Knowledge-Based Systems

Knowledge-Based Systems - ISSN 0950-7051
Source Normalized Impact per Paper (SNIP): 2.902 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 1.754 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 5.921 (2019) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
5 Year Impact Factor: 6.075 (2019) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2016 to the previous five years and divided by the source items published in the previous five years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
Volumes: Volumes 211-234
Issues: 1 issue
ISSN: 09507051

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Description



Knowledge-based Systems is an international and interdisciplinary journal in the field of artificial intelligence. The journal will publish original, innovative and creative research results in the field, and is designed to focus on research in knowledge-based and other artificial intelligence techniques-based systems with the following objectives and capabilities: to support human prediction and decision-making through data science and computation techniques; to provide a balanced coverage of both theory and practical study in the field; and to encourage new development and implementation of knowledge-based intelligence models, methods, systems, and software tools, with applications in business, government, education, engineering and healthcare.



This journal's current leading topics are but not limited to:

  • Machine learning theory, methodology and algorithms
  • Data science theory, methodologies and techniques
  • Knowledge presentation and engineering
  • Recommender systems and E-service personalization
  • Intelligent decision support systems, prediction systems and warning systems
  • Computational Intelligence systems
  • Data-driven optimization
  • Cognitive interaction and brain–computer interface
  • Knowledge-based computer vision techniques



Special Issue Instructions

Knowledge-based Systems (KBS), an international and interdisciplinary peer-reviewed academic journal in the field of artificial intelligence, welcomes the submission of special issues on timely topics within the scope of the journal. The main objectives of the journal to organize special issues are to bring together state-of-the-art and high-quality research works, to promote key advances in the science and applications in the important field of knowledge-based systems, and to drive emerging research topics and establish flagships in the field.



How to submit your Special Issue proposal:

  • Check the selection criteria below for a KBS special issue to make sure your proposal is relevant to the journal,
  • Write your special issue proposal in the structure given below,
  • Submit the special issue proposal to the Editor-in-Chief (EiC),
  • The EiC and KBS special issue assessment panel will then review your proposal and reply with their decision.



Guest Editors' Duty and Special Issue Process:

After a special issue proposal is accepted by the journal, a call for papers can be formally distributed. All the papers submitted to the special issue will undergo a peer review process. Guest Editors will manage the process and ensure that the reviewing standards for Knowledge-Based Systems regular issues are maintained. A Managing Guest Editor, who will be responsible for distributing submissions to the other Guest Editors, will need to be nominated. After the Guest Editors make recommendations on each paper in the special issue, the EiC will make the final decisions of acceptance for publication. After all papers to be included in the Special Issue are accepted, the Guest Editors will be responsible for either preparing an Editorial (1–2 pages in length) or writing a field survey (5–10 pages in length), which will incorporate the selected papers and related literature relevant to the topic of the special issue.

Reproducibility Badge Initiative and Software Publication

Reproducibility Badge Initiative (RBI) is a collaboration with Code Ocean (CO), a cloud based computational reproducibility platform that helps the community by enabling sharing of code and data as a resource for non-commercial use. CO verifies the submitted code (and data) and certifies its reproducibility. Code submission will be verified by the Code Ocean team for computational reproducibility by making sure it runs, delivers results and it is self-contained. For more information please visit this help article. Note that an accepted paper will be published independently of the CO application outcome. However, if the paper receives the Reproducibility badge, it will be given additional exposure by having an attached R Badge, and by being citable at the CO website with a DOI.

We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs.

For more information contact us at: software.impacts@elsevier.com