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Gholam Ali Montazer

Bio: Gholam Ali Montazer is an academic researcher from Tarbiat Modares University. The author has contributed to research in topics: Artificial neural network & Cluster analysis. The author has an hindex of 19, co-authored 95 publications receiving 1351 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper discusses the architecture of a fuzzy system including both modules, utilizing fuzzy concept for dealing with the uncertainty of the problem, and has been applied to a real case of vendor selection process in one of the greatest and the most famous companies in the Iranian oil industry, OIEC.
Abstract: Any decision process deals with two different concerns as its cornerstones, evaluating the alternatives and ranking them based on their performances. In any decision process, the former phase is usually the premise of the latter one. Alternatives' evaluation is the concept that largely depends on the experts and their expertise, which increase uncertainty in the decision-making process. In addition to all proposed methods for having the experts' knowledge as evaluations of the alternatives, utilizing expert decision support systems (EDSS) can be a sensible response to such a need. Having evaluated the alternatives in the first phase of a decision-making process, the second phase of the process deals with the ranking the alternatives based on their performances obtained from the first phase. In this paper, we discuss the architecture of a fuzzy system including both modules, utilizing fuzzy concept for dealing with the uncertainty of the problem. Concerning the problem we had been dealt with, our system comprises a fuzzy evaluation module, which is a fuzzy expert system and an appropriate tool for evaluating the existing alternatives promptly and smoothly, without the imposed time delays by the experts to propose their comments and the uncertainty of such expertise-based comments, and a fuzzy ranking module, which is a fuzzy version of ELECTRE III method ranking the alternatives based on their outranking relations and by considering the existing uncertainty in their performances. This way the final ranking is resulted from an independent fuzzy system, which has considered the existing uncertainty in the evaluations not once but twice. Our proposed system has been applied to a real case of vendor selection process in one of the greatest and the most famous companies in the Iranian oil industry, OIEC, and the results are discussed.

145 citations

Journal ArticleDOI
TL;DR: This investigation shows that both frameworks have powerful capabilities to cope with the uncertainty in the medical pattern recognition problems, but, IFSs yield better detection rate as a result of more accurate modeling which is involved with incurring more computational cost.

132 citations

Journal ArticleDOI
TL;DR: A fuzzy expert system for selecting superior stocks in order to encounter the uncertainty of stock portfolio recommendation is developed and the recommendation rules which experts at Tehran Stock Exchange (TSE) use for portfolio recommendation are modeled.
Abstract: The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria that often are vague and have uncertainty conditions. Therefore, we are faced with a special Multiple Criteria Decision Making (MCDM) problem. The purpose of this paper is to develop a fuzzy expert system for selecting superior stocks in order to encounter the uncertainty of stock portfolio recommendation and model the recommendation rules which experts at Tehran Stock Exchange (TSE) use for portfolio recommendation. The results of implementing the designed fuzzy expert system at TSE were affirmative.

110 citations

Journal ArticleDOI
TL;DR: A novel inference engine named fuzzy-evidential hybrid inference engine has been proposed using Dempster-Shafer theory of evidence and fuzzy sets theory that models the information's vagueness and decision making's uncertainty precisely and through information fusion, provides more accurate results.
Abstract: In many engineering problems, we encounter vagueness in information and uncertainty in decision making, so as these phenomena cause we could not reach to certain results for our proposed solution. In this paper, a novel inference engine named fuzzy-evidential hybrid inference engine has been proposed using Dempster-Shafer theory of evidence and fuzzy sets theory. This hybrid engine operates in two phases. In the first phase, it models the input information's vagueness through fuzzy sets. In following, extracting the fuzzy rule set for the problem, it applies the fuzzy inference rules on the acquired fuzzy sets to produce the first phase results. At second phase, the acquired results of previous stage are assumed as basic beliefs for the problem propositions and in this way, the belief and plausibility functions (or the belief interval) are set. Gathering information from different sources, they provide us with diverse basic beliefs which should be fused to produce an integrative result. For this purpose, evidential combination rules are used to perform the information fusion. Having applied the proposed engine on the coronary heart disease (CHD) risk assessment, it has yielded 91.58% accuracy rate for its correct prediction. This hybrid engine models the information's vagueness and decision making's uncertainty precisely and through information fusion, provides more accurate results, so as it could be considered as an intelligent decision support system in diverse engineering problems.

86 citations

Journal ArticleDOI
TL;DR: According to the findings of this research, the multi-modal emotion recognition systems through information fusion as facial expressions, body gestures and user's messages provide better efficiency than the single- modal ones.

73 citations


Cited by
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01 Jan 2002

9,314 citations

01 Jan 2006

3,012 citations

Journal ArticleDOI
TL;DR: A systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection is provided by using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches.
Abstract: Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.

825 citations