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Dr Steve Ling

Biography

Dr Steve S.H. Ling has worked extensively on design and analysis on computational intelligence technologies since 2000.  He has a MPhil and PhD in Electronic and Information Engineering from the Hong Kong Polytechnic University (HKPU). The technical quality of the two theses is excellent, which is reflected by the results reported in his publications and his MPhil thesis was nominated as best thesis of the faculty.  In the year 2006-2007, he had worked as a research associate in School of Electrical, Electronic and Computer Engineering, The University of Western Australia (UWA). In the year 2008-2009, He had worked as research fellow in Department of Electrical and Computer Engineering, National University of Singapore (NUS). He joined to University of Technology Sydney (UTS) as chancellor’s postdoctoral research fellow and Lecturer in 2009 and is now Senior Lecturer.  Over the last 10 years, he has worked in the areas of design and analysis the topologies of neural network and neural fuzzy network, design and analysis of the operations of the evolutionary algorithm and the applications on different areas such as decision making, classification, and health technologies. 

Being an active research, he has published over 160 research papers and book chapter on fuzzy logic, neural and neural-fuzzy networks, genetic algorithm, swarm intelligence, and their applications.  Currently, he serves as Editor-in-Chief for Journal of Intelligent Learning Systems and Applications and Associate Editor for Electronics Letters [ERA A] and Australian Journal of Electrical and Electronics Engineering.

Besides, he is active in the international society. He has been a senior member IEEE since 2012.  In addition, He has a reviewer of some international journals (e.g. IEEE Transactions), conferences and grant proposals. He also acted as session chair and organizer in several international conferences. Since 2001, he was invited as speaker in various universities, institute and conferences.

Professional

Editorial Board

Editor-in-Chief, Journal of Intelligent Learning Systems and Applications, 2012–present. 

Associate Editor, IET Electronics Letters [ERA A], 2016-present.

Founding Associate Editor, CAAI Transactions on Intelligence Technology, 2015-present

Advisory Board Member, Scientific Research Publishing Inc, 2013-present.

Editorial Board Member, International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), 2011–present.

Associate Editor, Australian Journal of Electrical & Electronics Engineering, 2011–present.

Advisory Board Member, Transactions on Machine Learning and Artificial Intelligence, 2014–present.

Advisory Board Member, International Journal of Computational Intelligence Techniques, 2013–present.

Associate Editor, International Journal of Neural Networks, 2011–present.

Managing Executive Editor, Journal of Intelligent Learning Systems and Applications, 2010–2011.

Guest Editor, Special issue on T-S Fuzzy-Model-Based Control for Nonlinear Time-Delay Systems, on Journal of Applied Mathematics, 2015.

Lead Guest Editor, Special issue on hybrid intelligent methods for health technologies, on Applied Soft Computing, vol. 20, pp. 1-3, July 2014.

Guest Editor, Special issue on hybrid intelligent methods for health technologies, on Applied Soft Computing, vol. 14, Part A, pp. 1-3, Jan 2014.

Guest Editor, Special issue on hybrid evolutionary systems for manufacturing processes, on Applied Soft Computing, vol. 13, no.3, pp. 1329-1331,  2013.

Lead Guest Editor, Special issue on computational intelligence and health technologies, on International Journal of Bioinformatics Research and Applications, vol. 8, no. 5/6, 2012.

Conference Organization

Member, Program Committee, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2016), Changsha, China, Aug 2016.

Special Session Lead Organizer, “Special Session on Advanced Computational Intelligence Methods for Health Technologies and Applications”, 2016 IEEE World Congress on Computational Intelligence (WCCI 2016), Canada, July 2016.

Member, Technical Program Committee, International Conference on Mechanics and Mechanical Engineering (MME2015), Chengdu, China, Dec 2015.

Member, Program Committee, The 2015 11th International Conference on Natural Computation (ICNC'15), Zhangjiajie, China, Aug 2015.

Member, Program Committee, The 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'15), Zhangjiajie, China, Aug 2015.

Member, Program Committee, The Second BRICS Congress on Computational Intelligence (BRCIS-CCI 2015), Xiamen, China, Aug 2015.

Member, Program Committee, International Conference on Intelligent Information Processing security and Advanced Communication (IPAC 2015), Algeria, Nov 2015.  

Member, Program Committee, 10th International Conference on Natural Computation (ICNC 2014), Xiamen, China, Aug 2014

Member, Technical Program Committee, 2nd Conference on Artificial Intelligence and Data Mining (AIDM 2014), Suzhou, China, Mar 2014.

Member, Technical Program Committee, 3rd Australian Control Conference (AUCC 2013), Perth, Australia, Nov 2013.

Special Session Organizer, “Special Session on Industrial Applications of Evolving Fuzzy and Neural systems”, 2012 IEEE World Congress on Computational Intelligence (WCCI 2012), Australia, June 2012.

Member, Technical Committee, 2011 IEEE International Conference on Signal Processing, Communications and Computing, Xian, China, Sep 2011.

Special Session Organizer, “Special Session on Industrial Applications of Evolving Fuzzy Systems”, IEEE International Conference on Fuzzy Systems 2011 (FuzzIEEE 2011), Taiwan, June 2011.

Special Session Organizer and Chair, “Special Session on Computational Intelligence for Bioinformatics-Computational Intelligence in Biomedical Sciences and DNA Forensics”, 2010 IEEE World Congress on Computational Intelligence (WCCI 2010), Spain, July 2010.

Session Chair, 3rd International Conference on Network & System Security (NSS 2009), Gold Coast, Australia, Oct 2009.

Track Chair Assistant, International Conference on Control, Automation, Robotics and Vision, ICARCV 2008.

Image of Steve Ling
Senior Lecturer, School of Biomedical Engineering
Core Member, CHT - Centre for Health Technologies
Doctor of Philosophy
Senior Member, The Institution of Electrical and Electronic Engineers
 
Phone
+61 2 9514 2390

Research Interests

Neural networks and neural-fuzzy networks design
Machine learning
Analysis and design of evolutionary computations
Computational intelligent for biomedical systems (Nocturnal hypoglycemic detection, EGG/ECG classification, BCI)
Multi-agents system

Can supervise: Yes
Since 2010, 6 PhD students are completed.

48510-Introduction to Electrical Engineering

48520-Electronics and Circuits

Books

Lam, H.K., Ling, S.H. & Nguyen, H.T. 2012, Computational intelligence and its applications: Evolutionary computation, fuzzy logic, neural network and support vector machine techniques.
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© 2012 by Imperial College Press. All rights reserved. This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.
Ling, S.S. 2010, Genetic Algorithm and Variable Feed-Forward Neural Networks: Theory and Application, 1, Lambert Academic Publishing, Germany.
This thesis focuses on the real-coded genetic algorithm and different topologies of feedforward neural networks. Results in the following areas will be reported: (1) a real-coded genetic algorithm with new crossover and mutation operations, and its applications; (2) three different topologies of variable feed-forward neural networks, and their applications to shortterm electric load forecasting in Hong Kong and hand~written graffiti recognition.

Chapters

Ling, S.S. 2012, 'Nocturnal hypoglycaemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach' in Lam, H.K., Ling, S.H. & Nguyen, H.U.N.G. (eds), Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques.
Dehestani, D., Guo, Y., Ling, S.S., Su, S.T.E.V.E.N. & Nguyen, H. 2012, 'Intelligent fault detection and isolation of HVAC system based on online support vector machine' in Lam, H.K., Ling, S.T.E.V.E. & Nguyen, H. (eds), Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques.

Conferences

Savitha, R., Chan, K.Y., San, P.P., Ling, S.H. & Suresh, S. 2017, 'A hybrid Deep Boltzmann Functional Link Network for classification problems', 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016.
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© 2016 IEEE. This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic features and input weights for the discriminative model. A discriminative Functional Link Network (FLN) uses these features to approximate the nonlinear relationship between a set of features and their classes. FLN has three layers, namely, the input layer, the enhancement layer and the output layer. In a DBFLN, the features generated at the two hidden layers of the DBM act as the input features and the enhancement layer responses of the FLN. The output weights of the FLN are then estimated as a solution to a linear programming problem through pseudo-inverse. We first evaluate the performance of the DBFLN on three benchmark multi-category classification problems from the UCI machine learning repository, namely, the image segmentation problem, the vehicle classification problem and the glass identification problem. Performance study results on the benchmark classification problems show that DBFLN is an efficient classifier. We then use the DBFLN to classify the images in the TID2013 data set, based on their depth of distortions. The TID2013 data set comprises of 25 images, each with 5 levels of 24 distortion types. In all, the data set has 3000 images, which can be classified based on the depth of distortion. Thus, the IQA classification problem is defined as classifying the distorted images into one of the 5 classes (depending on the depth of distortion) using human visual image metrics as the input features. The performance of the DBFLN in classifying the image quality is compared with those of Support Vector Machines, Extreme Learning Machines, Random Vector Functional Link Network, and Deep Belief Network. Performance studies show the superior classification ability of th...
Chai, R., Tran, Y., Naik, G.R., Nguyen, T.N., Ling, S.H., Craig, A. & Nguyen, H.T. 2016, 'Classification of EEG-based Mental Fatigue using Principal Component Analysis and Bayesian Neural Network', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 (EMBC), 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, pp. 4654-4657.
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This paper presents an electroencephalography (EEG) based- classification of between pre- and post- mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76%. Also based on data from the 6 PCs, during eye closed, the classification between pre- and post- task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.
San, P.P., Ling, S.H. & Nguyen, H.T. 2016, 'Deep Learning Framework for Detection of Hypoglycemic Episodes in Children with Type 1 Diabetes', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 (EMBC), 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, pp. 3503-3506.
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Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia. The proposed DBN provides a superior classification performance with feature transformation on either processed or un-processed data. To illustrate the effectiveness of the proposed hypoglycemia detection system, 15 children with Type 1 diabetes were volunteered overnight. Comparing with several existing methodologies, the experimental results showed that the proposed DBN outperformed and achieved better classification performance.
San, P.P., Ling, S.H., Chai, R., Tran, Y., Craig, A. & Nguyen, H.T. 2016, 'EEG-based Driver Fatigue Detection using Hybrid Deep Generic Model', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 EMBC, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, USA, pp. 800-803.
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Classification of electroencephalography (EEG)- based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)- based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.
Gozasht, F., Boddupalli, A., Ling, S.S.H. & Mohan, A.S. 2016, 'Singly-fed shaped planar inverted-F antenna for circular polarization', AMS 2016 - 2016 2nd Australian Microwave Symposium, Conference Proceedings, Australian Microwave Symposium (AMS), IEEE, Adelaide, South Australia, Australia, pp. 33-34.
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© 2016 IEEE.We propose a singly-fed planar inverted-F antenna (PIFA), with double-shorting planes and a tapered patch for radiating circular polarization. FEKO® simulations are employed to optimize the geometry of the antenna. The antenna prototype resonates at 2.52GHz and has a measured impedance bandwidth of around 240MHz. The calculated 3dB axial ratio bandwidth falls within the impedance bandwidth, demonstrating satisfactory circular polarization characteristics.
Ling, S.S., Jabardi, M.H. & Fatlawi, A.A. 2016, 'An efficient diagnosis system for parkinson's disease using deep belief network', IEEE Congress on Evolutionary Computation, IEEE, Vancouver, Canada.
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Chai, R., Naik, G., Nguyen, T.N., Ling, S.H., Tran, Y. & Nguyen, H.T. 2016, 'Selecting Optimal Electroencephalography Channels for Mental Tasks Classification: An approach using ICA', 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE Congress on Evolutionary Computation, IEEE, Vancouver, Canada, pp. 1331-1335.
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This paper presents a systematic method to select optimal electroencephalography (EEG) channels for three mental tasks-based brain-computer interface (BCI) classification. A blind source separation (BSS) technique based on independent component analysis (ICA) with its back-projecting of the scalp map was used for selecting the optimal EEG channels. The three mental tasks included: mental letter composing, mental arithmetic and mental Rubik's cube rolling. Based on a power spectral density (PSD), the features of the two-channel EEG data were extracted, and then were classified by Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map showed that the prominent channels could be selected for dominant features from original six EEG channels (C3, C4, P3, P4, O1, O2) to four dominant channels (P3, O1, C4, O2) with the best two EEG channels selection at O1&C4. Two channel combinations classification yielded to the best two EEG channels of O1&C4 with an accuracy of 76.4%, followed by P3&O2 with an accuracy of 74.5%; P3&C4 with an accuracy of 71.9% and O1&O2 with an accuracy of 70%.
Al-Fatlawi, A.H., Jabardi, M.H. & Ling, S.H. 2016, 'Efficient diagnosis system for Parkinson's disease using deep belief network', Evolutionary Computation (CEC), 2016 IEEE Congress on, IEEE Congress on Evolutionary Computation, IEEE, Piscataway, USA, pp. 1324-1330.
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In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks' parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective method to diagnosis Parkinson's disease by using the speech signal.
Wong, G.Y., Leung, F.H.F. & Ling, S.S.H. 2016, 'Identification of protein-ligand binding site using multi-clustering and support vector machine', IECON Proceedings (Industrial Electronics Conference), 42nd Annual Conference of the IEEE Industrial Electronics Society, IECON, IEEE, Florence, Italy, pp. 939-944.
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© 2016 IEEE.Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%.
Chai, R., Naik, G., Tran, Y., Ling, S., Craig, A. & Nguyen, H.T. 2015, 'Classification of Driver Fatigue in an Electroencephalography-Based Countermeasure System with Source Separation Module', Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2015 (EMBC 2015), The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2015 (EMBC 2015), IEEE, Milano, Italy, pp. 514-517.
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An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).
Chai, R., Smith, M.R., Nguyen, T.N., Ling, S.H., Coutts, A.J. & Nguyen, H.T. 2015, 'Comparing Features Extractors in EEG-Based Cognitive Fatigue Detection of Demanding Computer Tasks', Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2015 (EMBC 2015), The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2015 (EMBC 2015), IEEE, Milano, Italy, pp. 7594-7597.
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An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value < 0.05).
Chai, R., Tran, Y.H., Craig, A., Ling, S.S. & Nguyen, H.T. 2014, 'Enhancing Accuracy of Mental Fatigue Classification using Advanced Computational Intelligence in an Electroencephalography System', The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Chicago, pp. 1338-1341.
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A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.
Ling, S.S. & Jiang, F. 2014, 'Application on Self-Provisioning of Communication Network Service using Fuzzy Particle Swarm Optimization', Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on, International Conference on Control Automation Robotics & Vision, IEEE, Singapore, pp. 1245-1250.
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In this paper, a self-provisioning of communication network service based on a fuzzy particle swarm optimization is proposed to minimize the configuration cost of four layer communication network. An swarm optimization called fuzzy particle swarm optimization (FPSO) is introduced. In this FPSO, the inertia weight of PSO is adaptively determined by a set of fuzzy rule. Also, a cross-mutated operation is presented to drive the solution to escape from local optima where the control parameter of this operation is also governed by a set of fuzzy rule. A performance comparison is given to show the performance of the proposed FPSO on the self-provisioning of communication network service and found that the performance of FPSO is significantly better than that of the existing hybrid PSO methods in a statistical sense.
Ling, S.H., San, P.P., Lam, H.K., Nguyen, H.T. & IEEE 2014, 'Non-invasive Detection of Hypoglycemic Episodes in Type1 Diabetes Using Intelligent Hybrid Rough Neural System', 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), pp. 1238-1242.
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Liu, C., Lam, H.K., Zhang, X., Li, H., Ling, S.H. & IEEE 2014, 'Relaxed Stability Conditions Based on Taylor Series Membership Functions for Polynomial Fuzzy-Model-Based Control Systems', 2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), pp. 2111-2118.
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Wong, G.Y., Leung, F.H.F., Ling, S.-.H. & IEEE 2014, 'An Under-sampling Method Based on Fuzzy Logic for Large Imbalanced Dataset', 2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), pp. 1248-1252.
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San, P.P., Ling, S.S., Soe, N.N. & Nguyen, H.T. 2014, 'A novel extreme learning machine for hypoglycemia detection', 36nd Annual International conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Chicago, pp. 302-305.
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Chai, R., Tran, Y., Craig, A., Ling, S.H. & Nguyen, H.T. 2014, 'Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system', 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, pp. 1318-1341.
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&copy; 2014 IEEE. A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.
Nguyen, L., Nguyen, V., Ling, S.S. & Nguyen, H.T. 2013, 'Analyzing EEG Signals under Insulin-induced Hypoglycemia in Type 1 Diabetes Patients', Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 35th Annual Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Osaka, Japan, pp. 1980-1983.
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Hypoglycemia is dangerous and considered as a limiting factor of the glycemic control therapy for patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is especially feared because early warning symptoms are unclear during sleep so an episode of hypoglycemia may lead to a fatal effect on patients. The main objective of this paper is to explore the correlation between hypoglycemia and electroencephalography (EEG) signals. To do this, the EEG of five T1DM adolescents from an overnight insulin-induced study is analyzed by spectral analysis to extract four different parameters. We aim to explore the response of these parameters during the clamp study which includes three main phases of normal, hypoglycemia and recovery. We also look at data at the blood glucose level (BGL) of 3.3-3.9 mmol/l to find a threshold to distinguish between non-hypoglycemia and hypoglycemia states. The results show that extracted EEG parameters are highly correlated with patients' conditions during the study. It is also shown that at the BGL of 3.3 mmol/l, responses to hypoglycemia in EEG signals start to significantly occur.
Chai, R., Ling, S.S., Hunter, G., Tran, Y.H. & Nguyen, H.T. 2013, 'Classification of wheelchair commands using brain computer interface: comparison between able-bodied persons and patients with tetraplegia', Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 35th Annual Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Osaka, Japan, pp. 989-992.
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This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubiks cube rolling forward; these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window.
Nguyen, L., Nguyen, V., Ling, S.S. & Nguyen, H.T. 2013, 'Combining Genetic Algorithm and Levenberg-Marquardt Algorithm in Training Neural Network for Hypoglycemia Detection using EEG Signals', Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 35th Annual Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Osaka, Japan, pp. 5386-5389.
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Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and Levenberg-Marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.
San, P., Ling, S.S. & Nguyen, H.T. 2013, 'Combinational neural logic system and its industrial application on hypoglycemia monitoring system', IEEE Conference on Industrial Electronics and Applications, ICIEA 2013, IEEE, Melbourne, Australia, pp. 947-952.
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In this paper, a combinational neural logic network (NLN) with the neural-Logic-AND, -OR and -NOT gates is applied on the development of non-invasive hypoglycemia monitoring system. It is an alarm system which measured physiological parameters of electrocardiogram (ECG) signal and determine the onset of hypoglycemia by use of proposed NLN. Due to different nature of application, conventional neural networks (NNs) with common structure may not always guarantee the optimal solution. Based on knowledge of application, the proposed NLN is designed systematically in order to incorporate the characteristics of application into the structure of proposed network. The parameter of the proposed NLN will be trained by hybrid particle swarm optimization with wavelet mutation (HPSOWM). The proposed NLN will be practically analyzed using real data sets collected from 15 children (569 data sets) with Type 1 diabetes at the Department of Health, Government of Western Australia. By using the proposed method, the detection performance is enhanced. Compared with other conventional NNs, the proposed NLN gives better performance in terms of sensitivity and specificity.
Wong, G.Y., Leung, F.H.F., Ling, S.-.H. & IEEE 2013, 'A Novel Evolutionary Preprocessing Method Based on Over-sampling and Under-sampling for Imbalanced Datasets', 39TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2013), pp. 2354-2359.
Chai, R., Hunter, G., Ling, S.S. & Nguyen, H.T. 2012, 'Real-Time Microcontroller based Brain Computer Interface for Mental Task Classifications using Wireless EEG Signals from Two Channels', Proceedings of the Ninth IASTED International Conference on Biomedical Engineering, The Ninth IASTED International Conference on Biomedical Engineering, ACTA Press, Innsbruck, Austria, pp. 336-342.
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A brain computer interface (BCI) using electroencephalography (EEG) to measure brain activities could provide severely disabled people with alternative means of control and communication. In a practical system, portability, low power and real-time operation are the keys requirements. This could be accomplished by using an embedded microcontroller based system. The main contribution of this paper shows the development of a real-time BCI prototype system to classify groups of mental tasks based on such a system. The relevant mental tasks used are mental arithmetic, figure rotation, letter composing, visual counting and eyes closed action. Moreover, the system uses a separate two channels only wireless EEG measurement module with the active positions at parietal and occipital lobes. The result shows the wireless EEG module has a good performance with a CMRR of more than 95dB. In addition, the size of the module is small (36x36 mm2) and current consumption is low enough to operate off a 3V coin cell battery. The mental tasks were classified using a feed-forward back-propagation artificial neural network (ANN) trained with the Levenberg-Marquardt algorithm. An accuracy of around 70% was achieved with bit rate at around 0.4 bits/trial for six subjects tested to select between three separate mental tasks.
San, P., Ling, S.S. & Nguyen, H.T. 2012, 'Intelligent detection of hypoglycemic episodes in children with Type 1 diabetes using adaptive neural-fuzzy inference system', Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 34th Annual Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, San Diego, California, USA, pp. 6325-6328.
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Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mel- litus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%.
Nguyen, L., Nguyen, V., Ling, S.S. & Nguyen, H.T. 2012, 'An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels', Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 34th Annual Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, San Diego, California, USA, pp. 3515-3518.
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Hypoglycemia is the most common but highly feared side effect of the insulin therapy for patients with Type 1 Diabetes Mellitus (T1DM). Severe episodes of hypoglycemia can lead to unconsciousness, coma, and even death. The variety of hypoglycemic symptoms arises from the activation of the autonomous central nervous system and from reduced cerebral glucose consumption. In this study, electroencephalography (EEG) signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected non-invasively using EEG signals from only two channels. This paper demonstrates that a significant advantage can be achieved by implementing adaptive training. By adapting the classifier to a previously unseen person, the classification results can be improved from 60% sensitivity and 54% specificity to 75% sensitivity and 67% specificity.
Chai, R., Ling, S.S., Hunter, G. & Nguyen, H.T. 2012, 'Toward Fewer EEG Channels and Better Feature Extractor of Non-Motor Imagery Mental Tasks Classification for a Wheelchair Thought Controller', Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), IEEE, San Diego, CA, USA, pp. 5266-5269.
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This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels.
Chai, R., Ling, S.S., Hunter, G. & Nguyen, H.T. 2012, 'Mental Task Classifications Using Prefrontal Cortex Electroencephalograph Signals', Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), IEEE, San Diego, CA, USA, pp. 1831-1834.
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For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%.
San, P., Ling, S.S. & Nguyen, H.T. 2012, 'Hybrid Particle Swarm Optimization Based Normalized Radial Basis Function Neural Network For Hypoglycemia Detection', International Joint Conference on Neural Networks, WCCI 2012 IEEE World Congress on Computational Intelligence, IEEE, Brisbane, Australia, pp. 2718-2723.
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In this study, a normalized radial basis function neural network (NRBFNN) is presented for detection of hypoglycemia episodes by using physiological parameters of electrocardiogram (ECG) signal. ypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. Based on heart rate (HR) and corrected QT interval (QTc) of electrocardiogram (ECG) signal, a hybrid particle swarm optimization based normalized RBFNN is developed for recognization of hypoglycemia episodes. A global learning algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used to optimize the parameters of NRBFNN. From a clinical study of 15 children with Type 1 diabetes, natural occurrence of nocturnal hypoglycemic episodes associated with increased heart rates and corrected QT interval are studied. The overall data are organized into a training set (5 patients), validation set (5 patients) and testing set (5 patients) randomly selected. Using the optimized NRBFNN, the testing performance for detection of hypoglycemic episodes are satisfactory with 76.74% of sensitivity and 51.82% of specificity.
Jiang, F., Ling, S.S., Chan, K.Y., Chaczko, Z.C., Leung, F.H. & Frater, M. 2012, 'An immunology-inspired host-based multi-engine anomaly detection system with hybrid particle swarm optimisations', IEEE International Conference on Fuzzy Systems, IEEE, Australia, pp. 1279-1286.
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In this paper, multiple detection engines with multilayered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunologybased self/nonself discrimination. The classications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and signicantly reduce the false positive rate. The proposed HPSOWM works especially well for the efcient classication of unknown behaviors and malicious attacks.
Chai, R., Ling, S.S., Hunter, G. & Nguyen, H.T. 2012, 'Mental non-motor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network', Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN 2012), 2012 IEEE World Congress on Computational Intelligence (WCCI 2012) / 2012 International Joint Conference on Neural Networks (IJCNN 2012), IEEE, Brisbane, Australia, pp. 978-984.
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A genetic algorithm (GA)-based neural network classification in the application of brain computer interface (BCI) for controlling a wheelchair is presented in this paper. This study uses an electroencephalography (EEG) as a non-invasive BCI approach to discriminate three non-motor imagery mental tasks for disabled individuals who may have difficulty in using BCI based motor imagery tasks. The three tasks classification is mapped into three wheelchair movements: left, right and forward and the relevant combination mental tasks used in this study are mental arithmetic, letter composing, Rubik's cube rolling, visual counting, ringtone imagery and spatial navigation. The results show the proposed system provides good classification performance after selecting the most effective of three discriminative tasks across combination of the different non-motor imagery mental tasks for the five subjects tested. The average classification accuracy is between 76% and 85 %, with information transfer rates varies from 0.5 to 0.8 bits per trial.
San, P., Ling, S.S. & Nguyen, H.T. 2012, 'Optimized variable translation wavelet neural network and its application on hypoglycemia detection system', 7th IEEE Conference on Industrial Electronics and Applications, 7th IEEE Conference on Industrial Electronics and Applications, IEEE, Singapore, pp. 547-551.
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An hybrid particle swarm optimization based optimized variable translation wavelet neural network (VTWNN) is proposed for detection of hypoglycemic episodes in patients with Type 1 diabetes mellitus (T1DM). Due to excellent performance in capturing nonstationary signal and nonlinear function modeling of VTWNN, it is used as a suitable classifier in hypoglycemia detection system. A global training algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) operation is investigated for parameters optimization of proposed VTWNN detection system. In this clinical study, 15 children with Type 1 diabetes were observed overnight. All the real data sets collected from Department of Heath, Government of Western Australia. Several experiments are performed over a randomly selected training set 5 patients (184 data points), validation set 5 patients (192 data points) and testing set 5 patients (153 data points) respectively. Using variable translation wavelet neural network (VTWNN), the value of testing sensitivity and specificity are 79.07 % and 50.00 %. The results show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity.
Ling, S.S., Nguyen, H.T., Leung, F.H., Chan, K.Y. & Jiang, F. 2012, 'Intelligent fuzzy particle swarm optimization with cross-mutated operation', IEEE Congress on Evolutionary Computation, IEEE Congress on Evolutionary Computation (CEC), IEEE, Australia, pp. 3009-3016.
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This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCM method performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods
Nguyen, L., Nguyen, V., Ling, S.S. & Nguyen, H.T. 2012, 'A particle swarm optimization-based neural network for detecting nocturnal hypoglycemia using electroencephalograph (EEG) signals', 2012 IEEE World Congress on Computational Intelligence, IEEE, Brisbane, Australia, pp. 2730-2735.
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For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia or the state of low blood glucose level is a very common but dangerous complication. Hypoglycemia episodes can lead to a large number of serious symptoms and effects, including unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. By analyzing electroencephalography (EEG) signals from five T1DM patients during an overnight study, we find that under hypoglycemia, both centroid theta frequency and centroid alpha frequency change significantly against non-hypoglycemia conditions. Furthermore, a neural network is developed to detect hypoglycemia using the mentioned two EEG features. A standard particle swarm optimization strategy is applied to optimize the parameters of this neural network. By using the proposed method, we obtain the classification performance of 82% sensitivity and 63% specificity. The results demonstrate that hypoglycemia episodes can be detected non-invasively and effectively from EEG signals.
Ling, S.S., Nuryani, N. & Nguyen, H.T. 2012, 'Hybrid Particle Swarm - based Fuzzy Support Vector Machine for Hypoglycaemia Detection', IEEE International Conference on Fuzzy Systems, IEEE, Australia, pp. 450-455.
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Severe hypoglycemia is potentially life-threatening. This article introduces a novel hypoglycemia detection strategy using a hybrid particle swarm - based fuzzy support vector machine (SFisSvm) technique. The inputs of this system are six electrocardiographic (ECG) parameters. The system parameters of SFisSvm are optimized using a particle swarm optimization method. The proposed hypoglycemia detector system is a combination of two subsystems, namely, fuzzy inference system (FIS) and support vector machine (SVM). Two most significant inputs, heart rate and RTpc are fed to FIS, and its output is used for input of the SVM. The other ECG parameters and the output of FIS are fed to SVM and, then, are classified to indicate the presence of hypoglycemia. In this study, three and five membership functions are investigated for FIS. Furthermore, radial basis function (RBF), sigmoid and linear kernel functions are employed for mapping the inputs to high dimensional space in SVM. Performances of SFisSvm with different kernel functions are compared. As conclusion, the performance of SFisSvm is found with 75.19%, 83.71% and 79.33% in terms of sensitivity, specificity and geometric mean.
Chan, K.Y., Ling, S.S., Nguyen, H.T. & Jiang, F. 2012, 'A hypoglycemic episodes diagnosis system based on neural networks for Type 1 diabetes mellitus', IEEE Congress on Evolutionary Computation, CEC 2012, IEEE, Australia, pp. 2046-2051.
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Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients blood glucose levels based on these patients physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated.
Wong, G., Leung, F.H. & Ling, S.S. 2012, 'Predicting protein-ligand binding site with differential and support vector machine', International Joint Conference on Neural Networks, IEEE, Australia, pp. 2724-2729.
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Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. When the scores are calculated from these methods, the method of classification is very important and can affect the prediction results greatly. A developed support vector machine (SVM) is used to classify the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential, offset from protein, conservation score and the information around the pockets. Since SVM is sensitive to the input parameters and the positive samples are more relevant than negative samples, differential evolution (DE) is applied to find out the suitable parameters for SVM. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.
San, P.P., Ling, S.H. & Nguyen, H.T. 2012, 'Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 6325-6328.
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Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. &copy; 2012 IEEE.
Chai, R., Ling, S.H., Hunter, G.P. & Nguyen, H.T. 2012, 'Mental task classifications using prefrontal cortex electroencephalograph signals', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 1831-1834.
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For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%. &copy; 2012 IEEE.
Chai, R., Ling, S.H., Hunter, G.P. & Nguyen, H.T. 2012, 'Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 5266-5269.
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This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels. &copy; 2012 IEEE.
San, P., Ling, S.S. & Nguyen, H.T. 2011, 'Block-based neural network for hypoglycemia detection', 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Boston, Massachusetts, USA, pp. 5666-5669.
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In this paper, evolvable block based neural network (BBNN) is presented for detection of hypoglycemia episodes. The structure of BBNN consists of a two-dimensional (2D) array of fundamental blocks with four variable inputoutput nodes and weight connections. Depending on the structure settings, each block can have one of four different internal configurations. To provide early detection of hypoglycemia episodes, the physiological parameters such as heart rate (HR) and corrected QT interval (QTc) of electrocardiogram (ECG) signal are used as the inputs of BBNN. The overall structure and weights of BBNN are optimized by an evolutionary algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM). The optimized structures and weights of BBNN are capable to compensate large variations of ECG patterns caused by individual and temporal difference since a fixed structure classifiers are easy to fail to trace ECG signals with large variations. The ECG data of 15 patients are organized into a training set, a testing set and a validation set, each of which has randomly selected 5 patients. The simulation results shows that the proposed algorithm, BBNN with HPSOWM can successfully detect the hypoglycemic episodes in T1DM in term of testing sensitivity (76.74%) and test specificity (50.91%).
Nuryani, N., Ling, S.S. & Nguyen, H.T. 2011, 'Ventricular Repolarization Variability for Hypoglycemia Detection', 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Boston, Massachusetts, USA, pp. 7961-7964.
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Hypoglycemia is the most acute and common complication of Type 1 diabetes and is a limiting factor in a glycemic management of diabetes. In this paper, two main contributions are presented; firstly, ventricular repolarization variabilities are introduced for hypoglycemia detection, and secondly, a swarm-based support vector machine (SVM) algorithm with the inputs of the repolarization variabilities is developed to detect hypoglycemia. By using the algorithm and including several repolarization variabilities as inputs, the best hypoglycemia detection performance is found with sensitivity and specificity of 82.14% and 60.19%, respectively.
Jiang, F., Ling, S.S. & Agbinya, J.I. 2011, 'A nature inspired anomaly detection system using multiple detection engines', 2011 6th International Conference on Broadband and Biomedical Communications (IB2Com), International Conference on Broadband and Biomedical Communications, IEEE, Melbourne, Australia, pp. 200-205.
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The rapid growth of computer networks presents challenges to the single detection engine based system, which has been insufficient in meeting end-users' requirements in the large-scale distributed complex network. In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed. The principle is to coordinate the results from each single-engine intrusion alert system, by seamlessly integrating with the multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviour of system calls are further examined by an advanced fuzzy-based inference process called HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and reduce the false positive rate significantly. The proposed HPSOWM especially works for the efficient classification of unknown behaviors and malicious attacks.
Ling, S.S., Jiang, F., Chan, K.Y. & Nguyen, H.T. 2011, 'Permutation flow shop scheduling: fuzzy particle swarm optimization approach', IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference On Fuzzy Systems, IEEE, Taipei, Taiwam, pp. 572-578.
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AbstractA fuzzy particle swarm optimization (PSO) for the minimization of makespan in permutation flow shop scheduling problem is presented in this paper. In the proposed fuzzy PSO, the inertia weight of PSO and the control parameter of the crossmutated operation are determined by a set of fuzzy rules. To escape the local optimum, cross-mutated operation is introduced. In order to make PSO suitable for solving permutation flow shop scheduling problem, a roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a swap-based local search for scheduling problem is designed for the local exploration on a discrete job permutation space. Flow shop benchmark functions are employed to evaluate the performance of the fuzzy PSO for flow shop scheduling problems and the results indicate that the algorithm performs better compared with existing hybrid PSO algorithms.
Jiang, F., Ling, S.S. & Frater, M. 2011, 'A Distributed Smart Routing Scheme for Terrestrial Sensor Networks with Hybrid Neural Rough Sets', Proceedings of IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference on Fuzzy Systems, IEEE, Taipei, Taiwan, pp. 2238-2244.
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Abstract The limited power consumption, as a major constraint, presents challenges in improving the network throughput for Wireless Sensor Networks (WSNs). Due to the limited computational power, the applications of WSNs in Terrestrial Networks require the capability to pre-process the observation data so as to remove irrelevant features or factors from multi-dimensional dataset. This paper proposes a intelligent distributed energy efficient routing algorithm inspired from natural learning and adaptation process with the aid of hybrid Neural Rough Sets theory, which is used to efficiently reduce the dimensionality of input dataset. The algorithmic implementation and experimental validation are described in this paper. Details of the algorithm and its testing procedures are presented in comparison with the other power-aware protocols, e.g., mini-hop. The validation of the proposed model is carried out via a wireless sensor network test-bed implemented in Castalia Simulator. The experimental results show the network performance measurements such as delay, throughput and packet loss that have been greatly improved as the outcome of applying this integration with Neural Rough Sets.
Ling, S.S., Nguyen, H.T. & Leung, F.H. 2011, 'Hypoglycemia detection using fuzzy inference system with genetic algorithm', IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference on Fuzzy Systems, IEEE, Taipei, Taiwan, pp. 2225-2231.
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AbstractIn this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the telectrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.
Chan, K.Y., Dillon, T., Ling, S.S. & Kwong, C. 2011, 'Determination of process conditions of epoxy dispensing processes using a genetic algorithm based neural fuzzy networks', IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference on Fuzzy Systems June 27-30, 2011, Taipei, Taiwan, IEEE, Taipei, Taiwan, pp. 2253-2260.
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In this paper, process conditions of epoxy dispensing processes are determined by the proposed genetic algorithm based neural fuzzy networks, which consists of two tasks: a) the approach of neural fuzzy networks, which was shown to be better than the other existing approaches, is proposed to develop models in relating between process parameters and quality characteristics for the epoxy dispensing processes; b) the approach of genetic algorithm is used to determine process parameters with respect to pre-defined quality requirements based on the developed neural fuzzy network models. The results indicate that, based on the proposed genetic algorithm based neural fuzzy network, estimated process parameters can achieve specified requirements of microchip encapsulations with high and robust qualities.
Chan, K.Y., Dillon, T., Ling, S.S. & Kwong, C. 2011, 'Manufacturing modeling using an evolutionary fuzzy regression', IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference on Fuzzy Systems June 27-30, 2011, Taipei, Taiwan, IEEE, Taipei, Taiwan, pp. 2261-2267.
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Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and onlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out.
Lai, J.C., Leung, F.H., Ling, S.S. & Shi, E.C. 2011, 'Economic load dispatch using intelligent optimization with fuzzy control', IEEE International Conference on Fuzzy Systems 2011, IEEE International Conference on Fuzzy Systems, IEEE, Taipei, Taiwan, pp. 2219-2224.
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In this paper, Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm is proposed to tackle the economic load dispatch problem. To provide the self-terminating ability, a technique called Iteration Windows (IW) is introduced to govern the number of iteration in each searching stage during the optimization. The size of IW is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the IW till the end of the searching process. A wavelet based mutation process is embedded in the DE searching process to enhance the searching performance. The weight F of DE is also controlled by the fuzzy controller to further speed up the searching process. The proposed method is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELDVPL) Problem. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration and performs significantly better than the conventional methods in terms of convergence speed and solution quality.
Nguyen, L., Ling, S.S., Jones, T.W. & Nguyen, H.T. 2011, 'Identification of hypoglycemic states for patients with T1DM using various parameters derived from EEG signals', 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Boston, Massachusetts, USA, pp. 2760-2763.
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For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia is a very common but dangerous complication which can lead to unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. In this study, we explore the connection between hypoglycemic episodes and the electrical activity of neurons within the brain or electroencephalogram (EEG) signals. By analyzing EEG signals from a clinical study of five children with T1DM, associated with hypoglycemia at night, we find that some EEG parameters change significantly under hypoglycemia condition. Based on these parameters, a method of detecting hypoglycemic episodes using EEG signals with a feed-forward multi-layer neural network is proposed. In our application, the classification results are 72% sensitivity and 55% specificity when the EEG signals are acquired from 2 electrodes C3 and O2. Furthermore, signals from different channels are also analyzed to observe the contributions of each channel to the performance of hypoglycemia classification.
Nguyen, L.B., Ling, S.S.H., Jones, T.W. & Nguyen, H.T. 2011, 'Identification of hypoglycemic states for patients with T1DM using various parameters derived from EEG signals', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 2760-2763.
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For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia is a very common but dangerous complication which can lead to unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. In this study, we explore the connection between hypoglycemic episodes and the electrical activity of neurons within the brain or electroencephalogram (EEG) signals. By analyzing EEG signals from a clinical study of five children with T1DM, associated with hypoglycemia at night, we find that some EEG parameters change significantly under hypoglycemia condition. Based on these parameters, a method of detecting hypoglycemic episodes using EEG signals with a feed-forward multi-layer neural network is proposed. In our application, the classification results are 72% sensitivity and 55% specificity when the EEG signals are acquired from 2 electrodes C3 and O2. Furthermore, signals from different channels are also analyzed to observe the contributions of each channel to the performance of hypoglycemia classification. &copy; 2011 IEEE.
San, P.P., Ling, S.H. & Nguyen, H.T. 2011, 'Block based neural network for hypoglycemia detection', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 5666-5669.
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In this paper, evolvable block based neural network (BBNN) is presented for detection of hypoglycemia episodes. The structure of BBNN consists of a two-dimensional (2D) array of fundamental blocks with four variable input-output nodes and weight connections. Depending on the structure settings, each block can have one of four different internal configurations. To provide early detection of hypoglycemia episodes, the physiological parameters such as heart rate (HR) and corrected QT interval (QTc) of electrocardiogram (ECG) signal are used as the inputs of BBNN. The overall structure and weights of BBNN are optimized by an evolutionary algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM). The optimized structures and weights of BBNN are capable to compensate large variations of ECG patterns caused by individual and temporal difference since a fixed structure classifiers are easy to fail to trace ECG signals with large variations. The ECG data of 15 patients are organized into a training set, a testing set and a validation set, each of which has randomly selected 5 patients. The simulation results shows that the proposed algorithm, BBNN with HPSOWM can successfully detect the hypoglycemic episodes in T1DM in term of testing sensitivity (76.74%) and test specificity (50.91%). &copy; 2011 IEEE.
Nuryani, N., Ling, S. & Nguyen, H.T. 2011, 'Ventricular repolarization variability for hypoglycemia detection', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 7961-7964.
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Hypoglycemia is the most acute and common complication of Type 1 diabetes and is a limiting factor in a glycemic management of diabetes. In this paper, two main contributions are presented; firstly, ventricular repolarization variabilities are introduced for hypoglycemia detection, and secondly, a swarm-based support vector machine (SVM) algorithm with the inputs of the repolarization variabilities is developed to detect hypoglycemia. By using the algorithm and including several repolarization variabilities as inputs, the best hypoglycemia detection performance is found with sensitivity and specificity of 82.14% and 60.19%, respectively. &copy; 2011 IEEE.
Chan, K.Y., Dillon, T.S., Kwong, C.K., Ling, S.H. & IEEE 2011, 'Using Genetic Programming for Developing Relationship between Engineering Characteristics and Customer Requirements in New Products', 2011 6TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), pp. 526-531.
Lai, J.C., Leung, F.H. & Ling, S.S. 2010, 'A new differential evolution with self-terminating ability using fuzzy control and k-nearest neighbors', IEEE Congress on Evolutionary Computation (CEC) - 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, pp. 503-510.
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A new Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm to determine the terminating condition is proposed. A technique called Iteration Windows is introduced to govern the number of iteration in each searching stage. The size of the iteration windows is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the iteration windows until the end of the searching process. The wavelet based mutation process is embedded in the DE searching process to enhance the searching performance of DE. The F weight of DE is also controlled by the fuzzy controller to further speed up the searching process. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration.
Lai, J.C., Leung, F.H. & Ling, S.S. 2010, 'Economic Load Dispatch using Differential Evolution with Double Wavelet Mutation Operations', IEEE Congress on Evolutionary Computation (CEC) - 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, pp. 2013-2018.
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In this paper, a modified Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to handle a load flow problem. The wavelet based operation is embedded in the DE mutation and crossover operation. In the DE mutation operation, the scaling factor is controlled by a wavelet function. In the DE crossover operation, a wavelet-based mutation operation is embedded in it. The trial population vectors are thus modified by the wavelet function. The double wavelet mutations are applied in order to enhance DE in exploring the high-dimension solution space more effectively for better solution quality and stability. The proposed DE algorithm is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) Problem. It is shown empirically that the proposed method out-performs significantly the conventional methods in terms of convergence speed, solution quality and solution stability.
Ling, S.S., Nguyen, H.T. & Chan, K.Y. 2010, 'Genetic algorithm based fuzzy multiple regression for the nocturnal hypoglycemic classification', IEEE Congress on Evolutionary Computation (CEC) - 2010 IEEE WORLD Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, pp. 2659-2664.
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Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities.
Chan, K.Y., Zhu, H.L., Lau, C., Dillon, T.S. & Ling, S.S. 2010, 'Determination of chemo-responses for osteosarcoma using a hybrid evolutionary algorithm', IEEE Congress on Evolutionary Computation (CEC) - 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, pp. 1865-1869.
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In this paper, a hybrid evolutionary algorithm (HEA) based on the approaches of the evolutionary algorithm and a local search (LS) is proposed to determine the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is one of the most common malignant bone tumor in children. The HEA consists of a population of individuals but the evolution of individuals is conducted by a LS, rather than the crossover and mutation used in the traditional evolutionary algorithms. The proposed HEA can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. Experimental results indicate that HEA can obtain more accurate signatures than the other existing approaches in determining chemoresponse for osteosarcoma.
Ling, S.S., Nuryani, N. & Nguyen, H.T. 2010, 'Evolved Fuzzy Reasoning Model for Hypoglycaemic Detection', Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 'Merging Medical Humanism and Technology', IEEE Engineering in Medicine and Biology Society Annual Conference, Piscataway, USA, Buenos Aires, Argentina, pp. 4662-4665.
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Hypoglycaemia is a serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal) continuously to provide early detection of hypoglycemic episodes in Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, an evolved fuzzy reasoning model (FRM) to recognize the presence of hypoglycaemic episodes is developed. To optimize the fuzzy rules and the fuzzy membership functions of FRM, an evolutionary algorithm called hybrid particle swarm optimization with wavelet mutation operation is investigated. All data sets are collected from Department of Health, Government of Western Australia for a clinical study. The results show that the proposed algorithm performs well in terms of the clinical sensitivity and specificity.
Nuryani, N., Ling, S.S. & Nguyen, H.T. 2010, 'Electrocardiographic T-wave Peak-to-end Interval for Hypoglycaemia Detection', Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Merging Medical Humanism and Technology, IEEE Engineering in Medicine and Biology Society Annual Conference, The Printing House, Inc., Buenos Aires, Argentina, pp. 618-621.
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Electrocardiographic T wave peak-to-end interval (TpTe) is one parameter of T wave morphology, which contains indicators for hypoglycaemia. This paper shows the corrected TpTe (TpTec) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in this paper. By comparing with the classification systems using inputs of corrected QT interval (QTc) and heart rate only, the results indicate that the inclusion of TpTec in combination with QTc and heart rate performs better in the detection of hypoglycaemia in terms of sensitivity, specificity and accuracy.
Ling, S.S., Nuryani, N. & Nguyen, H.T. 2010, 'Hypoglycaemia detection for Type 1 diabetic patients based on ECG parameters using fuzzy support vector machine', International Joint Conference on Neural Networks - 2010 IEEE World Congress on Computational Intelligence, International Joint Conference on Neural Networks, IEEE, Spain, pp. 2253-2259.
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Nocturnal hypoglycaemia in type 1 diabetic patients can be dangerous in which symptoms may not be apparent while blood glucose level decreases to very low level, and for this reason, an effective detection system for hypoglycaemia is crucial. This research work proposes a detection system for the hypoglycaemia based on the classification of electrocardiographic (ECG) parameters. The classification uses a Fuzzy Support Vector Machine (FSVM) with inputs of heart rate, corrected QT (QT c) interval and corrected TpTe (TpTec) interval. Three types of kernel functions (radial basis function (RBF), exponential radial basis function (ERBF) and polynomial function) are investigated in the classification. Moreover, parameters of the kernel functions are tuned to find the optimum of the classification. The results show that the FSVM classification using RBF kernel function demonstrates better performance than using SVM. However, both classifiers result approximately same performance if ERBF and polynomial kernel functions are used.
Chan, K.Y., Ling, S.S., Dillon, T.S. & Nguyen, H.T. 2010, 'Classification of Hypoglycemic Episodes for Type 1 Diabetes Mellitus based on Neural Networks', IEEE Congress on Evolutionary Computation (CEC) - 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, pp. 1444-1448.
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Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming.
Ling, S.H., Nuryani & Nguyen, H.T. 2010, 'Evolved fuzzy reasoning model for hypoglycaemic detection', 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, pp. 4662-4665.
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Hypoglycaemia is a serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal) continuously to provide early detection of hypoglycemic episodes in Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, an evolved fuzzy reasoning model (FRM) to recognize the presence of hypoglycaemic episodes is developed. To optimize the fuzzy rules and the fuzzy membership functions of FRM, an evolutionary algorithm called hybrid particle swarm optimization with wavelet mutation operation is investigated. All data sets are collected from Department of Health, Government of Western Australia for a clinical study. The results show that the proposed algorithm performs well in terms of the clinical sensitivity and specificity. &copy; 2010 IEEE.
Nuryani, Ling, S. & Nguyen, H.T. 2010, 'Electrocardiographic T-wave peak-to-end interval for hypoglycaemia detection', 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, pp. 618-621.
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Electrocardiographic T wave peak-to-end interval (TpTe) is one parameter of T wave morphology, which contains indicators for hypoglycaemia. This paper shows the corrected TpTe (TpTec) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in this paper. By comparing with the classification systems using inputs of corrected QT interval (QTc) and heart rate only, the results indicate that the inclusion of TpTec in combination with QTc and heart rate performs better in the detection of hypoglycaemia in terms of sensitivity, specificity and accuracy. &copy; 2010 IEEE.
Ling, S.S., Nguyen, H.T. & Chan, K.Y. 2009, 'A New Particle Swarm Optimization Algorithm for Neural Network Optimization', 2nd International Workshop on Data Mining and Artificial Intelligence (DMAI 2009), IEEE International Conference on Network and System Security, International Conference on network and System Security, IEEE, Gold Coast, Australia, pp. 516-521.
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This paper presents a new particle swarm optimization (PSO) algorithm for tuning parameters (weights) of neural networks. The new PSO algorithm is called fuzzy logic-based particle swarm optimization with cross-mutated operation (FPSOCM), where the fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the value of the inertia weight becomes variable. The cross-mutated operation is effectively force the solution to escape the local optimum. Tuning parameters (weights) of neural networks is presented using the FPSOCM. Numerical example of neural network is given to illustrate that the performance of the FPSOCM is good for tuning the parameters (weights) of neural networks.
Chan, K.Y., Yiu, K.C., Low, S.Y., Nordholm, S. & Ling, S.S. 2009, 'Speech recognition enhancement using beamforming and a genetic algorithm', 2nd International Workshop on Data Mining and Artificial Intelligence (DMAI 2009), IEEE International Conference on Network and System Security, International Conference on network and System Security, IEEE, Gold Coast, Australia, pp. 510-515.
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This paper proposes a genetic algorithm (GA) based beamformer to optimize speech recognition accuracy for a pretrained speech recognizer. The proposed beamformer is designed to tackle the non-differentiable and non-linear natures of speech recognition by employing the GA algorithm to search for the optimal beamformer weights. Specifically, a population of beamformer weights is reproduced by crossover and mutation until the optimal beamformer weights are obtained. Results show that the speech recognition accuracies can be greatly improved even in noisy environments.
Yeung, C.W., Leung, F.H., Chan, K.Y. & Ling, S.S. 2009, 'An integrated approach of particle swarm optimization and support vector machine for gene signature selection and cancer prediction', International Joint Conference on Neural Networks 2009, IEEE International Joint Conference on Neural Networks, IEEE, Atlanta, USA, pp. 3450-3456.
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To improve cancer diagnosis and drug development, the classification of tumor types based on genomic information is important. As DNA microarray studies produce a large amount of data, expression data are highly redundant and noisy, and most genes are believed to be uninformative with respect to the studied classes. Only a fraction of genes may present distinct profiles for different classes of samples. Classification tools to deal with these issues are thus important. These tools should learn to robustly identify a subset of informative genes embedded in a large dataset that is contaminated with high dimensional noises. In this paper, an integrated approach of support vector machine (SVM) and particle swarm optimization (PSO) is proposed for this purpose. The proposed approach can simultaneously optimize the selection of feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied to search the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients. Crossvalidation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one out of fourteen patient samples, suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma.
Lai, J.C.Y., Leung, F.H.F., Ling, S.H. & IEEE 2009, 'A New Differential Evolution with Wavelet Theory Based Mutation Operation', 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5, pp. 1116-1122.
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Ling, S.H., Lau, S.C., Luo, M., Ge, S.S. & IEEE 2009, 'Dynamic Resource Allocation with Machine Degradation using Decentralized Multi-Agent Approach', 2009 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, VOLS 1-3, pp. 1169-1174.
Chan, K.Y., Zhu, H.L., Lau, C.C., Ling, S.H. & IEEE 2008, 'Gene Signature Selection for Cancer Prediction Using an Integrated Approach of Genetic Algorithm and Support Vector Machine', 2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, pp. 217-224.
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Yeung, C.W., Ling, S.H., Chan, Y.H., Leung, F.H.F. & IEEE 2008, 'Restoration of Half-toned Color-Quantized Images using Particle Swarm Optimization with Wavelet Mutation', 2008 IEEE REGION 10 CONFERENCE: TENCON 2008, VOLS 1-4, pp. 1844-1849.
Ling, S.H., Iu, H.H.C., Leung, F.H.F., Chan, K.Y. & IEEE 2008, 'Modelling the Development of Fluid Dispensing for Electronic Packaging: Hybrid Particle Swarm Optimization Based-Wavelet Neural Network Approach', 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8, pp. 98-103.
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Iu, H.H.C., Ling, S.H., Lu, D.D.C. & IEEE 2007, 'Comparative study of bifurcation boundry in parallel-connected buck converters under democratic current-sharing control', 2007 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING, VOLS 1-2, pp. 359-363.
Tan, J., lu, H.H.C., Ling, S.H. & IEEE 2007, 'Symbolic representation of border collision bifurcation in switching DC/DC converters', IECON 2007: 33RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-3, CONFERENCE PROCEEDINGS, pp. 2010-2014.
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Lam, H.K., Ling, S.H., Iu, H.H.C., Yeung, C.W., Leung, F.H.F. & IEEE 2007, 'Control of nonlinear systems with a linear state-feedback controller and a modified neural network tuned by genetic algorithm', 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, pp. 1614-1619.
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Chan, K.Y., Ling, S.H., Chan, K.W., Iu, H.H.C., Pong, G.T.Y. & IEEE 2007, 'Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA', 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, pp. 2901-2908.
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Ling, S.H., Iu, H.H.C., Chan, K.Y., Ki, S.K. & IEEE 2007, 'Economic load dispatch: A new hybrid particle swarm optimization approach', 2007 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING, VOLS 1-2, pp. 225-231.
Chan, K.Y., Ling, S.H., Iu, H.H.C., Kwong, C.K. & IEEE 2007, 'A GA-based data mining approach to process improvement of fluid dispensing for electronic packaging', 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, pp. 4350-4357.
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Ling, S.H., Yeung, C.W., Chan, K.Y., Iu, H.H.C., Leung, F.H.F. & IEEE 2007, 'A new hybrid particle swarm optimization with wavelet theory based mutation operation', 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, pp. 1977-1984.
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Ling, S.H., Leung, F.H.F., Lam, H.K. & IEEE 2006, 'A variable node-to-node-link neural network and its application to hand-written recognition', 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, pp. 921-928.
Ling, S.H., Lam, H.K., Leung, F.H.F. & IEEE 2005, 'A variable-parameter neural network trained by improved genetic algorithm and its application', Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, pp. 1343-1348.
Ling, S.H., Leung, F.H.F. & IEEE 2005, 'Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning', Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, pp. 1325-1330.
Ling, S.H., Leung, F.H.F. & IEEE 2005, 'Genetic algorithm-based variable translation wavelet neural network and its application', Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, pp. 1365-1370.
Ling, S.H., Leung, F.H.F., Lam, H.K. & IEEE 2004, 'Genetic Algorithm Based Variable-Structure Neural Network and its Industrial Application', IECON 2004: 30TH ANNUAL CONFERENCE OF IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOL 2, pp. 1273-1278.
Ling, S.H., Lam, H.K., Leung, F.H.F., Lee, Y.S. & IEEE 2003, 'Genetic algorithm based neural-tuned neural network', IECON'03: THE 29TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1 - 3, PROCEEDINGS, pp. 2423-2428.
Ling, S.H., Lam, H.K., Leung, F.H.F., Lee, Y.S. & IEEE 2003, 'A genetic algorithm based variable structure Neural Network', IECON'03: THE 29TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1 - 3, PROCEEDINGS, pp. 436-441.
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Lam, H.K., Ling, S.H., Leung, F.H.F., Tam, P.K.S. & Lee, Y. 2003, 'Gain estimation for an AC power line data network transmitter using a neural-fuzzy network and an improved genetic algorithm', PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, pp. 167-172.
Ling, S.H., Lam, H.K., Leung, F.H.F. & Lee, Y.S. 2003, 'A genetic algorithm based fuzzy-tuned neural network', PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, pp. 220-225.
Ling, S.H., Lam, H.K., Leung, F.H.F., Lee, Y.S. & IEEE 2003, 'Improved genetic algorithm for economic load dispatch with valve-point loadings', IECON'03: THE 29TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1 - 3, PROCEEDINGS, pp. 442-447.
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Lam, H.K., Ling, S.H., Leung, F.H.F., Tam, P.K.S., Lee, Y.S., IEEE & IEEE 2002, 'Playing Tic-tac-toe using a modified neural network and an improved genetic algorithm', IECON-2002: PROCEEDINGS OF THE 2002 28TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, pp. 1984-1989.
Ling, S.H., Lam, H.K., Leung, F.H.F., Tam, P.K.S., IEEE & IEEE 2002, 'A novel GA-based neural network for short-term load forecasting', PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, pp. 2761-2766.
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Ling, S.H., Leung, F.H.F., Lam, H.K., Tam, P.K.S., IEEE & IEEE 2002, 'Short-term daily load forecasting in an intelligent home with GA-based neural network', PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, pp. 997-1001.
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Ling, S.H., Lam, H.K., Leung, F.H.F., Tam, P.K.S., IEEE & IEEE 2002, 'Learning of neural network parameters using a fuzzy genetic algorithm', CEC'02: PROCEEDINGS OF THE 2002 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, pp. 1928-1933.
Lam, H.K., Ling, S.H., Leung, F.H.F., Tam, P.K.S., Lee, Y.S., IEEE & IEEE 2002, 'Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm', IECON-2002: PROCEEDINGS OF THE 2002 28TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, pp. 1926-1929.
Lam, H.K., Leung, K.F., Ling, S.H., Leung, F.H.F., Tam, P.K.S., IEEE & IEEE 2002, 'On interpretation of graffiti digits and commands for eBooks: Neural fuzzy network and genetic algorithm approach', PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2, pp. 443-448.
Lam, H.K., Ling, S.H., Leung, K.F., Leung, F.H.F., IEEE, IEEE & IEEE 2001, 'On interpretation of graffiti commands for eBooks using a neural network and an improved genetic algorithm', 10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, pp. 1464-1467.
Lam, H.K., Ling, S.H., Leung, F.H.F., Tam, P.K.S., IEEE, IEEE, IEEE, IEEE & IEEE 2001, 'Tuning of the structure and parameters of neural network using an improved genetic algorithm', IECON'01: 27TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-3, pp. 25-30.
Lam, H.K., Ling, S.H., Leung, F.H.F., Tam, P.K.S., IEEE, IEEE & IEEE 2001, 'Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithm', 10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, pp. 908-911.
Ling, S.H., Leung, F.H.F., Tam, P.K.S., IEEE, IEEE & IEEE 2001, 'Daily load forecasting with a fuzzy-input-neural network in an intelligent home', 10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, pp. 449-452.
Ling, S.H., Lam, H.K., Leung, F.H.F., Tam, P.K.S., IEEE, IEEE & IEEE 2001, 'A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home', 10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, pp. 1456-1459.

Journal articles

Ling, S.H., San, P.P., Lam, H.K. & Nguyen, H.T. 2017, 'Hypoglycemia detection: multiple regression-based combinational neural logic approach', Soft Computing, vol. 21, no. 2, pp. 543-553.
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&copy; 2015 Springer-Verlag Berlin Heidelberg Hypoglycemia is a common and serious side effect of type 1 diabetes. We measure physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients using a multiple regression-based combinational neural logic approach. In this work, a neural logic network with multiple regression is applied to the development of non-invasive hypoglycemia monitoring system. It is an alarm system which measures the physiological parameters of electrocardiogram signal (heart rate and corrected QT interval) and determine the onset of hypoglycemia by the use of proposed hybrid neural logic approach. In this clinical application, a combinational neural logic network with multiple regression is systematically designed to hypoglycemia detection based on the characteristic of this application. To optimize the parameter of the hybrid combinational neural logic system, hybrid particle swarm optimization with wavelet mutation is applied to tuned the parameters of the system. To illustrate the effectiveness of the proposed method, hypoglycemia monitoring system which will be practically analyzed using real data sets collected from 15 children ((Formula presented.) years) with type 1 diabetes at the Department of Health, Government of Western Australia. With the use of proposed method, the best testing sensitivity of 79.07 % and specificity of 53.64 % were obtained.
Chai, R., Naik, G., Nguyen, T.N., Ling, S., Tran, Y., Craig, A. & Nguyen, H. 2017, 'Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-based System.', IEEE journal of biomedical and health informatics, vol. 21, no. 3, pp. 715-724.
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This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8% and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor) and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC=0.93) against other methods such as power spectral density (PSD) as feature extractor (AUC-ROC=0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.
Chai, R., Naik, G.R., Ling, S.H. & Nguyen, H.T. 2017, 'Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems', BioMedical Engineering OnLine, vol. 16, no. 5, pp. 1-23.
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Chai, R., Ling, S.H., San, P.P., Naik, G., Nguyen, T.N., Tran, Y., Craig, A. & Nguyen, H.T. 2017, 'Improving EEG-based Driver Fatigue Classification using Sparse-Deep Belief Networks', Frontiers in Neuroscience, vol. 11, no. 103, pp. 1-14.
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This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN) and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6% and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3% and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8%, 9.5% and 2.5% over ANN, BNN and DBN classifiers respectively.
Ling, S.H., Chan, K.Y., Leung, F.H.F., Jiang, F. & Nguyen, H. 2016, 'Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization', Engineering Applications of Artificial Intelligence, vol. 47, pp. 68-80.
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&copy; 2015 Elsevier Ltd. This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures.
Chan, K.Y. & Ling, S.H. 2016, 'A forward selection based fuzzy regression for new product development that correlates engineering characteristics with consumer preferences', JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol. 30, no. 3, pp. 1869-1880.
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Ekong, U., Lam, H.K., Xiao, B., Ouyang, G., Liu, H., Chan, K.Y. & Ling, S.H. 2016, 'Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines', Neurocomputing, vol. 199, pp. 66-76.
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An interval type-2 fuzzy support vector machine (IT2FSVM) is proposed to solve a classification problem which aims to classify three epileptic seizure phases (seizure-free, pre-seizure and seizure) from the electroencephalogram (EEG) captured from patients with neurological disorder symptoms. The effectiveness of the IT2FSVM classifier is evaluated based on a set of EEG samples which are collected from 10 patients at Peking university hospital. The EEG samples for the three seizure phases were captured by the 112 2-s 19 channel EEG epochs, where each patient was extracted for each sample. Feature extraction was used to reduce the feature vector of the EEG samples to 45 elements and the EEG samples with the reduced features are used for training the IT2FSVM classifier. The classification results obtained by the IT2FSVM are compared with three traditional classifiers namely Support Vector Machine, k-Nearest Neighbor and naive Bayes. The experimental results show that the IT2FSVM classifier is able to achieve superior learning capabilities with respect to the uncontaminated samples when compared with the three classifiers. In order to validate the level of robustness of the IT2FSVM, the original EEG samples are contaminated with Gaussian white noise at levels of 0.05, 0.1, 0.2 and 0.5. The simulation results show that the IT2FSVM classifier outperforms the traditional classifiers under the original dataset and also shows a high level of robustness when compared to the traditional classifiers with white Gaussian noise applied to it.
Ling, S.H., San, P.P. & Nguyen, H.T. 2016, 'Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes.', ISA Transactions, vol. 64, pp. 440-446.
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Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM.
Lam, H.K., Ekong, U., Xiao, B., Ouyang, G., Liu, H., Chan, K.Y. & Ling, S.H. 2015, 'Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications', NEUROCOMPUTING, vol. 149, pp. 1177-1187.
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Ling, S.H. 2015, 'Iterated Function System-Based Crossover Operation for Real-Coded Genetic Algorithm', Journal of Intelligent Learning Systems and Applications, vol. 07, no. 02, pp. 37-41.
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Chan, K.Y., Lam, H.K., Dillon, T.S. & Ling, S.H. 2015, 'A Stepwise-Based Fuzzy Regression Procedure for Developing Customer Preference Models in New Product Development', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 23, no. 5, pp. 1728-1745.
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Al-Fatlawi, A., Ling, S.S. & Lam, H.K. 2014, 'A comparison of neural classifiers for graffiti recognition', Journal of Intelligent Learning Systems and Application, vol. 6, no. 2, pp. 94-112.
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Ling, S.S., San, P., Chan, K.Y., Leung, F.H. & Liu, Y. 2014, 'An intelligent swarm based-wavelet neural network for affective mobile phone design', Neurocomputing, vol. 142, pp. 30-38.
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San, P., Ling, S.S., Nuryani, N. & Nguyen, H.T. 2014, 'Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system', IEEE Transactions on Cybernetics, vol. 44, no. 8, pp. 1338-1349.
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Lai, J.C., Leung, F.H. & Ling, S.S. 2014, 'Hypoglycaemia detection using fuzzy inference system with intelligent optimizer', Applied Soft Computing, vol. 20, pp. 54-65.
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Chai, R., Ling, S.S., Hunter, G., Tran, Y.H. & Nguyen, H.T. 2014, 'Brain-Computer Interface Classifier for Wheelchair Commands Using Neural Network With Fuzzy Particle Swarm Optimization', IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 5, pp. 1614-1624.
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This paper presents the classification of a three-class mental task-based braincomputer interface (BCI) that uses the Hilbert-Huang transform for the features extractor and fuzzy particle swarm optimization with cross-mutated-based artificial neural network (FPSOCM-ANN) for the classifier. The experiments were conducted on five able-bodied subjects and five patients with tetraplegia using electroencephalography signals from six channels, and different time-windows of data were examined to find the highest accuracy. For practical purposes, the best two channel combinations were chosen and presented. The three relevant mental tasks used for the BCI were letter composing, arithmetic, and Rubiks cube rolling forward, and these are associated with three wheelchair commands: left, right, and forward, respectively. An additional eyes closed task was collected for testing and used for onoff commands. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The accuracies for patients with tetraplegia were lower compared to the able-bodied subjects; however, this was improved by increasing the duration of the time-windows.TheFPSOCM-ANNprovides improved accuracies compared to genetic algorithm-based artificial neural network (GA-ANN) for three mental tasks-based BCI classifications with the best classification accuracy achieved for a 7-s time-window: 84.4% (FPSOCM-ANN) compared to 77.4% (GA-ANN).More comparisons on feature extractors and classifiers were included. For two-channel classification, the best two channels were O1 and C4, followed by second best at P3 and O2, and third best at C3 and O2. Mental arithmetic was the most correctly classified task, followed by mental Rubik's cube rolling forward and mental letter composing.
Lam, H.K., Ekong, U., Liu, H., Xiao, B., Araujo, H., Ling, S.H. & Chan, K.Y. 2014, 'A study of neural-network-based classifiers for material classification', NEUROCOMPUTING, vol. 144, pp. 367-377.
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Ling, S.S.H., Chan, K.Y., Palade, V., Dillon, T., Nguyen, H.T., Nguyen, T.N. & Chen, X.W. 2014, 'Special issue on hybrid intelligent methods for health technologies', Applied Soft Computing, vol. 20, pp. 1-3.
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Lai, J.C., Leung, F.H., Ling, S.S. & Nguyen, H.T. 2013, 'Hypoglycaemia detection using fuzzy inference system with multi-objective double wavelet mutation differential evolution', Applied Soft Computing, vol. 13, no. 5, pp. 2803-2811.
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In this paper, a fuzzy inference system (FIS) is developed to recognize hypoglycaemic episodes. Hypoglycaemia (low blood glucose level) is a common and serious side effect of insulin therapy for patients with diabetes. We measure some physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The FIS captures the relationship between the inputs of heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QTc), change of HR, change of QTc and the output of hypoglycaemic episodes to perform the classification. An algorithm called Differential Evolution with Double Wavelet Mutation (DWM-DE) is introduced to optimize the FIS parameters that govern the membership functions and fuzzy rules. DWM-DE is an improved Differential Evolution algorithm that incorporates two wavelet-based operations to enhance the optimization performance. To prevent the phenomenon of overtraining (over-fitting), a validation approach is proposed. Moreover, in this problem, two targets of sensitivity and specificity should be met in order to achieve good performance. As a result, a multi-objective optimization using DWM-DE is introduced to perform the training of the FIS. Experiments using the data of 15 children with TIDM (569 data points) are studied. The data are randomly organized into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). The result shows that the proposed FIS tuned by the multi-objective DWM-DE can offer good performance of doing classification.
San, P., Ling, S.S. & Nguyen, H.T. 2013, 'Industrial application of evolvable block-based neural network to hypoglycemia monitoring system', IEEE Transactions On Industrial Electronics, vol. 60, no. 12, pp. 5892-5901.
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Insulin-dependent diabetes mellitus is classified as type 1 diabetes mellitus (T1DM), and it can be further classified as immune-mediated or idiopathic. It is dangerous and can result in unconsciousness, seizures, and even sudden death. The most common physiological parameters to be effected from a hypoglycemic reaction are heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Considering the correlation between physiological parameters of an ECG signal and the status of hypoglycemia, a noninvasive hypoglycemia monitoring system is tested and introduced by proposing a hybrid particle-swarm-optimization-based block-based neural network (BBNN) algorithm. The proposed BBNN model offers advantages over conventional neural networks by performing the simultaneous optimization of both structure and weights. The hybrid particle swarm optimization with wavelet mutation searches for optimized structure and network parameters through particle information over a search space. All the actual data sets of 15 T1DM children were collected at the Department of Health, Government of Western Australia. Several experiments showed that the proposed BBNN performed well in terms of better sensitivity and specificity.
San, P., Ling, S.S. & Nguyen, H.T. 2013, 'Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system', Neural Computing & Applications, vol. 23, no. 7-8, pp. 2177-2184.
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To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation
Wong, G., Leung, F.H. & Ling, S.S. 2013, 'Predicting Protein-Ligand Binding Site Using Support Vector Machine with Protein Properties', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 6, pp. 1517-1529.
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Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In the past two decades, different approaches have been developed to predict the binding site, such as the geometric, energetic, and sequence-based methods. When scores are calculated from these methods, the algorithm for doing classification becomes very important and can affect the prediction results greatly. In this paper, the support vector machine (SVM) is used to cluster the pockets that are most likely to bind ligands with the attributes of geometric characteristics, interaction potential, offset from protein, conservation score, and properties surrounding the pockets. Our approach is compared to LIGSITE, LIGSITEcsc, SURFNET, Fpocket, PocketFinder, Q-SiteFinder, ConCavity, and MetaPocket on the data set LigASite and 198 drug-target protein complexes. The results show that our approach improves the success rate from 60 to 80 percent at AUC measure and from 61 to 66 percent at top 1 prediction. Our method also provides more comprehensive results than the others.
Wong, G., Leung, F.H. & Ling, S.S. 2013, 'Predicting protein-ligand binding site using support vector machine with protein properties', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 6, pp. 1517-1529.
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Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In the past two decades, different approaches have been developed to predict the binding site, such as the geometric, energetic, and sequence-based methods. When scores are calculated from these methods, the algorithm for doing classification becomes very important and can affect the prediction results greatly. In this paper, the support vector machine (SVM) is used to cluster the pockets that are most likely to bind ligands with the attributes of geometric characteristics, interaction potential, offset from protein, conservation score, and properties surrounding the pockets. Our approach is compared to LIGSITE, LIGSITEcsc, SURFNET, Fpocket, PocketFinder, Q-SiteFinder, ConCavity, and MetaPocket on the data set LigASite and 198 drug-target protein complexes. The results show that our approach improves the success rate from 60 to 80 percent at AUC measure and from 61 to 66 percent at top 1 prediction. Our method also provides more comprehensive results than the others.
Chai, R., Ling, S.H., Hunter, G.P., Tran, Y. & Nguyen, H.T. 2013, 'Classification of wheelchair commands using brain computer interface: comparison between able-bodied persons and patients with tetraplegia.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, vol. 2013, pp. 989-992.
This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubik's cube rolling forward; these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window.
Chan, K.Y., Dillon, T., Lam, H.K., Ling, S.S.H. & Nguyen, H.T. 2013, 'Special issue on hybrid evolutionary systems for manufacturing processes', APPLIED SOFT COMPUTING, vol. 13, no. 3, pp. 1329-1331.
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Nuryani, N., Ling, S.S. & Nguyen, H.T. 2012, 'Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection', Annals Of Biomedical Engineering, vol. 40, no. 4, pp. 934-945.
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Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.
Lai, J.C., Leung, F.H., Ling, S.S. & Shi, E.C. 2012, 'An Improved differential evolution and its industrial application', Journal of Intelligence Learning Systems and Application, vol. 4, no. 2, pp. 81-97.
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In this paper, an improved Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to solve the Economic Load Dispatch (ELD) problem. The double wavelet mutations are applied in order to enhance DE in exploring the solution space more effectively for better solution quality and stability. The first stage of wavelet operation is embedded in the DE mutation operation, in which the scaling factor is governed by a wavelet function. In the second stage, a wavelet-based mutation operation is embedded in the DE crossover operation. The trial population vectors are modified by the wavelet function. A suite of benchmark test functions is employed to evaluate the performance of the proposed DE in different problems. The result shows empirically that the proposed method out-performs signifycantly the conventional methods in terms of convergence speed, solution quality and solution stability. Then the proposed method is applied to the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) problem, which is a process to share the power demand among the online generators in a power system for minimum fuel cost. Two different conditions of the ELD problem have been tested in this paper. It is observed that the proposed method gives satisfactory optimal costs when compared with the other techniques in the literature
Ling, S.S. & Nguyen, H.T. 2012, 'Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model', Artificial Intelligence in Medicine, vol. 55, no. 3, pp. 177-184.
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Introduction: Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-reasoning system. Methods: Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-reasoning model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated. Conclusion: We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-reasoning model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10 min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.
Chan, K.Y., Yiu, C., Dillon, T.S., Nordholmand, S. & Ling, S.S. 2012, 'Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization', IEEE Transaction on Industrial Informatics, vol. 8, no. 4, pp. 869-879.
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For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a signi?cant improvement regarding speech recognition accuracy
Ling, S.S.H., Nguyen, H. & Lam, H.K. 2012, 'Computational intelligence in health technologies.', International journal of bioinformatics research and applications, vol. 8, no. 5-6, pp. 323-324.
Nguyen, L.B., Nguyen, A.V., Ling, S.H. & Nguyen, H.T. 2012, 'An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 2012, pp. 3515-3518.
Hypoglycemia is the most common but highly feared side effect of the insulin therapy for patients with Type 1 Diabetes Mellitus (T1DM). Severe episodes of hypoglycemia can lead to unconsciousness, coma, and even death. The variety of hypoglycemic symptoms arises from the activation of the autonomous central nervous system and from reduced cerebral glucose consumption. In this study, electroencephalography (EEG) signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected non-invasively using EEG signals from only two channels. This paper demonstrates that a significant advantage can be achieved by implementing adaptive training. By adapting the classifier to a previously unseen person, the classification results can be improved from 60% sensitivity and 54% specificity to 75% sensitivity and 67% specificity.
Ling, S.H., San, P.P., Nguyen, H.T. & Leung, F.H.F. 2012, 'Non-invasive nocturnal hypoglycemia detection for insulin-dependent diabetes mellitus using genetic fuzzy logic method', International Journal of Computational Intelligence and Applications, vol. 11, no. 4, pp. 1-17.
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Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection. &copy; 2012 Imperial College Press.
Ling, S.S. 2011, 'A new neural network structure: node-to-node-link neural network', Journal of Intelligence Learning Systems and Application, vol. 2, no. 1, pp. 1-11.
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This paper presents a new neural network structure and namely node-to-node-link neural network (N-N-LNN) and it is trained by real-coded genetic algorithm (RCGA) with average-bound crossover and wavelet mutation [1]. The N-N-LNN exhibits a node-to-node relationship in the hidden layer and the network parameters are variable. These characteristics make the network adaptive to the changes of the input environment, enabling it to tackle different input sets distributed in a large domain. Each input data set is effectively handled by a corresponding set of network parame-ters. The set of parameters is governed by other nodes. Thanks to these features, the proposed network exhibits better learning and generalization abilities. Industrial application of the proposed network to hand-written graffiti recognition will be presented to illustrate the merits of the network.
Ling, S.S. & Nguyen, H.T. 2011, 'Genetic algorithm based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes', IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp. 308-315.
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Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasivemonitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.
Liu, Y., Liu, B. & Ling, S.S. 2011, 'The almost periodic solution of lotka-volterra recurrent neural networks with delays', Neurocomputing, vol. 74, no. 6, pp. 1062-1068.
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By the fixed-point theorem subject to different polyhedrons and some inequalities (e.g.,the inequality resulted from quadratic programming), we obtain three theorems for the Lotka&acirc;Volterra recurrent neural network shaving almost periodic coefficients and delays. One of the three theorems can only ensure the existence of an almost periodic solution, whose existence and uniqueness the other two theorems are about. By using Lyapunov function, the sufficient condition guaranteeing the global stability of the solution is presented. Furthermore, two numerical examples are employed to illustrate the feasibility and validity of the obtained criteria. Compared with known results, the networks model is novel, and the results are extended and improved.
Ling, S.S. & Lam, H.K. 2011, 'Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer functions', Journal of Intelligence Learning Systems and Application, vol. 3, no. 1, pp. 37-44.
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Computational intelligence is a powerful tool for game development. In this paper, an algorithm of playing the game Tic-Tac-Toe with computational intelligence is developed. This algorithm is learned by a Neural Network with Double Transfer functions (NNDTF), which is trained by genetic algorithm (GA). In the NNDTF, the neuron has two transfer functions and exhibits a node-to-node relationship in the hidden layer that enhances the learning ability of the network. A Tic-Tac-Toe game is used to show that the NNDTF provide a better performance than the traditional neural network does.
Ling, S.S., Jiang, F., Nguyen, H.T. & Chan, K.Y. 2011, 'Hybrid Fuzzy Logic-Based Particle Swarm Optimization For Flow Shop Scheduling Problem', International Journal of Computational Intelligence and Applications, vol. 10, no. 3, pp. 335-356.
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This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.
Chan, K., Ling, S.S., Dillon, T.S. & Nguyen, H.T. 2011, 'Diagnosis Of Hypoglycemic Episodes Using A Neural Network Based Rule Discovery System', Expert Systems With Applications, vol. 38, no. 8, pp. 9799-9808.
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Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients' physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients.
Dehestani, D., Eftekhari, F., Guo, Y., Ling, S.S., Su, S. & Nguyen, H.T. 2011, 'Online support vector machine application for model based fault detection and isolation of HVAC system', International Journal of Machine Learning and Computing, vol. 1, no. 1, pp. 66-72.
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Chan, K.Y., Chan, K.W., Pong, G.T., Aydin, M.E., Fogarty, T.C. & Ling, S.S. 2010, 'A statistics-based genetic algorithm for quality improvements of power supplies', European Journal of Industrial Engineering, vol. 3, no. 4, pp. 468-492.
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This paper presents a new statistics-based evolutionary algorithm to improve the qualities of power supplies, in which operational costs and the stability of the power supply are optimised to provide a highly smooth but low-cost power supply service to customers. The proposed method is incorporated with the characteristics of the stochastic method, evolutionary algorithm and a more systematical statistical method, orthogonal design. It intends to compensate for the built-in randomness of the stochastic method and, at the same time, overcome the limitations of local search methods that are not suitable for handling multi-optima problems. Case studies on the WSCC 9-bus and New England 39-bus systems indicate that the proposed approach outperforms the existing method in terms of robustness in solution and convergence speed while the solution quality that can offer a more stable and cheaper power supply to customers is achieved.
Ling, S.S., Lu, H., Leung, F.H. & Chan, K.Y. 2008, 'Improved hybrid particle swarm optimized wavelet neural network for Modeling the development of Fluid Dispensing for Electronic Packaging', IEEE Transactions On Industrial Electronics, vol. 55, no. 9, pp. 3447-3460.
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An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Modeling the development of Fluid Dispensing for Electronic Packaging (MFD-EP) is presented in this paper. In modeling the fluid dispensing process, it is important to understand the process behavior as well as determine the optimum operating conditions of the process for a high-yield, low-cost, and robust operation. Modeling the fluid dispensing process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.
Ling, S.S., Lu, H., Chan, K.Y., Lam, H., Yeung, B. & Leung, F. 2008, 'Hybrid particle swarm optimization with wavelet mutation and its industrial applications', IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 38, no. 3, pp. 743-763.
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A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite or benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neural-network-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.
Nga, J.H.C., Iu, H.H.C., Ling, S.H. & Lam, H.K. 2008, 'Comparative study of stability in different TCP/RED models', CHAOS SOLITONS & FRACTALS, vol. 37, no. 4, pp. 977-987.
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Chan, K.Y., Fogarty, T.C., Aydin, M.E., Ling, S.H. & Iu, H.H.C. 2008, 'Genetic algorithms with dynamic mutation rates and their industrial applications', International Journal of Computational Intelligence and Applications, vol. 7, no. 2, pp. 103-128.
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This paper presents a method on how to estimate main effects of gene representation. This estimate can be used not only to understand the domination of genes in the representation but also to design the mutation rate in genetic algorithms (GAs). A new approach of dynamic mutation rate is proposed by integrating the information of the main effects into the genes. By introducing the proposed method in GAs, both solution quality and solution stability can be improved in solving a set of parametrical test functions. The algorithm was applied to two illustrative applications to evaluate the performance of the proposed method, where the first application is on solving uncapacitated facility location problems and the next is on optimal power flow problems, which are employed. Results indicate that the proposed method yields significantly better results than the existing methods. &copy; 2008 Imperial College Press.
Leung, F.H.F., Ling, S.H. & Lam, H.K. 2008, 'An improved genetic-algorithm-based neural-tuned neural network', International Journal of Computational Intelligence and Applications, vol. 7, no. 4, pp. 469-492.
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This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural network. Two application examples are given to illustrate the merits of the proposed network and the improved GA. &copy; Imperial College Press.
Lam, H.K., Ling, W.-.K., Iu, H.H.-.C. & Ling, S.S.H. 2008, 'Synchronization of chaotic systems using time-delayed fuzzy state-feedback controller', IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, vol. 55, no. 3, pp. 893-903.
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Ling, S.S., Leung, F.H. & Lam, H. 2007, 'Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications', Soft Computing, vol. 11, no. 11, pp. 1033-1052.
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This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.
Leung, K., Leung, F.H., Lam, H. & Ling, S.S. 2007, 'Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition', Neural Computing & Applications, vol. 16, no. 4-5, pp. 419-431.
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This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits.
Ling, S.S. & Leung, F.H. 2007, 'An improved genetic algorithm with average-bound crossover and wavelet mutation operations', Soft Computing, vol. 11, no. 1, pp. 7-31.
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This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.
Ling, S.S., Leung, F.H. & Lam, H. 2005, 'An improved genetic algorithm based fuzzy-tuned neural network', International Journal Of Neural Systems, vol. 15, no. 6, pp. 457-474.
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This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (CA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved CA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved CA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.
Lam, H., Ling, S.S., Leung, F.H. & Tam, P. 2004, 'Function estimation using a neural-fuzzy network and an improved genetic algorithm', International Journal Of Approximate Reasoning, vol. 36, no. 3, pp. 243-260.
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This paper presents the estimation of the transmission gains for an AC power line data network in an intelligent home. The estimated gains ensure the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given.
Leung, K., Leung, F.H., Lam, H. & Ling, S.S. 2004, 'On interpretation of graffiti digits and characters for eBooks: Neural-fuzzy network and genetic algorithm approach', IEEE Transactions On Industrial Electronics, vol. 51, no. 2, pp. 464-471.
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This paper presents the rule optimization, tuning of the membership functions, and optimization of the number of fuzzy rules, of a neural-fuzzy network (NFN) using a genetic algorithm (GA). The objectives are achieved, by training a proposed NFN with rule switches. The proposed NFN and GA are employed to interpret graffiti number inputs and commands for electronic books (eBooks).
Leung, F.H., Lam, H., Ling, S.S. & Tam, P. 2004, 'Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm', IEEE Transactions On Industrial Electronics, vol. 51, no. 1, pp. 172-182.
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This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot. arm will be given.
Ling, S.S., Leung, P., Lam, H. & Tam, P. 2003, 'Short-term electric load forecasting based on a neural fuzzy network', IEEE Transactions On Industrial Electronics, vol. 50, no. 6, pp. 1305-1316.
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Electric load forecasting is, essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given.
Leung, F.H., Lam, H., Ling, S.S. & Tam, P. 2003, 'Tuning of the structure and parameters of a neural network using an improved genetic algorithm', IEEE Transactions On Neural Networks, vol. 14, no. 1, pp. 79-88.
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This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It will also be shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its link s is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.
Ling, S.S., Leung, F.H., Lam, H., Lee, Y. & Tam, P. 2003, 'A novel genetic-algorithm-based neural network for short-term load forecasting', IEEE Transactions On Industrial Electronics, vol. 50, no. 4, pp. 793-799.
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This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network.

Other

Ling, S.S. 2007, 'Real-coded genetic algorithm based variable feed-forward neural networks and their applications'.
Ling, S.S. & Leung, F., 'Intelligent techniques for home electric load forecasting and balancing'.