Special Issue "Computational Web Intelligence"
A special issue of Information (ISSN 2078-2489).
Deadline for manuscript submissions: closed (15 June 2014)
With the explosive growth of Web data, a challenging problem for a new generation of intelligent techniques is how to handle uncertain Web data and how to make the right decisions under Web uncertainty. The uncertain nature of the Web calls for more powerful intelligent techniques, capable of dealing with vagueness and imprecision in Web data. In addition, in a highly varying and decentralized environment like the Web, efficient computational approaches are required to cope with the complexity of different problems arising from the activity of users on the Web. Typical examples include Web-scale query answering and reasoning, distributed data storage and massive data analysis. These insights have triggered a lot of research aimed at exploiting the potential of Computational Intelligence (CI), which is a set of nature-inspired computational approaches primarily including fuzzy logic, neural networks, evolutionary computation, granular computing, rough sets and probabilistic methods. Along with this idea, Computational Web Intelligence (CWI) has emerged as a hybrid technology using CI and Web Technology (WT) to make intelligent Web applications. Briefly speaking, the concise relation is given by CWI=CI+WT. Of course, developing CWI applications is not as straightforward as one might anticipate. Adapting existing CI solutions may not always be appropriate for intelligent applications on the Web, but when it is, the solutions should incorporate learning mechanisms that will scale to the Web, adapt to individual user requirements, and personalize interfaces. The number of problems requiring Web-specific solutions is large, and solutions will require a sustained complementary effort to advance fundamental CI research and to incorporate CI components into every Web application.
The goal of this special issue is to provide the interested reader with a collection of papers describing recent developments in Computational Web Intelligence. Topics of interest include, but are not limited to:
- Fuzzy logic for Web applications
- Neural networks for Web applications
- Nvolutionary computation for Web applications
- Granular computing for Web applications
- Rough sets for Web applications
- Probabilistic methods for Web applications
Prof. Dr. Giovanna Castellano
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Fuzzy logic for Web Intelligence
- Neural networks for Web Intelligence
- Evolutionary computation for Web Intelligence
- Granular computing for Web Intelligence
- Rough sets for Web Intelligence
- Probabilistic methods for Web Intelligence