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Gong X, Fong S, Si YW. Fast multi-subsequence monitoring on streaming time-series based on Forward-propagation. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.03.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Deb S, Tian Z, Fong S, Wong R, Millham R, Wong KKL. Elephant search algorithm applied to data clustering. Soft comput 2018. [DOI: 10.1007/s00500-018-3076-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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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] [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. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2018. [DOI: 10.1166/jmihi.2018.2343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [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] [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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Song Q, Fong S, Deb S, Hanne T. Gaussian Guided Self-Adaptive Wolf Search Algorithm Based on Information Entropy Theory. ENTROPY (BASEL, SWITZERLAND) 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] [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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Usman SM, Usman M, Fong S. Epileptic Seizures Prediction Using Machine Learning Methods. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 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] [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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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] [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] [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 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] [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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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] [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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Wong KKL, Fong S, Wang D. Impact of advanced parallel or cloud computing technologies for image guided diagnosis and therapy. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
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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] [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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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] [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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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] [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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Khan A, Liu LS, Usman M, Fong S. Early Diagnosis of Alzheimer Disease Using Instance-Based Learning Techniques. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2016. [DOI: 10.1166/jmihi.2016.1808] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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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. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2016. [DOI: 10.1166/jmihi.2016.1800] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [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. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2016. [DOI: 10.1166/jmihi.2016.1809] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [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. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2016. [DOI: 10.1166/jmihi.2016.1807] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [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. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2016. [DOI: 10.1166/jmihi.2016.1806] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [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] [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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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] [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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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] [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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