameters of the regression model. Among the seven adaptive learning rate optimization algorithms, Nadam optimization algorithm has shown the best performance results in the current study. The proposed approach is shown to be a suitable tool to generate solid models for this complex real-world system. The results also show that the parallel pipeline of iDeepLe has the capacity to handle big data problems as well.
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Affiliation(s)
- Amirhessam Tahmassebi
- Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306-4120, United States of America
- * E-mail:
| | - Amir H. Gandomi
- School of Business, Stevens Institute of Technology, Hoboken, New Jersey 07030, United States of America
| | - Simon Fong
- Department of Computer Science and Information Science, University of Macau, Taipa, Macau
| | - Anke Meyer-Baese
- Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306-4120, United States of America
| | - Simon Y. Foo
- Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Tallahassee, Florida 32310-6046, United States of America
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Ma BB, Fong S, Millham R. Data stream mining in fog computing environment with feature selection using ensemble of swarm search algorithms. 2018 Conference on Information Communications Technology and Society (ICTAS) 2018. [DOI: 10.1109/ictas.2018.8368770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Wong KKL, Wang D, Fong S, Ng EYK. A Special Section on Big Data Technology and Information Management in Medical and Health Informatics. j med imaging hlth inform 2018. [DOI: 10.1166/jmihi.2018.2343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Bhadra P, Yan J, Li J, Fong S, Siu SWI. AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci Rep 2018; 8:1697. [PMID: 29374199 PMCID: PMC5785966 DOI: 10.1038/s41598-018-19752-w] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 01/03/2018] [Indexed: 02/05/2023] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs’ broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive:negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew’s correlation coefficient (MCC) of 0.9, area under the receiver operating characteristic curve (AUC-ROC) of 0.99, and the Kappa statistic of 0.9. Descriptor analysis of AMP/non-AMP distributions by means of Pearson correlation coefficients revealed that reduced feature sets (from a full-featured set of 105 to a minimal-feature set of 23) can result in comparable performance in all respects except for some reductions in precision. Furthermore, AmPEP outperformed existing methods in terms of accuracy, MCC, and AUC-ROC when tested on benchmark datasets.
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Affiliation(s)
- Pratiti Bhadra
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Jielu Yan
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Jinyan Li
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Shirley W I Siu
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China.
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Song Q, Fong S, Deb S, Hanne T. Gaussian Guided Self-Adaptive Wolf Search Algorithm Based on Information Entropy Theory. Entropy (Basel) 2018; 20:E37. [PMID: 33265128 PMCID: PMC7512246 DOI: 10.3390/e20010037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/13/2017] [Accepted: 01/04/2018] [Indexed: 11/17/2022]
Abstract
Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and demonstrated its capability especially for large-scale problems. However, it still inherits a common weakness for other swarm intelligence algorithms: that its performance is heavily dependent on the chosen values of the control parameters. In 2016, we published the Self-Adaptive Wolf Search Algorithm (SAWSA), which offers a simple solution to the adaption problem. As a very simple schema, the original SAWSA adaption is based on random guesses, which is unstable and naive. In this paper, based on the SAWSA, we investigate the WSA search behaviour more deeply. A new parameter-guided updater, the Gaussian-guided parameter control mechanism based on information entropy theory, is proposed as an enhancement of the SAWSA. The heuristic updating function is improved. Simulation experiments for the new method denoted as the Gaussian-Guided Self-Adaptive Wolf Search Algorithm (GSAWSA) validate the increased performance of the improved version of WSA in comparison to its standard version and other prevalent swarm algorithms.
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Affiliation(s)
- Qun Song
- Department of Computer and Information Science, University of Macau, Macau 999078, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Macau 999078, China
| | - Suash Deb
- Decision Sciences and Modelling Program, Victoria University, Melbourne 8001, Australia
| | - Thomas Hanne
- Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland
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Usman SM, Usman M, Fong S. Epileptic Seizures Prediction Using Machine Learning Methods. Comput Math Methods Med 2017; 2017:9074759. [PMID: 29410700 PMCID: PMC5749318 DOI: 10.1155/2017/9074759] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/06/2017] [Accepted: 10/04/2017] [Indexed: 11/23/2022]
Abstract
Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects.
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Affiliation(s)
- Syed Muhammad Usman
- Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
| | - Muhammad Usman
- Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
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Pancham J, Millham R, Fong S. Assessment of Feasible Methods Used by the Health Care Industry for Real Time Location. Annals of Computer Science and Information Systems 2017. [DOI: 10.15439/2017f541] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Charruyer A, Fong S, Weisenberger T, Taneja M, Soeung C, Ghadially R. 876 Manipulation of stem cell divisional behavior: Selectively promoting asymmetric and symmetric keratinocyte divisions in vitro. J Invest Dermatol 2017. [DOI: 10.1016/j.jid.2017.02.902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Fong S, Song W, Cho K, Wong R, Wong KKL. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition. Sensors (Basel) 2017; 17:s17030476. [PMID: 28264470 PMCID: PMC5375762 DOI: 10.3390/s17030476] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/20/2016] [Accepted: 12/22/2016] [Indexed: 11/25/2022]
Abstract
In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.
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Affiliation(s)
- Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa 999078, Macau, China.
| | - Wei Song
- Department of Digital Media Technology, North China University of Technology, Beijing 100144, China.
| | - Kyungeun Cho
- Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea.
| | - Raymond Wong
- School of Computer Science and Engineering, University of New South Wales, Sydney 2052, NSW, Australia.
| | - Kelvin K L Wong
- School of Medicine, Western Sydney University, Sydney 2560, NSW, Australia.
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Wang D, Fong S, Wong RK, Mohammed S, Fiaidhi J, Wong KKL. Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT. Sci Rep 2017; 7:43167. [PMID: 28230161 PMCID: PMC5322330 DOI: 10.1038/srep43167] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 01/20/2017] [Indexed: 11/21/2022] Open
Abstract
Outlier detection in bioinformatics data streaming mining has received significant attention by research communities in recent years. The problems of how to distinguish noise from an exception and deciding whether to discard it or to devise an extra decision path for accommodating it are causing dilemma. In this paper, we propose a novel algorithm called ODR with incrementally Optimized Very Fast Decision Tree (ODR-ioVFDT) for taking care of outliers in the progress of continuous data learning. By using an adaptive interquartile-range based identification method, a tolerance threshold is set. It is then used to judge if a data of exceptional value should be included for training or otherwise. This is different from the traditional outlier detection/removal approaches which are two separate steps in processing through the data. The proposed algorithm is tested using datasets of five bioinformatics scenarios and comparing the performance of our model and other ones without ODR. The results show that ODR-ioVFDT has better performance in classification accuracy, kappa statistics, and time consumption. The ODR-ioVFDT applied onto bioinformatics streaming data processing for detecting and quantifying the information of life phenomena, states, characters, variables and components of the organism can help to diagnose and treat disease more effectively.
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Affiliation(s)
- Dantong Wang
- Department of Computer and Information Science, Univeristy of Macau, SAR, Macau
| | - Simon Fong
- Department of Computer and Information Science, Univeristy of Macau, SAR, Macau
| | - Raymond K Wong
- School of Computer Science and Engineering, University of New South Wales, Australia
| | - Sabah Mohammed
- Department of Computer Science, Lakehead University, Thunder Bay, Canada
| | - Jinan Fiaidhi
- Department of Computer Science, Lakehead University, Thunder Bay, Canada
| | - Kelvin K L Wong
- School of Medicine, University of Western Sydney, New South Wales, Australia
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Wong KKL, Fong S, Wang D. Impact of advanced parallel or cloud computing technologies for image guided diagnosis and therapy. J Xray Sci Technol 2017; 25:187-192. [PMID: 28234271 DOI: 10.3233/xst-17252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Kelvin K L Wong
- School of Medicine, Western Sydney University, Sydney, Australia
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, Macau, China
| | - Defeng Wang
- Department of Imaging and Interventional Radiology, Research Center for Medical Image Computing, The Chinese University of Hong Kong, Hong Kong
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Li J, Fong S, Sung Y, Cho K, Wong R, Wong KKL. Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification. BioData Min 2016; 9:37. [PMID: 27980678 PMCID: PMC5131504 DOI: 10.1186/s13040-016-0117-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 11/21/2016] [Indexed: 11/27/2022] Open
Abstract
Background An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. Results In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Conclusions Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.
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Affiliation(s)
- Jinyan Li
- Department of Computer and Information Science, University of Macau, Taipa, Macau, S.A.R. China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, S.A.R. China
| | - Yunsick Sung
- Computer Engineering Division, Keimyung University, Daegu, South Korea
| | - Kyungeun Cho
- Department of Multimedia Engineering, College of Engineering, Dongguk University, Dongdaeipgu, South Korea
| | - Raymond Wong
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2000 Australia
| | - Kelvin K L Wong
- Centre for Biomedical Engineering, School of Electrical & Electronic Engineering, University of Adelaide, Adelaide, Australia.,School of Medicine, Western Sydney University, Campbelltown, Sydney Australia
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Li J, Fong S, Siu S, Mohammed S, Fiaidhi J, Wong KKL. WITHDRAWN: Improving classification of protein binders for virtual drug screening by novel swarm-based feature selection techniques. Comput Med Imaging Graph 2016:S0895-6111(16)30087-8. [PMID: 27717712 DOI: 10.1016/j.compmedimag.2016.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 07/18/2016] [Accepted: 08/09/2016] [Indexed: 11/19/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Jinyan Li
- Department of Information and Computer Science, University of Macau, Macau SAR, China.
| | - Simon Fong
- Department of Information and Computer Science, University of Macau, Macau SAR, China.
| | - Shirley Siu
- Department of Information and Computer Science, University of Macau, Macau SAR, China.
| | - Sabah Mohammed
- Department of Computer Science, Lakehead University, Thunder Bay, Canada.
| | - Jinan Fiaidhi
- Department of Computer Science, Lakehead University, Thunder Bay, Canada.
| | - Kelvin K L Wong
- School of Medicine, University of Western Sydney, New South Wales, Australia.
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Zeng D, Peng J, Fong S, Qiu Y, Wong R, Mon YJ. WITHDRAWN: Sentiment prediction by text mining medical documents using optimized swarm search-based feature selection. Comput Med Imaging Graph 2016:S0895-6111(16)30074-X. [PMID: 27693005 DOI: 10.1016/j.compmedimag.2016.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 07/14/2016] [Accepted: 07/28/2016] [Indexed: 10/21/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Daohui Zeng
- First affiliated hospital of Guangzhou university of TCM, Guangzhou, China.
| | | | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau.
| | - Yining Qiu
- Department of Computer and Information Science, University of Macau, Taipa, Macau.
| | - Raymond Wong
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
| | - Yi-Jen Mon
- Department of Information Engineering, Taoyuan Innovation Institute of Technology, Taoyuan City, Taiwan.
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Wong KKL, Wang D, Fong S, Ng EYK. <I>A Special Section on</I> Advanced Computing Techniques for Machine Learning and Data Mining in Medical Informatics. J Med Imaging Hlth Inform 2016. [DOI: 10.1166/jmihi.2016.1800] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Chan JH, Visutarrom T, Cho SB, Engchuan W, Mongolnam P, Fong S. A Hybrid Approach to Human Posture Classification During TV Watching. J Med Imaging Hlth Inform 2016. [DOI: 10.1166/jmihi.2016.1809] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Li J, Fong S, Mohammed S, Fiaidhi J, Chen Q, Tan Z. Solving the Under-Fitting Problem for Decision Tree Algorithms by Incremental Swarm Optimization in Rare-Event Healthcare Classification. J Med Imaging Hlth Inform 2016. [DOI: 10.1166/jmihi.2016.1807] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Doungpan N, Engchuan W, Meechai A, Fong S, Chan JH. Gene-Network-Based Feature Set (GNFS) for Expression-Based Cancer Classification. J Med Imaging Hlth Inform 2016. [DOI: 10.1166/jmihi.2016.1806] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Fong S, Wang D, Fiaidhi J, Mohammed S, Chen L, Ling L. WITHDRAWN: Clinical pathways inference from decision rules by hybrid stream mining and fuzzy unordered rule induction strategy. Comput Med Imaging Graph 2016:S0895-6111(16)30065-9. [PMID: 27666793 DOI: 10.1016/j.compmedimag.2016.06.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 06/17/2016] [Accepted: 06/27/2016] [Indexed: 12/29/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Simon Fong
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | - Dana Wang
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | - Jinan Fiaidhi
- Department of Computer Science, Lakehead University, Thunder Bay, Canada
| | - Sabah Mohammed
- Department of Computer Science, Lakehead University, Thunder Bay, Canada
| | - Libo Chen
- Department of Endocrinology, Guangdong Medical College Affiliated Shenzhen Nanshan Hospital, Shenzhen 518052, China.
| | - Li Ling
- Department of Endocrinology, Guangdong Medical College Affiliated Shenzhen Nanshan Hospital, Shenzhen 518052, China.
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Min X, Liu L, He Y, Gong X, Fong S, Xu Q, Wong KKL. WITHDRAWN: Benchmarking swarm intelligence clustering algorithms with case study of medical data. Comput Med Imaging Graph 2016:S0895-6111(16)30053-2. [PMID: 27666794 DOI: 10.1016/j.compmedimag.2016.06.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 05/03/2016] [Accepted: 06/11/2016] [Indexed: 11/22/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Xiaoping Min
- Department of Computer Science, Xiamen University, Xiamen 361005, China.
| | - Liansheng Liu
- First Affiliated Hospital of Guangzhou University of TCM, Guangzhou 510405, Guangdong, China.
| | - Yuying He
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361005, China.
| | - Xueyuan Gong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China.
| | - Simon Fong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China.
| | - Qiwen Xu
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China.
| | - Kelvin K L Wong
- Translational Gastroenterology Laboratory, School of Medicine, Western Sydney University, Campbelltown, NSW 2751, Australia.
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Li J, Liu LS, Fong S, Wong RK, Mohammed S, Fiaidhi J, Sung Y, Wong KKL. WITHDRAWN: Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data. Comput Med Imaging Graph 2016:S0895-6111(16)30037-4. [PMID: 27236411 DOI: 10.1016/j.compmedimag.2016.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 04/27/2016] [Accepted: 05/05/2016] [Indexed: 11/25/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Jinyan Li
- Department of Computer and Information Science, University of Macau, Taipa, Macao.
| | - Lian-Sheng Liu
- First Affiliated Hospital of Guangzhou University of TCM, Guangzhou 510405, Guangdong, China.
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macao.
| | - Raymond K Wong
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2000, Australia.
| | - Sabah Mohammed
- Department of Computer Science, Lakehead University, Thunder Bay, Canada.
| | - Jinan Fiaidhi
- Department of Computer Science, Lakehead University, Thunder Bay, Canada.
| | - Yunsick Sung
- Computer Engineering Division, Keimyung University, Daegu, South Korea.
| | - Kelvin K L Wong
- School of Medicine, University of Western Sydney, New South Wales, Australia.
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