Special Issue on Data-driven Decision Making - Theory, Methods, and Applications
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
• 小类 : 计算机：跨学科应用 - 2区
Data-driven decision making approaches have been widely used in emergency response, medicine, manufacturing, renewable energy, and so on. Businesses generally use a wide range of tools to get useful information from big data, and to present it in ways that back up decisions. They form a hot research topic owing to their importance and effectiveness in addressing aspects of uncertainty and incompleteness of data. Moreover, in some cases the incomplete information about the consequences of the alternatives can be tackled by means of the theory of Soft Computing (SC), Data Mining (DM) and Artificial Intelligence (AI). Now and in the future, data-driven decision making under uncertainty and incompleteness would be a quite promising research line representing a high quality breakthrough in this topic.
The objective of this special issue is to explore latest up-to-date methods of SC, DM and AI, and their applications in data-driven decision making under uncertainty and incompleteness environment. Both theoretical and applied results with applications are sought for. It offers a concentrative venue for researchers to make rapid exchange of ideas and original research findings in data-driven decision making problems. In particular, new interdisciplinary approaches in SC, DM and AI for decision making theories and applications, or strong conceptual foundation in newly evolving topics are especially welcome.
TOPICS OF INTEREST FOR THE SPECIAL ISSUE
We invite researchers and experts worldwide to submit high-quality original research papers and critical survey articles on the following potential topics and their applications, but are not limited to:
Big (small) data analysis for decision making
Soft Computing and its applications in decision making
Nature-inspired optimization for decision making
Fuzzy Multiple Criteria/Objective Decision Making
Consensus and cooperation for decision making
Rough Sets and its applications
Fuzzy set qualitative comparative analysis
Decision making with incomplete / uncertain systems