Journal Information
Journal of Computational Science
Impact Factor:

Call For Papers
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.

The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.

This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.

Computational science typically unifies three distinct elements:

• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).

The Journal of Computational Science aims to be an international platform to exchange novel research results in simulation based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices.

The journal welcomes original, unpublished high quality contributions in the field of computational science at large, addressing one or more of the aforementioned elements.
Last updated by Dou Sun in 2016-09-25
Special Issues
Special Issue on Evolutionary Computational Intelligence Paradigms for Business Intelligence and Big data Analytics: Decision Making & Optimization
Submission Date: 2017-05-01

Aim and Scope: Presently, Evolutionary Computational Intelligence (CI) approaches are becoming an attractive research area due to its ability to deal with impreciseness, subjectivity, and knowledge uncertainty in decision making process. Moreover, CI approaches that has an ability to solve the complex business problem of today’s organization and can be considered in the context of global optimization. The concept of applying evolutionary CI paradigms (artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence, rough sets and etc) in business intelligence (BI) and big data analytics is feasible and sound. BI covers a variety of tools and methods that can aid the organizations in making effective decisions by analyzing their data. Besides, the business organizations have been handling huge volume of data that leads to be effective computing paradigms. Consequently, the use of evolutionary computational intelligence approaches entails that could add value to the organization for handling uncertain information in the decision making process than the traditional analysis and tools presently employed. Due to rapid advances in business processes, the organizations to meet desired needs, challenges that exploit the predictive power of computational intelligence approaches has been analyzed in depth for decision making and optimization. Thus, this special issue intends to facilitate the organizations for gaining the competitive advantage of marketplace via evolutionary computational intelligence approaches in business intelligence & big data analytics highlighted above. We invite researchers to contribute original research articles as well as review articles that will seek the continuing efforts to understand the recent trends of evolutionary algorithms can be used for business intelligence and big data analytics. Topics of Interest: We seek original and high quality submissions related to (but not limited to) one or more of the following topics: (Note that this special issue emphasizes "real world" applications) Fuzzy decision making in business intelligence and analytics Fuzzy logic for optimizing the success factors in business intelligence Fuzzy with data mining hybrid methods for BI & big data analytics Predicting business failures using rough sets Fuzzy and rough set data analysis for enterprise data analytic applications Applying probabilistic approaches towards rough set theory and their applications in BI & big data analytics Neural computation for business intelligence and big data analytics Computational intelligence versus statistical approaches for BI Evolving neuro and fuzzy systems for Predictive analysis of BI big data analytics Hybrid optimization algorithms for BI & big data analytics. Swarm intelligence and bio-inspired computation for BI applications big data management Convergence of CI solutions for BI process and big data performance management Artificial neural networks and its applications in business process modeling Evolutionary algorithms for strategic marketing Soft computational approaches for total quality management Business forecasting and expert systems with reference to CI Rough set model based on knowledge acquisition of market moments CI in meta-knowledge discovery and representation Industrial applications of business analytics and optimization Submission Instructions All submitted papers must be clearly written in excellent English and contain only original work and cutting-edges survey, which has not been published by or is currently under review for any other journal or conference. All papers submitted to this Special Issue will undergo the standard peer-review procedures of Journal of Computational Science. All manuscripts should be submitted through the Elsevier Editorial System: Authors should select "SI:ECI-BI-BD" when reaching step of selecting an article type name in this special issue submission process. For further information, please contact the leading guest editor of this special issue: Dr. Arun Kumar Sangaiah at Important Dates May 1, 2017: Deadline for paper submission August 1, 2017: Review notification October 1, 2017: Revised submissions due November 15, 2017: Second-round decision notification December 15, 2017: Final decision notification
Last updated by Dou Sun in 2017-03-05
Special Issue on Information Diffusion in Online Social Networks (IDOSN)
Submission Date: 2017-05-15

Information diffusion research originates from the study of the spread of infectious disease among a population. The process of diffusion of various types of information, like technological innovations, news, topics, and opinions, is described as a contagion that spreads from node to node like an epidemic. As online social networks (OSN) are emerging and flourishing, massive amounts of data are produced and consumed in a rapid rate. This rapid production and consumption of large-scale OSN data bring two new challenges to the study of information diffusion. First, events (topics), issues, rumors, etc. happen and evolve very quickly in OSN. Analyzing and modeling this fast diffusion of these types of information, and eventually detecting and predicting diffusion outbreaks, have attracted a great deal of research interests. Second, due to the huge volume of OSN big data, researchers have recently focused on how to extract valuable information from OSN big data to analyze diffusion and on how to accelerate computation speed when dealing with large-scale diffusion network. Information diffusion in OSN plays a fundamental role in the settings that include the spread of technological innovations, word of mouth effects in marketing, and the spread of news, topics, and opinions. The study of information diffusion in OSN has important implications on online marketing, information retrieval, caching and recommendation systems, and rumor containment. This special issue aims to push the state of the art in all facets of information diffusion in online social networks, in order to develop innovative ideas fostering the design of the new generation of social network platforms and their services. Special attention is expected in how to deal with OSN big data for studying information diffusion. Furthermore, we encourage interdisciplinary research to engender cross-fertilization of different disciplines (computer science, sociology, and anthropology). Topics of interest include, but are not limited to: Information diffusion analysis and modeling Diffusion source identification and locating User behavior and influence analysis Network structure and community evolution analysis Popularity evolution analysis and prediction Topic evolution tracking and modeling Event tracking and detection Classification, ranking, summarization, and recommendation for information diffusion Large-scale diffusion network analysis Information retrieval in OSN OSN big data analysis and management OSN data parallel computing OSN virtual data management and processing Submission Instructions All submitted papers must be clearly written in English and contain only original work, which has not been published by or is currently under review for any other journal or conference. Authors should prepare their manuscript according to the Instructions for Authors available from the online submission page of Journal of Computational Science. All papers submitted to this Special Issue will undergo the standard peer-review procedures of Journal of Computational Science. All manuscripts should be submitted through the Elsevier Editorial System: Authors should select "SI: IDOSN" when reaching step of selecting an article type name in this special issue submission process. For further information, please contact the guest editor of this special issue: Dr. Ying Hu at Important Dates Full paper submission deadline: May 15th, 2017 First round decision: Aug 15th, 2017 Final decision: Dec 15th, 2017 Expected publication: Spring 2018
Last updated by Dou Sun in 2017-03-05
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