International Journal of Computational Science and Engineering http://www.inderscience.com/browse/index.php?journalID=125&year=2011&vol=6&issue=4 Inderscience Publishers Ltd en-uk International Journal of Computational Science and Engineering 1742-7185 1742-7193 © 2011 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijcse_scoverijcse.jpg http://www.inderscience.com/browse/index.php?journalID=125&year=2011&vol=6&issue=4 http://www.inderscience.com/link.php?id=43919 By modelling the outputs produced by real world systems, we can study and, therefore, understand how they work and behave under different circumstances. This is especially interesting to support the prediction of future behaviour and, consequently, decision-making, what is particularly required in certain application domains. In order to proceed with such modelling, we organise system outputs as time series and study how those series were generated. The study of the time series generation process typically requires specialists and also detailed information on how and where data was obtained from. However, none of them may be available in certain circumstances. Such limitations motivated this paper which presents a survey of techniques commonly used to evaluate and classify time series generation processes and, most importantly, a novel automatic and systematic approach to conduct such task with a minimum of human intervention and subjectivity. By using such approach, researchers can select adequate techniques to model time series, reducing the modelling time and improving the chances to obtain higher accuracy. Classification of time series generation processes using experimental tools: a survey and proposal of an automatic and systematic approach
Renato P. Ishii; Ricardo A. Rios; Rodrigo F. Mello
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 217 - 237
By modelling the outputs produced by real world systems, we can study and, therefore, understand how they work and behave under different circumstances. This is especially interesting to support the prediction of future behaviour and, consequently, decision-making, what is particularly required in certain application domains. In order to proceed with such modelling, we organise system outputs as time series and study how those series were generated. The study of the time series generation process typically requires specialists and also detailed information on how and where data was obtained from. However, none of them may be available in certain circumstances. Such limitations motivated this paper which presents a survey of techniques commonly used to evaluate and classify time series generation processes and, most importantly, a novel automatic and systematic approach to conduct such task with a minimum of human intervention and subjectivity. By using such approach, researchers can select adequate techniques to model time series, reducing the modelling time and improving the chances to obtain higher accuracy.

]]> 10.1504/IJCSE.2011.043919 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 217 - 237 Renato P. Ishii; Ricardo A. Rios; Rodrigo F. Mello Department of Computing Systems, Federal University of Mato Grosso do Sul, Campo Grande-MS Caixa Postal 549, 79070-900, Brazil. ' Department of Computer Science, Institute of Mathematics and Computer Sciences, São Carlos-SP 13566-590, Brazil. ' Department of Computer Science, Institute of Mathematics and Computer Sciences, São Carlos-SP 13566-590, Brazil time series generation modelling decision making stochasticity determinism linearity stationarity experimental tools time series classification automatic approach survey. 2011-12-01T23:20:50-05:00 6 4 217 237 2011-12-01T23:20:50-05:00 http://www.inderscience.com/link.php?id=43923 Text categorisation (TC) is the task of assigning predefined categories to text. The primary step in TC is to transform documents into a representation suitable for machine learning algorithms. Bag of Words is the most popular document representation. Most of the machine learning algorithms are sensitive to the features fed into it and are misled by the high dimensionality of text. Feature selection (FS) is an important preprocessing step to remove redundant and irrelevant terms in the training corpus. This paper proposes an ant colony optimization (ACO) algorithm to select features for categorizing longer documents whose categories are closely related. Heuristic value for each word is computed by the statistical dependency of the term to a category and its compactness value. Compactness of a term indicates its spread in a document. Experiments were conducted with documents from 20 newsgroup and Reuters-21578 benchmarks. The selected features were fed into the naïve Bayes classifier and its performance was analysed. It was observed that the performance of the classifier improves with the features selected by the proposed method. The processes involved in algorithm are time intensive and demands parallelism. Hence the ACO algorithm was parallelised using the MapReduce programming model. The parallel algorithm was implemented and tested with a cluster of six machines formed using Hadoop. A parallel ACO algorithm to select terms to categorise longer documents
M. Janaki Meena; K.R. Chandran; A. Karthik; A. Vijay Samuel
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 238 - 248
Text categorisation (TC) is the task of assigning predefined categories to text. The primary step in TC is to transform documents into a representation suitable for machine learning algorithms. Bag of Words is the most popular document representation. Most of the machine learning algorithms are sensitive to the features fed into it and are misled by the high dimensionality of text. Feature selection (FS) is an important preprocessing step to remove redundant and irrelevant terms in the training corpus. This paper proposes an ant colony optimization (ACO) algorithm to select features for categorizing longer documents whose categories are closely related. Heuristic value for each word is computed by the statistical dependency of the term to a category and its compactness value. Compactness of a term indicates its spread in a document. Experiments were conducted with documents from 20 newsgroup and Reuters-21578 benchmarks. The selected features were fed into the naïve Bayes classifier and its performance was analysed. It was observed that the performance of the classifier improves with the features selected by the proposed method. The processes involved in algorithm are time intensive and demands parallelism. Hence the ACO algorithm was parallelised using the MapReduce programming model. The parallel algorithm was implemented and tested with a cluster of six machines formed using Hadoop.

]]> 10.1504/IJCSE.2011.043923 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 238 - 248 M. Janaki Meena; K.R. Chandran; A. Karthik; A. Vijay Samuel Department of CSE, PSG College of Technology, Coimbatore 641004, Tamilnadu, India. ' Department of IT, PSG College of Technology, Coimbatore 641004, Tamilnadu, India. ' Department of CSE, PSG College of Technology, Coimbatore 641004, Tamilnadu, India. ' Department of CSE, PSG College of Technology, Coimbatore 641004, Tamilnadu, India Bag of Words metaheuristics ant colony optimisation ACO CHIR parallel algorithms map reduce longer reduce text categorisation machine learning feature selection document classification. 2011-12-01T23:20:50-05:00 6 4 238 248 2011-12-01T23:20:50-05:00 http://www.inderscience.com/link.php?id=43922 The deformation of seabed considering seepage-stress coupling is analysed using finite element method in this study, the effect of the varying pore pressure on stress field is accounted in the analysis, and the impact of reservoir permeability and the well pressure on the decomposition radius is studied. The changes of mechanical parameters due to hydrate decomposition are achieved by using user defined field subroutine. The results show that vertical and horizontal effective stress of soil in the dissociated area increase significantly while gas hydrates are decomposed by depressurisation. The reservoir permeability and well pressure have significant impact on the decomposition radius. The seabed deformation increases non-linearly with the increasing of the gas hydrate decomposition radius. The maximum vertical displacement reaches 18 m and the maximum horizontal displacement reaches 7.5 m. A seepage-stress coupled analysis of seabed deformation induced by the decomposition of gas hydrate
Zhenwei Zhao; Xinchun Shang
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 249 - 254
The deformation of seabed considering seepage-stress coupling is analysed using finite element method in this study, the effect of the varying pore pressure on stress field is accounted in the analysis, and the impact of reservoir permeability and the well pressure on the decomposition radius is studied. The changes of mechanical parameters due to hydrate decomposition are achieved by using user defined field subroutine. The results show that vertical and horizontal effective stress of soil in the dissociated area increase significantly while gas hydrates are decomposed by depressurisation. The reservoir permeability and well pressure have significant impact on the decomposition radius. The seabed deformation increases non-linearly with the increasing of the gas hydrate decomposition radius. The maximum vertical displacement reaches 18 m and the maximum horizontal displacement reaches 7.5 m.

]]> 10.1504/IJCSE.2011.043922 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 249 - 254 Zhenwei Zhao; Xinchun Shang Civil Engineering Department, Fujian University of Technology, No. 3, Xueyuan Road, Fuzhou, China. ' Department of Mathematics and Mechanics, University of Science and Technology Beijing, No. 30, Xueyuan Road, Haidian District, Beijing, China gas hydrates vertical wells seabed deformation pore pressure well pressure depressurisation settlement horizontal displacement stress field seepage finite element method FEM reservoir permeability permafrost deep ocean sediments sediment offshore platforms gas production. 2011-12-01T23:20:50-05:00 6 4 249 254 2011-12-01T23:20:50-05:00 http://www.inderscience.com/link.php?id=43924 Virtualisation technology is a principal research issue in current cloud computing domain. We can obtain many benefits using the virtualisation technology in cloud and cluster computing, such as the ability to deploy any virtual platforms rapidly, easiness to manage all precious resources, provide customisation services platform and cost reduction. In order to discover optimal computing performance for virtualisation platforms, related virtualisation platforms with several well-known virtual machine tools are evaluated via standard benchmark programmes, including HPC challenge benchmark, NetPIPE and NCHC Application Suite. In this paper, we will analyse and compare significant experiment results that not only demonstrate the adequacy of virtual machines for high-performance computing, but also present different performance characteristics for virtualisation on cloud environment. The performance analysis for virtualisation cluster and cloud platforms
Ying-Chuan Chen; Shuen-Tai Wang; Hsi-Ya Chang; Te-Ming Chen; Chin-Hung Li
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 255 - 263
Virtualisation technology is a principal research issue in current cloud computing domain. We can obtain many benefits using the virtualisation technology in cloud and cluster computing, such as the ability to deploy any virtual platforms rapidly, easiness to manage all precious resources, provide customisation services platform and cost reduction. In order to discover optimal computing performance for virtualisation platforms, related virtualisation platforms with several well-known virtual machine tools are evaluated via standard benchmark programmes, including HPC challenge benchmark, NetPIPE and NCHC Application Suite. In this paper, we will analyse and compare significant experiment results that not only demonstrate the adequacy of virtual machines for high-performance computing, but also present different performance characteristics for virtualisation on cloud environment.

]]> 10.1504/IJCSE.2011.043924 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 255 - 263 Ying-Chuan Chen; Shuen-Tai Wang; Hsi-Ya Chang; Te-Ming Chen; Chin-Hung Li Software Technology Division, National Center for High-Performance Computing, NCHC, No. 28, Nan-Ke 3rd Rd., Hsin-Shi Dist., Tainan City 74147, Taiwan. ' Software Technology Division, National Center for High-Performance Computing, NCHC, No. 28, Nan-Ke 3rd Rd., Hsin-Shi Dist., Tainan City 74147, Taiwan. ' Software Technology Division, National Center for High-Performance Computing, NCHC, No. 28, Nan-Ke 3rd Rd., Hsin-Shi Dist., Tainan City 74147, Taiwan. ' Software Technology Division, National Center for High-Performance Computing, NCHC, No. 28, Nan-Ke 3rd Rd., Hsin-Shi Dist., Tainan City 74147, Taiwan. ' Software Technology Division, National Center for High-Performance Computing, NCHC, No. 28, Nan-Ke 3rd Rd., Hsin-Shi Dist., Tainan City 74147, Taiwan virtualisation technology cloud computing virtual platforms virtual machines performance cluster computing virtual machine tools high performance computing. 2011-12-01T23:20:50-05:00 6 4 255 263 2011-12-01T23:20:50-05:00 http://www.inderscience.com/link.php?id=43925 We propose a scheduling strategy in this paper to address the load imbalance problem of the distributed parallel apriori (DPA) algorithm published recently. We use fine grained tasks that are derived by dividing the tasks defined by DPA into smaller subtasks. The subtasks will be scheduled by a dynamic self-scheduling scheme for better load balance. Furthermore, we propose two different methods for data transmission from the master to workers. The first one broadcasts all the frequent k-itemsets to all work nodes while the second one transmits only the required data to each individual work node. Experimental results demonstrate the proposed two approaches both outperform DPA. The first one is more suitable for small datasets and the second one provides steadier performance improvement no matter which self-scheduling scheme is adopted. A fine-grained scheduling strategy for improving the performance of parallel frequent itemsets mining
Chao-Chin Wu; Lien-Fu Lai; Liang-Tsung Huang; Syun-Sheng Jhan; Chung Lu
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 264 - 274
We propose a scheduling strategy in this paper to address the load imbalance problem of the distributed parallel apriori (DPA) algorithm published recently. We use fine grained tasks that are derived by dividing the tasks defined by DPA into smaller subtasks. The subtasks will be scheduled by a dynamic self-scheduling scheme for better load balance. Furthermore, we propose two different methods for data transmission from the master to workers. The first one broadcasts all the frequent k-itemsets to all work nodes while the second one transmits only the required data to each individual work node. Experimental results demonstrate the proposed two approaches both outperform DPA. The first one is more suitable for small datasets and the second one provides steadier performance improvement no matter which self-scheduling scheme is adopted.

]]> 10.1504/IJCSE.2011.043925 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 264 - 274 Chao-Chin Wu; Lien-Fu Lai; Liang-Tsung Huang; Syun-Sheng Jhan; Chung Lu Department of Computer Science and Information Engineering, National Changhua University of Education, 2 Shi Da Road, Changhua City 500, Taiwan. ' Department of Computer Science and Information Engineering, National Changhua University of Education, 2 Shi Da Road, Changhua City 500, Taiwan. ' Department of Biotechnology, MingDao University, 69 Wen-Hua Road, Peetow, Changhua 523, Taiwan. ' Department of Information Technology, Ling Tung University, 1 Ling Tung Road, Taichung City 409, Taiwan. ' Department of Computer Science and Information Engineering, National Changhua University of Education, 2 Shi Da Road, Changhua City 500, Taiwan data mining frequent itemsets parallel computing distributed computing cluster systems dynamic scheduling load imbalance fine-grained scheduling self-scheduling. 2011-12-01T23:20:50-05:00 6 4 264 274 2011-12-01T23:20:50-05:00 http://www.inderscience.com/link.php?id=43927 Background subtraction is a very popular approach for foreground segmentation in a still scene image. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. In order to compensate for illumination changes, a background model updating process is generally employed, which leads to extra computation time. Background extraction is a fast and efficient moving object segmentation algorithm. This paper presents a novel background extraction algorithm based on improved mode algorithm to obtain the regions of static background, and a novel fuzzy background subtraction approach for video object segmentation. The goal of employing these approaches is to obtain a clean static background reference image and then apply it to background subtraction. We compare our method with other methods and report experimental result. Background extraction using improved mode algorithm for visual surveillance applications
M. Sivabalakrishnan; D. Manjula
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 275 - 282
Background subtraction is a very popular approach for foreground segmentation in a still scene image. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. In order to compensate for illumination changes, a background model updating process is generally employed, which leads to extra computation time. Background extraction is a fast and efficient moving object segmentation algorithm. This paper presents a novel background extraction algorithm based on improved mode algorithm to obtain the regions of static background, and a novel fuzzy background subtraction approach for video object segmentation. The goal of employing these approaches is to obtain a clean static background reference image and then apply it to background subtraction. We compare our method with other methods and report experimental result.

]]> 10.1504/IJCSE.2011.043927 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 275 - 282 M. Sivabalakrishnan; D. Manjula Department of CSE, College of Engineering, Anna University, Chennai 600 025, India. ' Department of CSE, College of Engineering, Anna University, Chennai 600 025, India mode background extraction foreground detection fuzzy background subtraction SAD visual surveillance still scene images illumination changes video object segmentation image segmentation sum of absolute difference. 2011-12-01T23:20:50-05:00 6 4 275 282 2011-12-01T23:20:50-05:00 http://www.inderscience.com/link.php?id=43928 With the technological advancements, new tools help people to save every precious moment. Using digital image, video, text or others ways captured every special moment forever through the different platforms and various ways to connection. This travel information system integrates GPS positioning function on mobile device while you want to remain the moment. It uses a novel method to save the special moment with emotion tag in this research. The travel information system uses various ways to show the travel information, such as blog, Google Map and Google Earth to show the time, locations and event during the trip. This research uses real-time recording system make user share diary, videos, feeling and pictures with friends. Travel information system collects every user's experience to provide other user for travel consult. An innovative method for this research, it is not only using quantifiable emotional data for recording travel, but also utilising quantifiable emotional data for travelling schedule recommendation. Using emotional classification model for travel information system
Jason C. Hung; Min-Feng Lee; Yu-Bing Wang
International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 283 - 293
With the technological advancements, new tools help people to save every precious moment. Using digital image, video, text or others ways captured every special moment forever through the different platforms and various ways to connection. This travel information system integrates GPS positioning function on mobile device while you want to remain the moment. It uses a novel method to save the special moment with emotion tag in this research. The travel information system uses various ways to show the travel information, such as blog, Google Map and Google Earth to show the time, locations and event during the trip. This research uses real-time recording system make user share diary, videos, feeling and pictures with friends. Travel information system collects every user's experience to provide other user for travel consult. An innovative method for this research, it is not only using quantifiable emotional data for recording travel, but also utilising quantifiable emotional data for travelling schedule recommendation.

]]> 10.1504/IJCSE.2011.043928 International Journal of Computational Science and Engineering, Vol. 6, No. 4 (2011) pp. 283 - 293 Jason C. Hung; Min-Feng Lee; Yu-Bing Wang Department of Information Management, Overseas Chinese University, No. 100, Chiao Kwang Rd., Xitun Dist., Taichung City 407, Taiwan ' Department of Information Management, Overseas Chinese University, No. 100, Chiao Kwang Rd., Xitun Dist., Taichung City 407, Taiwan ' Business School, University of Northern Virginia, Annandale, VA 22003, USA emotion classification travel information systems GPS global positioning systems mobile devices emotion tags real-time recording information sharing user experiences quantifiable emotional data travelling schedules travel recommendations digital cameras digital video recorders cell phones mobile phones travel experiences travel notes tourists travel planning. 2011-12-01T23:20:50-05:00 6 4 283 293 2011-12-01T23:20:50-05:00