Fuzzy Sets and Fuzzy Logic

Aliev, R. A.

doi:10.1007/978-3-642-34895-2_1

Cited by 9 publications

(10 citation statements)

References 95 publications

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“…The crisp set maps universe X to a set of two elements, which are 1 and 0. For any element x of universe X, characteristic function (x) equals 1 if x is an element of set A, and equals 0 if x is not an element in the set A [13,17,18,19].…”

Section: Crisp and Fuzzy Setsmentioning

confidence: 99%

“…For any element x of universe X, membership function is equal to the degree to which x is an element of set A. This degree is a value between 0 and 1, and represents the degree of membership of element x in the set A [13,17,18,19].…”

Section: Crisp and Fuzzy Setsmentioning

confidence: 99%

“…Assume that A and B are two fuzzy sets and x is an element in the universe of disclosure (X). Both fuzzy sets A and B are defined by their membership functions, which are and .The three basic operations of fuzzy sets are defined as [13,17,18,19]:…”

Section: Operations Of Fuzzy Setsmentioning

confidence: 99%

“…Classical sets represent a special type of fuzzy set, in which membership values are a subset of the interval [0, 1]. The common rules of the classical set theory are applied to fuzzy set theory as follows [13,17,18,19]:…”

Section: A1 Properties Of Fuzzy Setsmentioning

confidence: 99%

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Comparative analysis of classification models for diagnosis Type 2 Diabetes

Almadni

Abhari

2021

Preprint

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Diabetes mellitus type 2 has become one of the major causes of premature diseases and death in many countries. It accounts for the majority of diabetes cases around the world. Thus, we need to develop a system that diagnoses type 2 diabetes. In this thesis, a fuzzy expert system is proposed using the Mamdani fuzzy inference system to diagnose type 2 diabetes effectively. In order to evaluate the performance of our system, a comparative study has been initiated, and will contrast the proposed system with data mining algorithms, namely J48 Decision tree, multilayer perceptron, support vector machine, and Naïve Bayes. The developed fuzzy expert system and the data mining algorithms are validated with real data from the UCI machine learning datasets. Moreover, the performance of the fuzzy expert system is evaluated by comparing it to related work that used the Mamdani inference system to diagnose the incidence of type 2 diabetes. Alternate title: Comparative analysis of data mining algorithms for diagnosis Type 2 Diabetes

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Section: Crisp and Fuzzy Setsmentioning

confidence: 99%

Section: Crisp and Fuzzy Setsmentioning

confidence: 99%

Section: Operations Of Fuzzy Setsmentioning

confidence: 99%

Section: A1 Properties Of Fuzzy Setsmentioning

confidence: 99%

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Comparative analysis of classification models for diagnosis Type 2 Diabetes

Almadni

Abhari

2021

Preprint

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“…Since the system has a high number of inputs and tags, a hierarchical structure with several controllers was designed to keep the rules in a number easily manageable by the expert. The membership of a fuzzy set is defined by trapezoidal functions-as they adequately model the system behavior and are not computationally complex-where the conjunction and implication operators use the minimum T-norm [34]. Moreover, the defuzzification process uses the FITA method (i.e., First Inter, Then Aggregate) since it is more consistent than FATI [35] and the MVP (Maximum Value Point) weighting method, also called weighted average method [36].…”

Section: Fuzzymentioning

confidence: 99%

Design, Modelling, and Implementation of a Fuzzy Controller for an Intelligent Road Signaling System

Domínguez

Sanguino

2018

Complexity

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Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions). USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings-not regulated by semaphores-which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system's intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT.

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