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Special issue on advanced intelligent computing theories and methodologies
Neurocomputing 137 (2014) 1–2
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
Special issue on advanced intelligent computing theories and methodologies
All the 34 articles appearing in this special issue on Advanced Intelligent Computing Theories and Methodologies are extended versions of the papers presented at the 2012 Eighth International Conference on Intelligent Computing (ICIC2012) held on July 25–29, 2012 in Huangshan, China. All the papers included here have been thoroughly reviewed and revised with the support of many reviewers under the Elsevier Editorial System (EES). Thirtyfour papers representing less than five percent of all eligible papers accepted at the ICIC2012 are selected for inclusion in this special issue. The selected papers are organized into the following sections.
1. Neural network based modeling and applications This issue starts with artificial neural networks for feedback control of a human elbow hydraulic prosthesis by Bevilacqua et al. by using artificial neural networks as an effective and simple method for obtaining in real time the solution of the problem while limiting the computational effort. This is followed by an interesting article by Kang et al. about an adaptive tracking controller for parallel robotic manipulators based on fully tuned radial basis function networks. Next, Zhang et al. presents a paper about how to detect the virus based on ensemble neural network and SVM. Then, an interesting paper by Yahya et al. is about artificial neural networks aided solution to the problem of geometrically bounded singularities and joint limits prevention of a three dimensional planar redundant manipulator. After that, Kuremoto et al. presents a paper focused on discussing time series forecasting method using a deep belief network with restricted Boltzmann machines. In addition, the last paper in this section deals with multi‐source transfer ELM‐based Q learning method.
2. Image/video processing applications This issue also contains interesting articles on image matching using moment invariants by Premaratne et al. 3D scene reconstruction enhancement method based on automatic context analysis and convex optimization by Le et al. Super-resolution restoration of millimeter wave image (MWM) based on sparse representation method by Shang et al. Recognizing complex events in real movies by combining audio and video features by Du et al. Faster computation of non-zero invariants from graph based method by Shamsuddin et al. There are four more papers on multi-channel features based automated segmentation of diffusion tensor imaging using an http://dx.doi.org/10.1016/j.neucom.2014.02.009 0925-2312 & 2014 Elsevier B.V. All rights reserved.
improved FCM with spatial constraints; efficient minutiae based geometric hashing for fingerprint database; mutual cascade method for pedestrian detection; and effect of morphing on embedding capacity and embedding efficiency.
3. Learning, feature exaction and classification Feature learning and extracting in classification has become a very popular topic in the pattern recognition field nowadays. Firstly, discrete exponential Bayesian networks: definition, learning and application for density estimation; a general method for P-model fixed structure stochastic automata (FSSA) learning in triple level environment; a comparative study and improvement of two ICA using reference signal methods; predicting dynamic deformation of retaining structure by Least Squares Support Vector Regression (LSSVR) based time series method; robust nose tip localization based on two-stage subclass discriminant analysis were presented in this section by Leray et al., Li et al., Mi et al., Wang et al. and Song et al., respectively. In addition, there are seven papers on denoising method based on null space pursuit for infrared spectrum; regularized complete linear discriminant analysis; locally adaptive multiple kernel clustering; robust iris recognition using sparse error correction model and discriminative dictionary learning; new learning automata based approach for online tracking of event patterns; co-training algorithm for EEG classification with biomimetic pattern recognition and sparse representation and Immune K-SVD algorithm for Dictionary Learning in Speech Denoising.
4. Evolutionary, classification and optimization This special issue also contains seven papers on Evolutionary, Classification and Optimization. The research presented by Han et al. describes a diversity-guided hybrid particle swarm optimization method based on gradient search. Ji et al. present interactive evolutionary algorithms with decision-maker's preferences for solving interval multi-objective optimization problems. Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization is presented by Niu et al. Liu et al. describe a particle swarm optimization using local stochastic search and enhancing diversity for continuous optimization. In addition, three more papers are on bacterial colony foraging optimization; invasive weed optimization algorithm for optimization; No-idle flow
2
Editorial / Neurocomputing 137 (2014) 1–2
shop scheduling problem; and an improved gene expression programming approach for symbolic regression problems. It should be stressed that recommendations for this special issue were made by the ICIC2012's International Program Committee, and the final selections were made on the basis of quality, novelty, and theoretical or practical importance. Each paper was subject to two rounds of review with a minimum of three reviewers, reflecting the high standards for the selected papers in this issue. We hope that you will find this special issue informative and beneficial to your own work. As guest editor, I would like to take this opportunity to thank all the authors for their contributions to this special issue, and the reviewers for their expert review comments. I would also like to thank the Editor-in-Chief of Neurocomputing, Tom Heskes, for his advice and support during the preparation of this special issue.
Guest Editor De-Shuang Huang Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China E-mail address: [email protected] URL: http://www.intelengine.cn/English/people/hds.htm Received 6 February 2014 Available online 15 February 2014
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
Special issue on advanced intelligent computing theories and methodologies
All the 34 articles appearing in this special issue on Advanced Intelligent Computing Theories and Methodologies are extended versions of the papers presented at the 2012 Eighth International Conference on Intelligent Computing (ICIC2012) held on July 25–29, 2012 in Huangshan, China. All the papers included here have been thoroughly reviewed and revised with the support of many reviewers under the Elsevier Editorial System (EES). Thirtyfour papers representing less than five percent of all eligible papers accepted at the ICIC2012 are selected for inclusion in this special issue. The selected papers are organized into the following sections.
1. Neural network based modeling and applications This issue starts with artificial neural networks for feedback control of a human elbow hydraulic prosthesis by Bevilacqua et al. by using artificial neural networks as an effective and simple method for obtaining in real time the solution of the problem while limiting the computational effort. This is followed by an interesting article by Kang et al. about an adaptive tracking controller for parallel robotic manipulators based on fully tuned radial basis function networks. Next, Zhang et al. presents a paper about how to detect the virus based on ensemble neural network and SVM. Then, an interesting paper by Yahya et al. is about artificial neural networks aided solution to the problem of geometrically bounded singularities and joint limits prevention of a three dimensional planar redundant manipulator. After that, Kuremoto et al. presents a paper focused on discussing time series forecasting method using a deep belief network with restricted Boltzmann machines. In addition, the last paper in this section deals with multi‐source transfer ELM‐based Q learning method.
2. Image/video processing applications This issue also contains interesting articles on image matching using moment invariants by Premaratne et al. 3D scene reconstruction enhancement method based on automatic context analysis and convex optimization by Le et al. Super-resolution restoration of millimeter wave image (MWM) based on sparse representation method by Shang et al. Recognizing complex events in real movies by combining audio and video features by Du et al. Faster computation of non-zero invariants from graph based method by Shamsuddin et al. There are four more papers on multi-channel features based automated segmentation of diffusion tensor imaging using an http://dx.doi.org/10.1016/j.neucom.2014.02.009 0925-2312 & 2014 Elsevier B.V. All rights reserved.
improved FCM with spatial constraints; efficient minutiae based geometric hashing for fingerprint database; mutual cascade method for pedestrian detection; and effect of morphing on embedding capacity and embedding efficiency.
3. Learning, feature exaction and classification Feature learning and extracting in classification has become a very popular topic in the pattern recognition field nowadays. Firstly, discrete exponential Bayesian networks: definition, learning and application for density estimation; a general method for P-model fixed structure stochastic automata (FSSA) learning in triple level environment; a comparative study and improvement of two ICA using reference signal methods; predicting dynamic deformation of retaining structure by Least Squares Support Vector Regression (LSSVR) based time series method; robust nose tip localization based on two-stage subclass discriminant analysis were presented in this section by Leray et al., Li et al., Mi et al., Wang et al. and Song et al., respectively. In addition, there are seven papers on denoising method based on null space pursuit for infrared spectrum; regularized complete linear discriminant analysis; locally adaptive multiple kernel clustering; robust iris recognition using sparse error correction model and discriminative dictionary learning; new learning automata based approach for online tracking of event patterns; co-training algorithm for EEG classification with biomimetic pattern recognition and sparse representation and Immune K-SVD algorithm for Dictionary Learning in Speech Denoising.
4. Evolutionary, classification and optimization This special issue also contains seven papers on Evolutionary, Classification and Optimization. The research presented by Han et al. describes a diversity-guided hybrid particle swarm optimization method based on gradient search. Ji et al. present interactive evolutionary algorithms with decision-maker's preferences for solving interval multi-objective optimization problems. Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization is presented by Niu et al. Liu et al. describe a particle swarm optimization using local stochastic search and enhancing diversity for continuous optimization. In addition, three more papers are on bacterial colony foraging optimization; invasive weed optimization algorithm for optimization; No-idle flow
2
Editorial / Neurocomputing 137 (2014) 1–2
shop scheduling problem; and an improved gene expression programming approach for symbolic regression problems. It should be stressed that recommendations for this special issue were made by the ICIC2012's International Program Committee, and the final selections were made on the basis of quality, novelty, and theoretical or practical importance. Each paper was subject to two rounds of review with a minimum of three reviewers, reflecting the high standards for the selected papers in this issue. We hope that you will find this special issue informative and beneficial to your own work. As guest editor, I would like to take this opportunity to thank all the authors for their contributions to this special issue, and the reviewers for their expert review comments. I would also like to thank the Editor-in-Chief of Neurocomputing, Tom Heskes, for his advice and support during the preparation of this special issue.
Guest Editor De-Shuang Huang Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China E-mail address: [email protected] URL: http://www.intelengine.cn/English/people/hds.htm Received 6 February 2014 Available online 15 February 2014