Journal Information
Expert Systems
http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291468-0394
Impact Factor:
0.947
Publisher:
John Wiley & Sons
ISSN:
1468-0394
Viewed:
1279
Tracked:
1

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Call For Papers
Aims and Scope

Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper.

As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.

Keywords

knowledge engineering, artificial intelligence, expert systems, big data, natural language processing, machine vision, analysis, pervasive computing, computational models for AI, hybrid intelligent systems

Abstracting and Indexing Information

    ABI/INFORM Database (ProQuest)
    Academic Search (EBSCO Publishing)
    Academic Search Alumni Edition (EBSCO Publishing)
    Academic Search Premier (EBSCO Publishing)
    Applied Science & Technology Index/Abstracts (EBSCO Publishing)
    CompuMath Citation Index (Thomson Reuters)
    Computer & Information Systems Abstracts (ProQuest)
    Computer Abstracts (Emerald)
    Computer Science Index (EBSCO Publishing)
    CSA Environmental Sciences & Pollution Management Database (ProQuest)
    Current Contents: Engineering, Computing & Technology (Thomson Reuters)
    Ergonomics Abstracts (EBSCO Publishing)
    GeoRef (AGI)
    InfoTrac (GALE Cengage)
    INSPEC (IET)
    Journal Citation Reports/Science Edition (Thomson Reuters)
    LISA: Library & Information Science Abstracts (ProQuest)
    ProQuest Central (ProQuest)
    PsycINFO/Psychological Abstracts (APA)
    Science Citation Index Expanded (Thomson Reuters)
    SCOPUS (Elsevier)
    The DBLP Computer Science Bibliography (University of Trier) 
Last updated by Dou Sun in 2017-05-17
Special Issues
Special Issue on New Data Envelopment Analysis Models for Assessing Sustainability
Submission Date: 2017-12-30

Sustainability implies business resiliency over time through robust economic, social and environmental systems. Outcomes of sustainable business practices include the creation of economic value, healthy ecosystems and strong communities. Sustainable business practices are fostered through engagement with stakeholders, effective environmental management systems and good governance, underpinned by effective measurement and evaluation. Recently, new data envelopment analysis (DEA) models have been developed to assess sustainability issues. However, these attempts are scarce and we need to focus more on this very important topic. In this special issue we look for ground-breaking papers on new DEA models to assess sustainability of settings (decision making units). This will help managers to manage their organisations in more sustainable ways. This special issue is an excellent opportunity for scholars to publish their ideas in DEA for assessing sustainability and also it will be very useful for practitioners to understand uses of DEA in sustainability evaluation. Interested scholars are encouraged to develop new DEA models and give a case study in the area of sustainability. In other words, authors should not only develop new DEA models but also should apply their models in a real settings to show efficacy of their proposed models. We do not accept the manuscripts with just application of DEA in sustainability assessment. Also, we do not accept manuscripts without case study. Contributed papers may deal with but are not limited to: - Black box DEA models - Network DEA models - Dynamic DEA models - Chance-constraint DEA models - Imprecise DEA models - Fuzzy DEA models - DEA-discriminant analysis (DEA-DA) models
Last updated by Dou Sun in 2017-05-17
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