International Journal of Computational Science and Engineering

This journal also publishes Open Access articles

Call for papers
Editor in Chief: Prof. Kuan-Ching Li
ISSN online: 1742-7193
ISSN print: 1742-7185
8 issues per year
Subscription price

 

Computational science and engineering is an emerging and promising discipline in shaping future research and development activities in academia and industry, in fields ranging from engineering, science, finance, and economics, to arts and humanities. New challenges arise in the modelling of complex systems, sophisticated algorithms, advanced scientific and engineering computing and associated (multidisciplinary) problem-solving environments. IJCSE addresses the state of the art of all aspects of computational science and engineering, highlighting computational methods and techniques for science and engineering applications.

 



 Topics covered include

  • Scientific and engineering computing, related/interdisciplinary applications
  • Problem-solving environments, complex systems
  • Advanced numerical computation and optimisation
  • Parallel and distributed computing
  • Programming models in GPU, multi/m any-core and cloud
  • Quantum computing technologies and applications
  • Distributed/federated information; knowledge management/discovery
  • Cyber security and cryptography
  • Performance modelling, evaluation and optimisation
  • Modelling/simulation, visualisation
  • Remote sensing and multi/hyperspectral imaging
  • Big data mining/applications, data analytics algorithms/applications
  • Machine learning, statistics, deep learning and artificial intelligence

More on this journal...
Objectives

The objective of IJCSE is to provide an outstanding channel for researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering advanced application of computational methods to sciences, engineering, arts and humanities fields, along with software developers and vendors, to contribute and to disseminate innovative and important new work, and to shape future directions for research, as well as to help industrial professionals apply various advanced computational techniques.


Readership

Scientists, engineers, researchers, graduate students, educators, programmers, industrial professionals and managers.


Contents

IJCSE is a refereed international journal providing an international forum to report, discuss and exchange experimental results, novel designs, work-in-progress, experience, case studies, and trend-setting ideas in the area of computational science and engineering. Papers should be of a quality that represents the state of the art in the field, bringing together the latest computing advances for scientific and engineering research, applications, and education, and stimulating future trends.


 

Hide

Browse issues

Vol. 15
Vol. 14
Vol. 13
Vol. 12
Vol. 11
Vol. 10

 

More volumes...

 

Get Permission
More on permissions

 IJCSE is indexed in:

 

 IJCSE is listed in:

 

Editor in Chief

  • Li, Kuan-Ching, Providence University, Taiwan
    (journaleditorialmanager@outlook.com)

      Executive Editor

      • Yang, Laurence T., St Francis Xavier University, Canada

      Associate Editors

      • Bhalla, Subhash, University of Aizu, Japan
      • Cheung, Yiu-Ming, Hong Kong Baptist University, Hong Kong SAR, China
      • Choo, Kim-Kwang Raymond, University of Texas at San Antonio, USA
      • De Mello, Rodrigo Fernandes, University of Sao Paulo, Brazil
      • Di Martino, Beniamino, Seconda Universitá di Napoli, Italy
      • Huang, Xinyi, Fujian Normal University, China
      • Juckeland, Guido, Helmholtz-Zentrum Dresden-Rossendorf e.V., Germany
      • Lu, Huimin, Kyushu Institute of Technology, Japan
      • Pathan, Al-Sakib Khan, Southeast University, Bangladesh

      Advisory Board

      • Bader, David A., Georgia Institute of Technology, USA
      • Bailey, David H., Lawrence Berkeley National Laboratory, USA
      • Bertino, Elisa, Purdue University, USA
      • Cao, Jiannong, Hong Kong Polytechnic University, Hong Kong SAR, China
      • Chang, Chein-I, University of Maryland, Baltimore County, USA
      • Chapman, Barbara, Stony Brook University, USA
      • Dongarra, Jack, University of Tennessee Knoxville, USA
      • Gaudiot, Jean-Luc, University of California – Irvine, USA
      • Gropp, William D., University of Illinois Urbana-Champaign, USA
      • Guo, Minyi, Shanghai Jiao Tong University, China
      • Hu, Bin, Lanzhou University, China
      • Jin, Hai, Huazhong University of Science and Technology, China
      • Keyes, David, Columbia University, USA
      • Li, Keqin, State University of New York at New Paltz, USA
      • Ling, Nam, Santa Clara University, USA
      • Pardalos, Panos M., University of Florida, USA
      • Pedrycz, Witold, University of Alberta, Canada
      • Xiang, Yang, Deakin University, Australia
      • Zomaya, Albert, University of Sydney, Australia

      Editorial Board Members

      • Benkner, Siegfried, University of Vienna, Austria
      • Castiglione, Aniello, University of Salerno, Italy
      • Chandrasekaran, Sunita, University of Delaware, USA
      • Chen, Jinjun, University of Technology, Sydney, Australia
      • De Sousa, Fabricio Simeoni, University of Sao Paulo (USP), Brazil
      • Dong, Mianxiong, Muroran Institute of Technology, Japan
      • Faragó, István, Eötvös Loránd University, Hungary
      • Gavrilova, Marina L., University of Calgary, Canada
      • Gorlatch, Sergei, Universität Münster, Germany
      • Kacsuk, Peter, Hungarian Academy of Sciences, Hungary
      • Katz, Daniel S., University of Illinois, USA
      • Kolodziej, Joanna, Cracow University of Technology, Poland
      • Liu, Yan, Concordia University, Canada
      • Lopez, Matthew Graham, Oak Ridge National Laboratory, USA
      • Luo, Xiangfeng, Shanghai University, China
      • Malyshkin, Victor, Russian Academy of Sciences, Russian Federation
      • Massetto, Francisco Isidro, Federal University of ABC, Brazil
      • Mehofer, Eduard, University of Vienna, Austria
      • Navaux, Philippe O. A., Federal University of Rio Grande do Sul (UFRGS), Brazil
      • Pop, Florin, University Politehnica of Bucharest, Romania
      • Ruede, Ulrich, University of Erlangen-Nürnberg, Germany
      • See, Simon, Nvidia, Singapore, Singapore
      • Shen, Jun, University of Wollongong, Australia
      • Strazdins, Peter, Australian National University, Australia
      • Susilo, Willy, University of Wollongong, Australia
      • Thulasiram, Ruppa K., University of Manitoba, Canada
      • Turek, Stefan, University of Dortmund, Germany
      • Wang, Bei, Princeton University, USA
      • Ylianttila, Mika, University of Oulu, Finland
      • Yuan, Juan-Ming, Providence University, Taiwan
      • Zhang, Shunxiang, Anhui University of Science and Technology, China
      • Zlatev, Zahari, Aarhus University, Denmark

       

      A few essentials for publishing in this journal

       

      • Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
      • Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
      • All our articles go through a double-blind review process.
      • All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
      • There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.

       

      Submission process

       

      All articles for this journal must be submitted using our online submissions system.

      Read our preparing and submitting articles page.

       

       

      Journal news

      • Forthcoming paper

         

        Title: High quality multi-core multi-level algorithm for community detection
        Authors: Suely Oliveira, Rahil Sharma
        Abstract: One of the most relevant and widely studied structural properties of networks is their community structure or clustering. Detecting communities is of great importance in various disciplines where systems are often represented as graphs. Different community detection algorithms have been introduced in the past few years, which look at the problem from different perspectives. Most of these algorithms, however, have expensive computational time that makes them impractical to use for large graphs found in the real world. Maintaining a good balance between the computational time and the quality of the communities discovered is a well known open problem in this area. In this paper, we propose a multi-core multi-level (MCML) community detection algorithm based on the topology of the graph, which contributes towards solving the above problem. MCML algorithm on two benchmark datasets results in detection of accurate communities. We detect high modularity communities by applying MCML on Facebook Forum dataset to find users with similar interests and Amazon product dataset. We also show the scalability of MCML on these large datasets with 16 Xeon Phi cores.

        More details...

      • Forthcoming paper

         

        Title: An improved indoor localisation algorithm based on wireless sensor network
        Authors: Min-Yi Guo, Chen Li, Jianzhong Wu, Jianping Cai, Zengwei Zheng, Jin Lv
        Abstract: Many sensor network applications require location awareness. In this paper, an improved positioning algorithm based on fingerprinting is presented for indoor environments. The improved algorithm compared with the traditional fingerprint recognition algorithm does not require offline fingerprint collection. The improved algorithm is robust in complex indoor environments and it can effectively deal with the situation of the failure of the beacon node. When there are new nodes added to the wireless sensor network, the algorithm will make use of the new nodes by generating new fingerprints to ensure the positioning performance of the algorithm.

        More details...

      • Forthcoming paper

         

        Title: Optimising the stiffness matrix integration of n-noded 3D finite elements
        Authors: J.C. Osorio, M. Cerrolaza, M. Perez
        Abstract: The integration of the stiffness and mass matrices in finite element analysis is a time-consuming task. When dealing with large problems having very fine discretisations, the finite element mesh becomes very large and several thousands of elements are usually needed. Moreover, when dealing with nonlinear dynamic problems, the CPU time required to obtain the solution increases dramatically because of the large number of times the global matrix should be computed and assembled. This is the reason why any reduction in computer time (even being small) when evaluating the problem matrices is of concern for engineers and analysts. The integration of the stiffness matrix of n-noded high-order hexahedral finite elements is carried out by taking advantage of some mathematical relations found among the nine terms of the nodal stiffness matrix, previously found for the more simple brick element. Significant time savings were obtained in the 20-noded finite element example case.

        More details...

      • Forthcoming paper

         

        Title: A cost-effective graph-based partitioning algorithm for a system of linear equations
        Authors: Hiroaki Yui, Satoshi Nishimura
        Abstract: There are many techniques for reducing the number of operations in directly solving a system of sparse linear equations. One such method is nested dissection (ND). In numerical analysis, the ND algorithm heuristically divides and conquers a system of linear equations, based on graph partitioning. In this article, we present a new algorithm for the first level of such graph partitioning, which splits a graph into two roughly equal-sized subgraphs. The algorithm runs in almost linear time. We evaluate and discuss the solving costs by applying the proposed algorithm to various matrices.

        More details...

      • Forthcoming paper

         

        Title: A comparative study of mixed least-squares FEMs for the incompressible Navier-Stokes equations
        Authors: Alexander Schwarz, Masoud Nickaeen, Serdar Serdas, Abderrahim Ouazzi, Jörg Schröder, Stefan Turek, Carina Nisters
        Abstract: In the present contribution we compare (quantitatively) different mixed least-squares finite element methods (LSFEMs) with respect to computational costs and accuracy. In detail, we consider an approach for Newtonian fluid flows, which are described by the incompressible Navier-Stokes equations. Various first-order systems are derived based on the residual forms of the equilibrium equation and the continuity condition. From these systems L^2-norm least-squares functionals are constructed, which are the basis for the associated minimisation problems. The first formulation under consideration is a div-grad first-order system resulting in a three-field formulation with total stresses, velocities, and pressure (S-V-P) as unknowns. Here, the variables are approximated in H(div) x H^1 x L^2 on triangles and in H^1 x H^1 x L^2 on quadrilaterals. In addition to that a reduced stress-velocity (S-V) formulation is derived and investigated. An advantage of this formulation is a smaller system matrix due to the absence of the pressure degree of freedom, which is eliminated in this approach. S-V-P and S-V formulations are promising approaches when the stresses are of special interest, e.g. for non-Newtonian, multiphase or turbulent flows. Furthermore, since in the total stress approach the pressure is approximated instead of its gradient, the proposed S-V-P formulation could be used in formulations with discontinuous pressure interpolation. For comparison the well-known first-order vorticity-velocity-pressure (V-V-P) formulation is investigated. In here, all unknowns are approximated in H^1 on quadrilaterals. Besides some numerical advantages, as e.g. an inherent symmetric structure of the system of equations and a directly available error estimator, it is known that least-squares methods have a drawback concerning mass conservation, especially when lower-order elements are used. Therefore, the main focus of the work is drawn to performance and accuracy aspects on the one side for finite elements with different interpolation orders and on the other side on the usage of efficient solvers, for instance of Krylov-space or multigrid type. Finally, two well-known benchmark problems are presented and the results are compared for different first-order formulations.

        More details...

      • Forthcoming paper

         

        Title: A semantic recommender algorithm for 3D model retrieval based on deep belief networks
        Authors: Li Chen, Hong Liu, Philip Moore
        Abstract: Interest in 3D modelling is growing; however, the retrieval results achieved for semantic-based 3D model retrieval systems have been disappointing. In this paper we propose a novel semantic recommendation algorithm based on a Deep Belief Network (DBN-SRA) to implement semantic retrieval with potential semantic correlations [between models] being achieved using deep learning form known model samples. The algorithm uses the feature correlation [between the models] as the conditions to enable semantic matching of 3D models to obtain the final recommended retrieval result. Our proposed approach has been shown to improve the effectiveness of 3D model retrieval, in terms of both retrieval time and, importantly, accuracy. Additionally, our study and our reported results suggest that our posited approach will generalise to recommender systems in other domains that are characterised by multiple feature relationships.

        More details...

      •  

        25 - 26 November 2017
        Kitakyushu, Japan


        Selected authors will be invited to elaborate on their research topic and submit the results to the journal.