A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities

Assessing applicants for faculty positions in universities involves many issues. Each issue may involve a judgment based on uncertain or imprecise data. The uncertainty in data may exist in the interpretation made by the evaluator. This issue might lead to improper decision making. Modeling such a system using fuzzy logic will provide a more efficient model for handling imprecision. This article presents a fuzzy system for modeling the assessment of applicants for employment at academic universities. This system will utilize a multi-stage fuzzy model for measuring and evaluating the applicants. Utilizing fuzzy logic for applicants' evaluation will help administrators in choosing the best candidates for faculty positions. The fuzzy system was developed using jFuzzyLogic Java library. The reliability of the proposed system was proved by evaluating real-world case studies to prove its effectiveness to mimic human judgment. Moreover, the developed system has been evaluated by comparing it with a traditional mathematical method to prove the credibility and fairness of the proposed fuzzy system.

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Driver's Behavior Modeling Using Fuzzy Logic

Mathematical Problems in Engineering ◽

10.1155/2010/172878 ◽

2010 ◽

Vol 2010 ◽

pp. 1-29 ◽

Cited By ~ 8

Author(s):

Sehraneh Ghaemi ◽

Sohrab Khanmohammadi ◽

Mohammadali Tinati

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Fuzzy Rules ◽

Behavior Modeling ◽

Linguistic Terms ◽

Steering Angle ◽

Hierarchical Fuzzy System

In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model calledModel Iis presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules calledModel IIandModel IIIby using Sugeno fuzzy inference.Model IIandModel IIIhave less linguistic terms thanModel Ifor the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

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Recent Advancement in Fuzzy System

Chaos and Complexity Theory in World Politics - Advances in Public Policy and Administration ◽

10.4018/978-1-4666-6070-0.ch010 ◽

2014 ◽

pp. 158-168

Author(s):

İ. Burhan Türkşen ◽

İbrahim Özkan

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Interdisciplinary Research ◽

Decision Process ◽

Fuzzy System ◽

Research Field ◽

Decision Under Uncertainty ◽

Recent Advancement ◽

Human Decision

Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative that best suites our needs. This process generally includes several areas of research including but not limited to Economics, Psychology, Philosophy, Mathematics, Statistics, etc. In this chapter the authors attempt to create a framework for uncertainties which surrounds the environment where human decision making takes place. For this purpose, the authors discuss how one ought to handle uncertainties within Fuzzy Logic. Furthermore, they present recent advances in Type 2 fuzzy system studies.

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Recent Advancement in Fuzzy System

Research Methods ◽

10.4018/978-1-4666-7456-1.ch020 ◽

2015 ◽

pp. 437-447

Author(s):

İ. Burhan Türkşen ◽

İbrahim Özkan

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Interdisciplinary Research ◽

Decision Process ◽

Fuzzy System ◽

Research Field ◽

Decision Under Uncertainty ◽

Recent Advancement ◽

Human Decision

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Development of the generic system of inference rules by knowledgebase

Программные системы и вычислительные методы ◽

10.7256/2454-0714.2021.1.34798 ◽

2021 ◽

pp. 1-9

Author(s):

Andrey Sergeevich Kopyrin ◽

Alina Olegovna Kopyrina

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy System ◽

Fuzzy Inference ◽

Structural Characteristics ◽

Fuzzy Cluster ◽

Inference Rules ◽

Digital Information ◽

Subject Field

The authors propose to align logical inference with the apparatus of fuzzy sets. When each solution is associated with a set of possible results with the known transitional probabilities, the solution is based on the digital information under uncertainty. Therefore, the main purpose of using fuzzy logic in expert systems consists in creation of computing devices (or software applications) that can imitate human-level reasoning and explain the techniques of decision-making. The goal of this research consists in detailed description of the reproducible standard method of setting rules of inference of the expert system for various economic subject fields, using a universal pattern of knowledgebase. For decision-making in a fuzzy system, the author suggests using the process of identification rule framework – determination of structural characteristics of fuzzy system, such as the number of fuzzy rules, number of linguistic terms the incoming variables are divided to. Such identification is conducted based on the fuzzy cluster analysis, using fuzzy decision trees. The authors present the structural chart of inference method on the basis of fuzzy logic. The presented in the article method of setting rules and fuzzy inference algorithm presented can be implemented in different areas of economics. The novelty of this work consists in automation and integration of the system for determination of fuzzy inference rules with the stage of input data collection in the subject field.

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Fuzzy model of the computer integrated decision support and management system in mineral processing

Yugoslav journal of operations research ◽

10.2298/yjor0802253m ◽

2008 ◽

Vol 18 (2) ◽

pp. 253-259 ◽

Cited By ~ 1

Author(s):

Igor Miljanovic ◽

Slobodan Vujic

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy Model ◽

Mineral Processing ◽

Original Model ◽

Integrated Systems ◽

Management Support ◽

Mineral Industry ◽

Fuzzy Models ◽

The Subject

During the research on the subject of computer integrated systems for decision making and management support in mineral processing based on fuzzy logic, realized at the Department of Applied Computing and System Engineering of the Faculty of Mining and Geology, University of Belgrade, for the needs of doctoral thesis of the first author, and wider demands of the mineral industry, the incompleteness of the developed and contemporary computer integrated systems fuzzy models was noticed. The paper presents an original model with the seven staged hierarchical monitoring-management structure, in which the shortcomings of the models utilized today were eliminated.

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Unification of imprecise data - translation of fuzzy to multi-valued knowledge over Y-axis

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.292459 ◽

2022 ◽

Vol 11 (1) ◽

pp. 0-0

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Numerical Study ◽

Knowledge Based Systems ◽

Membership Functions ◽

Imprecise Data ◽

Knowledge Based ◽

Data Translation ◽

Inference Systems ◽

Expert Reasoning

Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.

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An Evaluation of Total Project Risk Based on Fuzzy Logic

Verslas teorija ir praktika ◽

10.3846/btp.2016.534 ◽

2015 ◽

Vol 17 (1) ◽

pp. 23-31 ◽

Cited By ~ 9

Author(s):

Radek Doskočil

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy Model ◽

Empirical Method ◽

Project Risk ◽

Model Rule ◽

Decision Making Processes ◽

Project Risks ◽

Input Variables ◽

Total Project

The article deals with the use of fuzzy logic as a support of evaluation of total project risk. A brief description of actual project risk management, fuzzy set theory, fuzzy logic and the process of calculation is given. The major goal of this paper is to present am new expert decision-making fuzzy model for evaluating total project risk. This fuzzy model based on RIPRAN method. RIPRAN (RIsk PRoject ANalysis) method is an empirical method for the analysis of project risks. The Fuzzy Logic Toolbox in MATLAB software was used to create the decision-making fuzzy model. The advantage of the fuzzy model is the ability to transform the input variables The Number of Sub-Risks (NSR) and The Total Value of Sub-Risks (TVSR) to linguistic variables, as well as linguistic evaluation of the Total Value of Project Risk (TVPR) – output variable. With this approach it is possible to simulate the risk value and uncertainty that are always associated with real projects. The scheme of the model, rule block, attributes and their membership functions are mentioned in a case study. The use of fuzzy logic is a particular advantage in decision-making processes where description by algorithms is extremely difficult and criteria are multiplied.

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A Proposed Fuzzy Model for Diseases Diagnosis

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6419.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 4300-4304

Keyword(s):

Decision Making ◽

Fuzzy System ◽

System Approach ◽

Fuzzy Model ◽

Good Health ◽

Clinical Parameters ◽

Rule Based ◽

Medical Expertise ◽

Human Thinking ◽

The Right

Decision making has become a problem in environments full of uncertain, vague and imprecise information. They face many problems to train computer systems to simulate human thinking to make the right decision. Different methodologies and approaches have been used to train computers to understand and mimic human thinking. This paper proposes a fuzzy model for a bone disease to have the right diagnosis answer, as a human expertise doctor. and to prove that using fuzzy logic has a significant ability to mimic human thinking. The model accepts inputs in the different forms as physiological and clinical parameters and all data based on medical expertise, using a rule-based fuzzy system approach applied with fourteen rules to have final accurate output decisions. it has been tested in the orthopedic unit against the real existing diagnosis answer from expertise doctor and found that is capable of assisting medical experts in diagnosing diseases and provide good health services to their patients.

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Fuzzy Approach for Identifying Artistic Pigments with Raman Spectroscopy

Applied Spectroscopy ◽

10.1366/000370209788964412 ◽

2009 ◽

Vol 63 (8) ◽

pp. 947-957 ◽

Cited By ~ 5

Author(s):

R. Perez-Pueyo ◽

M. J. Soneira ◽

M. Castanys ◽

S. Ruiz-Moreno

Keyword(s):

Decision Making ◽

Raman Spectroscopy ◽

Fuzzy Logic ◽

Raman Spectrum ◽

Raman Spectra ◽

Fuzzy System ◽

Raman Band ◽

Decision Making Process ◽

Fuzzy Approach ◽

Spectrum Measurement

In this work, a fuzzy approach for automatically identifying artistic pigments from their Raman spectra is presented. The uncertainty introduced during the Raman spectrum measurement of pigments is considered in the design of the fuzzy system. The position of the Raman bands in the unknown spectrum can be subject to small displacements due to noise, misalignments in the calibration, etc. Fuzzy logic allows us to work with this uncertainty and to design a system based on the comparison between the Raman band positions in an unknown spectrum recorded from an artwork and the Raman band positions in spectra recorded from reference pigments gathered in databases. The fuzzy system provides the reference pigments whose Raman band positions match those of the unknown pigment analyzed and gives guidance to the decision-making process in the final identification.

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