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The journal welcomes original, unpublished high quality contributions in the field of computational science at large, addressing one or more of the aforementioned elements.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 2017-08-05
Special Issues
Special Issue on Computational Intelligence Paradigms in Recommender Systems and Online Social Networks
Submission Date: 2017-12-30

Computational Intelligence encompasses a number of nature-inspired computational methodologies, mainly artificial neural networks (ANNs), fuzzy sets, genetic algorithms (GAs), and their hybridizations, such as neuro-fuzzy computing and neo-fuzzy systems, for addressing real-world problems to which conventional modelling can be useless due to several reasons such as complexity, existent of uncertainties, and the stochastic nature of the processes. Computational Intelligence is a powerful methodology for a wide range of data analysis problems such as financial forecasting, industrial, scientific, and social media applications. The recent advances in computational intelligence have shown very promising results in industry, business, sciences and social media studies. Meanwhile, the online social networks (OSNs) such as Facebook, LinkedIn, Twitter, and Instagram have become very popular and attracted many users from all around the world. Recommender systems in combination with OSNs have also produced new business opportunities, making the social impact of OSNs more critical for product marketing, establishing new connections and improving the user’s experience by personalization of the user’s contents. This has led to new diverse challenges for practitioners and researchers of OSNs and recommender systems in terms of large-scale social network interactions and diversity of social media data from a multitude of OSNs. Given the success of computational intelligence methods and techniques in big data analysis applications, it is expected that they can also be applied successfully in the analysis of large-scale raw data in OSNs. In this context, computational intelligence paradigms comprising of numerous branches including neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems, can play a vital role in handling the different aspects of OSNs and recommender systems. In this special issue, we invite researchers to contribute high-quality articles and surveys focusing on computational intelligence methods for recommenders systems and OSNs. The relevant topics of this special issue include but are not limited to: - Computational intelligence solutions for OSNs and recommendation in recommender systems - Computational intelligence in mobile-cloud based computing for social network recommendation services - Big data analytics for community activity prediction, management, and decision-making in OSNs - Fuzzy system theory in OSNs and recommender systems - Social data analytical approaches using computational methods - Deep learning and machine learning algorithms for efficient indexing and retrieval in multimedia recommendation systems and OSNs - Intelligent techniques for smart surveillance and security in OSNs - Modeling, data mining, and public opinion analysis based on social big data - Crowd computing-assisted access control and digital rights management for OSNs - Evolutionary algorithms for data analysis and recommendations - Crowd intelligence and computing paradigms for sentimental analysis and recommendation - Applied soft computing for content security, vulnerability and forensics in OSNs - Computational intelligence in multimedia computing and context-aware recommendation - Scalable, incremental learning and understanding of OSN big data with its real-world applications for visualization, HCI, and virtual reality community - Crowd intelligence-assisted ubiquitous, personal, and mobile social media applications - Recommender systems for crowdsourcing and privacy preserving crowdsourcing - Crowdsourcing and crowd sensing based on OSN and its applications for trust evaluation - Artificial intelligence and pattern recognition technologies for recommendation in healthcare - Deep learning and computational intelligence based medical data analysis for recommendation and smart healthcare services
Last updated by Dou Sun in 2017-08-05
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