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Neurocomputing 71 (2008) 2616– 2618

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Neurocomputing

0925-23

doi:10.1

journal homepage: www.elsevier.com/locate/neucom

Editorial

Engineering of intelligent systems (ICEIS 2006)

This special issue brings together selected, expanded andsignificantly revised versions of papers presented at the 1st IEEEInternational Conference on ‘‘Engineering of Intelligent Systems’’(ICEIS’2006) held at Islamabad, Pakistan, 22–23 April 2006.

ICEIS’2006 represented the first occasion where leadingexperts and keynote researchers from all over the world gatheredtogether in Islamabad to exchange the latest results, explore newavenues and chart future directions for this cutting-edge inter-disciplinary field. The conference programme comprised approxi-mately 90 papers selected from around 250 submissions followinga rigorous double-blind peer review procedure involving threeinternational referees. The accepted manuscripts were included inthe IEEE Xplore Library and represented an innovative mix ofpapers, ranging from those addressing fundamental researchissues to those concerned with a variety of multidisciplinaryapplications in the areas of artificial intelligence, neuroscience,vision systems, robotics and many more.

The topics covered by the conference were theoretical as wellas application-oriented. Theoretical topics of interest included:artificial immune systems, artificial life, self-organizing andevolutionary computation, fuzzy logic, hybrid systems, modeland case-based reasoning, expert systems, knowledge extraction,machine learning, multi-agent systems, neural networks, learningautomata, neuromorphic systems, swarm intelligence and othernature-inspired computational techniques. On the applicationside, topics of interest included: concurrent engineering, condi-tion monitoring and control, data mining and knowledgeextraction, fault detection, hardware implementations, imageprocessing and computer vision, industrial diagnostics, e-businessand management, natural language processing, pattern recogni-tion, resource allocation, remote sensing, mobile robotics andbio-robotics, security, data fusion, signal processing, speechprocessing, digital and mobile telecommunications, biomedicalengineering, sensory systems, software engineering, schedulingand decision making, supply-chain management and other real-world applications of computational intelligence techniques.

The present special issue comprises 13 contributions selectedby the Guest Editors on the basis of originality, technical qualityand relevance. All papers have been subjected to the usualrigorous Neurocomputing peer review process by anonymousreferees.

The first manuscript ‘‘On linking human and machine brains’’,by K. Warwick and V. Ruiz, relates to brain–computer interfacing.This paper describes how the use of implant technology is able todiminish the effects of certain neural illnesses and to distinctlyincrease the range of abilities of those affected. A key element in

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the development of implant technology is the need for a clearinterface linking the human brain directly with a computer. Inparticular, the main thrust of the paper is a discussion of neuralimplant experimentation linking the human nervous system bi-directionally with the internet. The paper reports on experimentswhere neural signals were transmitted to various devices todirectly control them, in some cases via the internet, and feedbackto the brain was obtained from the fingertips of a robot hand,ultrasonic (extra) sensory input and neural signals directly fromanother human’s nervous system. Consideration is given to theprospects for neural implant technology in the future, both in theshort term as a therapeutic device and in the long term as a formof enhancement, including the realistic potential for thought.

In the next contribution titled ‘‘A multi-stage neural networkaided system for detection of microcalcifications in digitizedmammograms’’, N.R. Pal, B. Bhowmick, S.K. Patel, S. Pal and J. Daspropose a multi-stage detection system for microcalcification inbreast cancer. An online feature selection technique is used toidentify a set of useful features out of a set of features computed ata few randomly selected positive (calcified) and negative (normal)pixels. A neural network is then trained with the selected features.The network output is cleaned using connected componentanalysis and a new algorithm for removing thin elongatedstructures. A measure of local density of the calcified points isthen computed at every suspected pixel of these cleaned imagesand the peak of the mountain potential is used to classifymammograms as calcified or normal. The system is tested on aset of several mammograms comprising abnormal as well asnormal images that are not used in training. The paper shows thatthe developed system is able to classify all of them correctly and,for each abnormal image, the system is able to locate the calcifiedregions quite accurately.

The next contribution ‘‘A real-time kepstrum approach tospeech enhancement and noise cancellation’’, by J. Jeong and T.J.Moir, introduces a kepstrum method for identification of acoustictransfer functions and its real-time application in speechenhancement and noise cancellation. Using kepstrum analysis,the authors show that the kepstrum method is reliable andapplicable to real-time processing when based on the fast Fourier-transform. The authors claim that kepstrum analysis applicationto speech enhancement has several favourable features such asapplication flexibility, processing robustness and computationalsimplicity. Their results show that the kepstrum approachcontributes to speech enhancement and noise cancellation withan improved performance in signal-to-noise ratio by usingonly two microphones with a small physical dimension in size.

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Experiments on a 20 cm broadside two-microphone acousticbeamformer configuration are implemented in real-time in anindoor office.

The next manuscript ‘‘Estimation and decision fusion: Asurvey’’, by A. Sinha, H. Chen, D.G. Danu, T. Kirubarajan and M.Farooq, concerns data/decision/estimate fusion and combinationof classifiers. Data fusion is being applied in a large number ofengineering fields and relies on the use of several mathematicaltools. This survey focuses on some aspects of estimation anddecision fusion. In estimation fusion, the survey discusses thedevelopment of fusion architectures and algorithms. In decisionfusion, emphasis is put on techniques to combine classifiers.Methods using neural networks for data fusion are also brieflydiscussed.

The next manuscript titled ‘‘A modular classification model forreceived signal strength based location systems’’, by U. Ahmad,A.V. Gavrilov, S. Lee and Y.-K. Lee, reports on estimating location ofmobile devices based on received signal strength patterns torealize indoor positioning systems. Accuracy of received-signal-strength-based location estimation, particularly in large targetsites, is shown to be affected by several environmental factors, inparticular the temporal or permanent absence of radio signalssparsity and redundancy in feature spaces. In this paper, theauthors present a visibility-matrix-based modular classificationmodel that systematically caters for unavailable signals. Theirmodel is practically realized using the multilayer-perceptron andlearning vector quantization methods. In order to demonstrate therobustness and applicability of this model, the authors developtwo location estimation systems at different sites and test them inreal-world environments.

The next manuscript ‘‘FCANN: A new approach for extractionand representation of knowledge from ANN trained via formalconcept analysis’’, by L.E. Zarate, S. Mariano Dias and M.A. JunhoSong, mainly concerns rule extraction using neural networks.Artificial neural networks (ANNs) are being increasingly used forthe representation of different systems and physics processes.Once trained, ANNs are capable of dealing with operationalconditions not seen during the training process. However, humanscannot easily assimilate the knowledge content encoded by ANNs,since such implicit knowledge is difficult to be extracted. In thispaper, formal concept analysis (FCA) is used in order to extractand represent knowledge from trained ANNs. The new so-calledFCANN approach yields a complete canonical base, non-redundantand with minimum implications, which qualitatively describesthe process being studied. The FCANN method is not a classifieritself as per other methods for rule extraction and can be used todescribe and understand the relationship among the processparameters through implication rules. Comparisons of FCANNwith classical C4.5 and TREPAN algorithms are made in an attemptto illustrate its features along with its effectiveness and applica-tions to real-world problems.

The next manuscript titled ‘‘Complex stochastic systemsmodelling and control via iterative machine learning’’, by H.Wang, P. Afshar and A. Wang, addresses the modelling and controlof complex stochastic systems that require the control of theirstochastic distributions. Following a brief survey of recentdevelopments in the field, the authors propose a detailed designprocedure for an iterative-learning-based output probabilitydensity function control algorithm. In this context, a radial basisfunction (RBF) neural network is used to approximate the outputprobability density function, and the system dynamics arerepresented using a mathematical model between the weightsof the RBF neural networks and the control input. The wholecontrol horizon is divided into a number of batches and fixed RBFsare used within each batch. However, between batches, aniterative machine learning process for shape update of the RBFs

is developed so as to improve the closed-loop performance on abatch-by-batch basis.

The next manuscript titled ‘‘A fuzzy learning-sliding modecontroller for direct field oriented induction machines’’, by H.Rehman and R. Dhaouadi, presents a direct-field orientedinduction motor drive system with two distinct features: asliding-mode voltage-mode flux observer (SMVMFO) and a fuzzymodel reference learning controller (FMRLC). The speed of theinduction motor is regulated using an FMRLC, which does notrequire rigorous tuning of the controller’s parameters. The rotorflux estimation for the direct field orientation is realized using anewly proposed SMVMFO that is insensitive to the statorresistance variation. The complete drive system is shown toexhibit very good speed tracking performance, accurate fluxestimation and field orientation, as well as high robustness tostator resistance variation. Extensive simulations are presentedfor performance evaluation and validation of the proposed controlscheme.

The next manuscript titled ‘‘Adaptive multi-model slidingmode control of robotic manipulators using soft computing’’, by N.Sadati and R. Ghadami, presents an adaptive multi-model slidingmode controller for robotic manipulators. By using a multiplemodels technique, the nominal part of the control signal isconstructed according to the most appropriate model in differentenvironments. Adaptive single-input single-output (SISO) fuzzysystems or radial basis function (BF) neural networks are used toapproximate the discontinuous part of the control signal. The keyfeature of this scheme is that prior knowledge of the systemuncertainties is not required to guarantee stability. In addition, thechattering phenomenon in sliding mode control and the steady-state tracking error are eliminated. Furthermore, a theoreticalproof of the stability and convergence of the proposed scheme isalso presented.

The next manuscript titled ‘‘Polynomial models of genedynamics’’, by S. Faisal, G. Lichtenberg, and H. Werner, concernsthe analysis of gene expression data through microarrays. It hasbeen observed that genetic regulatory networks share manycharacteristics with Boolean networks such as periodicity andself-organization ability. In addition, it is a known fact that inthese networks most genes are governed by Canalizing Booleanfunctions. However, the actual gene expression level measure-ments are continuous valued. In order to combine discrete andcontinuous aspects, Zhegalkin polynomials are successfullyproposed by the authors as continuous representations of Booleanfunctions.

The next manuscript titled ‘‘A genetic algorithm for productdisassembly sequence planning’’, by W. Hui, X. Dong and D.Guanghong, presents a method to solve the disassembly sequenceplanning problem. An approach based on the disassemblyfeasibility information graph is presented to describe the productdisassembly sequence and operation information. The main ideais to map the disassembly sequence planning problem onto adisassembly feasibility information graph as an optimal path-searching problem. A genetic algorithm is employed to efficientlydetermine feasible and optimal disassembly solutions. Theauthors also present a case study to illustrate the performanceof the proposed method.

The next contribution titled ‘‘Autonomous intelligent cruisecontrol using a novel multiple-controller framework incorporat-ing fuzzy-logic based switching and tuning’’, by R.A. Abdullah,A. Hussain, K. Warwick and A. Zayed, presents a novel intelli-gent multiple-controller framework incorporating a fuzzy-logicbased switching and tuning supervisor along with a generalizedlearning model for an autonomous cruise-control application.Their proposed methodology combines the benefits of a con-ventional proportional-integral-derivative (PID) controller and a

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PID-structure-based simultaneous zero and pole placementcontroller. The authors show that the switching decision betweenthe two nonlinear fixed structure controllers can be madeeffectively on the basis of the required performance measureusing a fuzzy-logic based supervisor operating at the highest levelof the system. The supervisor is also employed to adaptively tunethe parameters of the controllers in order to achieve the desiredclosed-loop system performance. The intelligent multiple-controlframework is applied to the autonomous cruise-control problemin order to maintain a desired vehicle speed by controlling thethrottle plate angle in an electronic throttle control system.Sample simulation results using a validated nonlinear vehiclemodel are used to demonstrate the effectiveness of the proposedapproach with respect to adaptively tracking desired vehiclespeed changes and achieving the desired speed of response, whilepenalizing excessive control action.

The final contribution titled ‘‘Statistical models of KSE100index using hybrid financial systems’’, by S. Fatima and G. Hussain,utilizes hybrid financial systems (HFSs) to model Karachi StockExchange index data (KSE100) for short-term prediction. The HFSsdeveloped for this purpose are a combination of ANNs and ARIMAor ARCH/GARCH models. The authors compare ANNs with ARIMAand ARCH/GARCH on the basis of forecast mean square error(FMSE) and find that ANNs give better forecasting performanceand are able to outperform ARIMA and ARCH/GARCH models.Finally, while comparing the performance of HFSs of ANNARIMAand ANNARCH/GARCH with ANN models, the authors concludethat the HFS ANNARCH/GARCH is superior to standard ANN andHFS ANNARIMA in forecasting the KSE100 index.

The Guest Editors would like to thank all the authors for theircontributions and all the anonymous reviewers who greatlyhelped improve the quality of the papers in this Special Issue.Special thanks go to the Editor in Chief, Tom Heskes, for verykindly inviting us to edit this Special Issue, and to Vera Kamphuisfrom the Neurocomputing Editorial Office for her support inputting the Special Issue together.

Lastly, the Guest Editors owe an immense debt of gratitude toProfessor Jaffar ur Rahman, the founding General co-Chair ofICEIS’2006. Organizing the Conference in Pakistan was his brain-child and he worked extremely hard to make it happen. Sadly, heand his family (wife and four lovely little daughters) all perishedin the tragic earthquake that destroyed large tracts of northern

Pakistan in October 2006. The Guest Editors would like todedicate this Special Issue to the memory of Professor Rahman,his family and all the other victims of the catastrophic earthquaketragedy.

Lead Guest Editors

Amir Hussain

Centre for Cognitive and Computational Neuroscience,

University of Stirling, Stirling FK9 4LA,

Scotland, UK

E-mail address: a.hussain@cs.stir.ac.uk

Simone FioriDipartimento di Elettronica,

Intelligenza Artificiale e Telecomunicazioni,

Universita Politecnica delle Marche,

Ancona, Italy

Co-Guest Editors

I.M. Qureshi

Faculty of Engineering and Technology,

International Islamic University,

Islamabad, Pakistan

T.S. DurraniInstitute of Communications and Signal Processing,

University of Strathclyde,

Glasgow G1 1XW, Scotland, UK

M.M. AhmedMohammad Ali Jinnah University,

Islamabad Campus, Pakistan

K. FukushimaDepartment of Information and Communication Engineering,

The University of Electro-Communication,

Tokyo, Japan