editorial research and development of advanced computing...

EditorialResearch and Development of Advanced ComputingTechnologies

Shifei Ding,1,2 Zhongzhi Shi,2 and Ahmad Taher Azar3

1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China2Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences,Beijing 100190, China3Faculty of Computers and Information, Benha University, Benha 13511, Egypt

Correspondence should be addressed to Shifei Ding; [email protected]

Received 21 December 2014; Accepted 21 December 2014

Copyright © 2015 Shifei Ding et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Welcome to this special issue. Advanced computing tech-nologies are one of the hot research fields in artificialintelligence. This special issue aims to promote the research,development, and applications of advanced computing tech-nologies by providing a high-level international forum forresearchers and practitioners to exchange research results andshare development experiences. The papers in this editionwere selected among the highest rated papers in submittedmanuscripts. The selection of papers featured here coversthe topics of the main advanced computing technologies andexperimental studies of some application systems.

C.-W. Lin et al. proposed a privacy-preserving datamining method by using a compact prelarge GA-basedalgorithm to delete transactions for hiding sensitive itemsets.Thismethod solves the limitations of the evolutionary processby adopting both the compact GA-based mechanism andthe prelarge concept. A. Adam et al. build a framework forpeak detection on EEG signals in time domain analysis usingfeature selection and classifier parameters estimation basedon particle swarm optimization. The proposed frameworktries to find the best combination for all the available featuresthat offers good peak detection and a high classificationrate from the results in the conducted experiments. Y. Peiet al. propose a method to establish a human model inhigh dimensional search space by kernel classification forenhancing interactive evolutionary computation search. N.Sulaiman et al. propose a modified artificial bee colonyalgorithm to enhance the convergence speed and improve

the ability of the standard artificial bee colony algorithmto reach the global optimum by balancing the explorationand exploitation processes. This method was tested on thereactive power optimization problem and has outperformedother compared algorithms. Aiming at solving the biasedfeature selection in random forests, T. T. Nguyen et al.propose a modified random forests algorithm to select goodfeatures in learning random forests for high dimensionaldata. L. Li et al. put forward a prediction model based onmembrane computing optimization algorithm for chaos timeseries; the model optimizes simultaneously the parameters ofphase space reconstruction and least squares support vectormachine by using membrane computing optimization algo-rithm. M. R. Al-Othman et al. propose a heuristic rankingapproach on capacity benefit margin (CBM) determinationusing Pareto-based evolutionary programming technique.This paper introduces a novel multiobjective approach forCBM assessment taking into account tie-line reliability ofinterconnected systems and presents a new Pareto-basedevolutionary programming (EP) technique used to performa simultaneous determination of CBM for all areas of theinterconnected system.

In order to build a QoS-aware bufferless network-on-chip scheme for datacenters, J. Fang et al. propose QBLESS, aQoS-aware bufferless NoC scheme for datacenters. QBLESSconsists of two components: a routing mechanism (QBLESS-R) that can substantially reduce flit deflection for high-priority applications and a congestion-control mechanism

Hindawi Publishing Corporatione Scientific World JournalVolume 2015, Article ID 239723, 2 pageshttp://dx.doi.org/10.1155/2015/239723

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(QBLESS-CC) that guarantees performance for high-priorityapplications and improves overall system throughput. S.K. Chandrasekaran et al. propose a framework of primarypath reservation admission control protocol, which achievesimproved QoS by making use of backup route combinedwith resource reservation. In this paper, a network topologyhas been simulated and the present approach proves to be amechanism that admits the session effectively. V.Uc-Cetina etal. propose a Markov decision process model for solving theweb service composition problem. Iterative policy evaluation,value iteration, and policy iteration algorithms are used toexperimentally validate the present approach. S. U. Rehmanand A. Nadeem propose a novel approach to the testing ofmultiagent systems based on Prometheus design artifacts. Inthe proposed approach, different interactions between theagent and actors are considered to test the multiagent system.M. Bal et al. study 11 machine learning methods (SVM,MLP, C4.5, etc.) for the inference mechanism of medicaldecision support system based on ALARM network. Theperformances of 11 machine learning algorithms are tested on44 synthetic data sets (11 different dependent variables and 4different dataset sizes).The comparison of algorithms appliedtwo different tests (statistically difference and average ranktests). C4.5 decision tree is the best algorithm according toboth of the tests for our 44 datasets.The datasets havingmoresamples can be better predicted than having fewer samples.

A comparative study on interaction of form and motionprocessing streams by applying two different classifiers inmechanism for recognition of biological movement is pro-posed by B. Yousefi and C. K. Loo. The presented approachhas addressed a very substantial interrelevant comparisonof the interaction of two processing streams of mammalianbrain visual system. For mining data streams, A. O. Dı́az etal. present a fast adapting ensemble method which adaptsvery quickly to both abrupt and gradual concept drifts,and the method has been specifically designed to deal withrecurring concepts. After initializing color image by utilizingthe unsupervisedOrchard and Bouman clustering technique,D. Khattab et al. present a comparative study using differentcolor spaces to evaluate the performance of color imagesegmentation using the automatic GrabCut technique. M.-L. Huang et al. combine feature selection and SVM recursivefeature elimination to investigate the classification accuracyof multiclass problems for Dermatology and Zoo databases.Meanwhile, Taguchi’s method was jointly combined withSVM classifier in order to optimize parameters to increaseclassification accuracy for multiclass classification.

For image compression, N. A. Abu and F. Ernawanpropose a psychovisual threshold on the large discrete cosinetransform (DCT) image block which will be used to auto-matically generate the much needed quantization tables. G.C. Kim proposes a fully autonomous feature selection andcamouflaged object detection method based on the onlineanalysis of spectral and spatial features.

Z.Wang et al. point out that spectral clustering algorithmsapplied in community detection have two inadequacies andpresent a novel community detection algorithm based ontopology potential and spectral clustering that contains richstructural information of the network. A. Rodan et al. utilize

an ensemble of multilayer perceptrons whose training isobtained using negative correlation learning for predictingcustomer churn in a telecommunication company. S. Chenet al. describe a hybrid approach for forecasting fraudulentfinancial statements. The authors firstly screen the importantvariables using the stepwise regression and then use logisticregression, support vector machine, and decision tree toconstruct the classification models to make a comparison.

J. D. Gazzano et al. propose a complete grid infras-tructure for distributed high-performance computing basedon dynamically reconfigurable FPGAs. This infrastructurewas tested and significant performance gains have beenachieved. P systems are a class of distributed parallel com-puting models, H. Peng et al. present a novel clusteringalgorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membraneclustering algorithm. Q. Luo et al. propose a discrete batalgorithm (DBA) for optimal problem of permutation flowshop scheduling. The authors construct a direct relationshipbetween the job sequence and the vector of individuals inDBA.

Acknowledgments

As guest editors of this issue, we would like to thank all theauthors for submitting their work and the all the reviewersfor their indispensable contribution in finalizing this issue.We are very pleased to have edited this special issue and wesincerely hope that the readers will find interesting ideas in itand enjoy reading these papers.This special issue is supportedby the National Natural Science Foundation of China underGrant no. 61379101 and the National Basic Research Programof China under Grant no. 2013CB329502.

Shifei DingZhongzhi Shi

Ahmad Taher Azar

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