ISSN 2721-5792 (media online)
Accreditation : Non Sinta Index
Indexing : Crossref, Directory of Open Access Journals (DOAJ), GARUDA (Garba Rujukan Digital), Google Scholar, Index Copernicus International (ICI), SCOPUS, SINTA (Science and Technology Index), Web of Science (WOS)
Organizer : Institute of Computer Science (IOCS)
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Phone : 0281381251442
About the journal
An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston in the late 1970s. Examples of specialized intelligent decision support systems include Flexible manufacturing systems (FMS),intelligent marketing decision support systems and medical diagnosis systems. Ideally, an intelligent decision support system should behave like a human consultant: supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions. The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible. Many IDSS implementations are based on expert systems,a well established type of KBS that encode knowledge and emulate the cognitive behaviours of human experts using predicate logic rules, and have been shown to perform better than the original human experts in some circumstances. Expert systems emerged as practical applications in the 1980s based on research in artificial intelligence performed during the late 1960s and early 1970s. They typically combine knowledge of a particular application domain with an inference capability to enable the system to propose decisions or diagnoses. Accuracy and consistency can be comparable to (or even exceed) that of human experts when the decision parameters are well known (e.g. if a common disease is being diagnosed), but performance can be poor when novel or uncertain circumstances arise. Research in AI focused on enabling systems to respond to novelty and uncertainty in more flexible ways is starting to be used in IDSS. For example, intelligent agents that perform complex cognitive tasks without any need for human intervention have been used in a range of decision support applications. Capabilities of these intelligent agents include knowledge sharing, machine learning, data mining, and automated inference. A range of AI techniques such as case based reasoning, rough sets and fuzzy logic have also been used to enable decision support systems to perform better in uncertain conditions.

Aim & Scope
Aggregation of preferences based on soft computing Applications of OR techniques Applications of the intelligent decision support systems as in supplier selection, ensemble classifiers, portfolio selection, resource allocation, social networks, and Web. Artificial Intelligence Business intelligence Cognitive sciences Collaborative decision making Comparison analysis regarding different intelligent decision making models Computational Intelligence Computing and information technologies Consistency issues in preference modeling Context awareness, modeling, and management for DMSS Continuous and discrete optimization Data warehousing, online analytical processing and big data analytics Datamining