Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Fuzzy Decision Tree-based Inference System for Liver Disease Diagnosis

Medical diagnosis can be challenging because of a number of factors. Uncertainty in the diagnosis process arises from inaccuracy in the measurement of patient attributes, missing attribute data and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. Given this situation, a decision support system, which can help doctors come up with a more reliable diagnosis, can have a lot of potential. Decision trees are used in data mining for classification and regression. They are simple to understand and interpret as they can be visualized. But, one of the disadvantages of decision tree algorithms is that they deal with only crisp or exact values for data. Fuzzy logic is described as logic that is used to describe and formalize fuzzy or inexact information and perform reasoning using such information. Although both decision trees and fuzzy rule-based systems have been used for medical diagnosis, there have been few attempts to use fuzzy decision trees in combination with fuzzy rules. This study explored the application of fuzzy logic to help diagnose liver diseases based on blood test results. In this project, inference systems aimed at classifying patient data using a fuzzy decision tree and a fuzzy rule-based system were designed and implemented. Fuzzy decision tree was used to generate rules that formed the rule-base for the diagnostic inference system. Results from this study indicate that for the specific patient data set used in this experiment, the fuzzy decision tree-based inferencing out performed both the crisp decision tree and the fuzzy rule-based inferencing in classification accuracy

FUZZY LOGIC AND ITS APPLICATION: A BRIEF REVIEW

The goal of this work is to make a brief review on Fuzzy Logic along with its usefulness in several areas such as pattern recognition, control systems, knowledge-based systems, and medical diagnosis. Fuzzy Logic provides support in addressing imprecision, uncertainty, and vagueness etc, to make formalization of human reasoning. Because of its nature, it is widely accepted as a method of imitating the way of decision making in human thinking and natural language

Cloud Based Intelligent Decision Support System for Disaster Management Using Fuzzy Logic

Field of cloud computing is an emerging field in computer science. Computational intelligence and Decision support systems (DSS) have to gain concern as a computing solution to planned and unplanned problems of organizations in order to progress decision-making tasks in a better way. In today era, Disaster management is a big problem. To overcome this problem, a real time computation is required. Cloud computing is a tool to offer promising support to decision support system in a real time environment. In this paper, a fuzzy based decision support system is proposed to meet all the requirements using fuzzy logic inference system

The Fuzzy-Neuro Classifier for Decision Support

This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem

Cloud Based Intelligent Decision Support System for Disaster Management Using Fuzzy Logic

Field of cloud computing is an emerging field in computer science. Computational intelligence and Decisions Supports Systems (DSS) have to gained concerns as a computing solution to planned and unplanned problems of organizations in order to progress decision-making tasks in a better way. In today era, Disaster management is a big problem. To overcome this problem, a real time computation is required. Cloud computing is a tool to offer promising support to decision support system in a real time environment. In this paper, a fuzzy based decision support system is proposed to meet all the requirements using fuzzy logic inference system

Ідентифікація моделі медичної системи на базі нечіткої логіки

В роботі розглядається формалізація вхідної інформації при діагностиці неврологічних захворювань. Проаналізовано можливість застосування методів нечіткої логіки і штучних нейронних мереж. Виконана структурна та параметрична ідентифікація моделі медичної системи на базі нечіткої логіки для побудови комп’ютерної системи підтримки прийняття рішення при діагностиці неврологічних захворюваньIn this paper we consider the formalization of the initial information for the diagnosis of neurological diseases. The possibility of application of fuzzy logic and artificial neural networks. Performed structural and parametric identification of a model of health systems based on fuzzy logic to build a computer decision support system for the diagnosis of neurological disease

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

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

The development of a fuzzy expert system to help top decision makers in political and investment domains

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThe world’s increasing interconnectedness and the recent increase in the number of notable regional and international events pose greater and greater challenges for political decision-making, especially the decision to strengthen bilateral economic relationships between friendly nations. Typically, such critical decisions are influenced by certain factors and variables that are based on heterogeneous and vague information that exists in different domains. A serious problem that the decision-maker faces is the difficulty in building efficient political decision support systems (DSS) with heterogeneous factors. One must take many factors into account, for example, language (natural or human language), the availability, or lack thereof, of precise data (vague information), and possible consequences (rule conclusions). The basic concept is a linguistic variable whose values are words rather than numbers and are therefore closer to human intuition. A common language is thus needed to describe such information which requires human knowledge for interpretation. To achieve robustness and efficiency of interpretation, we need to apply a method that can be used to generate high-level knowledge and information integration. Fuzzy logic is based on natural language and is tolerant of imprecise data. Fuzzy logic’s greatest strength lies in its ability to handle imprecise data, and it is perfectly suited for this situation. In this thesis, we propose to use ontology to integrate the scattered information resources from the political and investment domains. The process started with understanding each concept and extracting key ideas and relationships between sets of information by constructing object paradigm ontology. Re-engineering according to the object-paradigm (OP) provided quality for the developed ontology where conceptualization can provide more expressive, reusable object and temporal ontology. Then fuzzy logic has been integrated with ontology. And a fuzzy ontology membership value that reflects the strength of an inter-concept relationship to represent pairs of concepts across ontology has been consistently used. Each concept is assigned a fixed numerical value representing the concept consistency. Concept consistency is computed as a function of strength of all the relationships associated with the concept. Fuzzy expert systems enable one to weigh the consequences (rule conclusions) of certain choices based on vague information. Rule conclusions follow from rules composed of two parts, the if antecedent (input) and the then consequent (output). With fuzzy expert systems, one uses fuzzy logic toolbox graphical user interface (GUI) tools to build up a fuzzy inference system (FIS) to aid in decision-making. This research includes four main phases to develop a prototype architecture for an intelligent DSS that can help top political decision makers