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State of the Art of Artificial Intelligence in Dentistry and Its Expected Future
Published in Lavanya Sharma, Mukesh Carpenter, Computer Vision and Internet of Things, 2022
Vukoman Jokanović, M. Živković, S. Živković
Fuzzy logic is an approach to computing based on the degrees of truth, rather than the usual true or false (1 or 0) logic. Therefore, it mimics human behavior, giving a certain result as partially true or false. It assumes that each output belongs somewhere between true and false, with different shades of green and blue instead of a completely green and blue. Fuzzy learning is a kind of AI used in medicine to diagnose, for example, diabetic neuropathy, or determine the required dose of the drug, based on the calculation of the brain volume, using MRI images. Based on such an approach, it is also possible to characterize ultrasound images or CT images of certain organs of the human body. Because these information are between partially correct and partially incorrect values, this method has limited application [19,21,33].
Routing in 3D UAV Swarm Networks
Published in Fei Hu, Xin-Lin Huang, DongXiu Ou, UAV Swarm Networks, 2020
Katelyn Isbell, Yang-Ki Hong, Fei Hu
In [15], the authors proposed dynamic 3D fuzzy routing based on traffic probability (DFTRP) to increase network lifetime and improve success rate of packet delivery. DFTRP uses hop-to-hop delivery in which the message is transmitted to a neighboring node based on fuzzy logic and local information until it reaches the destination. Fuzzy logic differs from Boolean logic in that it has degrees of “truth” rather than “false” or “true”. It uses the concept of human intuition to make decisions. An inference engine takes inputs, applies a fuzzy rule base, and produces outputs. A fuzzy set represents the relationship between an uncertain quantity x and a membership function (MF) μ in the range [0,1]. The fuzzy set A in the universe of discourse U can be represented by a set of ordered pairs such that A={(x,μA(x))|x∈U}
Dynamic fuzzy systems modeling
Published in Adedeji B. Badiru, Systems Engineering Models, 2019
Fuzzy Logic (and reasoning) is a scientific methodology for handling uncertainty and imprecision. Unlike in conventional (crisp) sets, the members of fuzzy sets are permitted varying degrees of membership. An element can belong to different fuzzy sets with varying membership grade in each set. The main advantage of fuzzy sets is that it allows classification and gradation to be expressed in a more natural language; this modeling concept is a useful technique when reasoning in uncertain circumstances or with inexact information that is typical of human situations. Fuzzy models are constructed based on expert knowledge rather than on pure mathematical knowledge; therefore, they are both quantitative and qualitative, but are considered to be more qualitative than quantitative. Therefore, a fuzzy expert system is a computer based decision tool that manipulates imprecise inputs based on the knowledge of an expert in that domain.
A novel image segmentation utilizing FUZZY-based LBP and active contour model
Published in The Imaging Science Journal, 2022
Mojtaba Sajadi, Mohammad Bagher Tavakoli, Farbod Setoudeh, Amir Hossein Salemi
Fuzzy logic is based on the concept of fuzzy sets in which there are no clear boundaries. Unlike Boolean dual-value logic, fuzzy logic is multi-value and deals with membership degrees and degrees of accuracy. Fuzzy logic uses any logical value from a set of real numbers between 0 (completely false) to 1 (completely true), known as its membership value, and the function that represents these values is called the membership function. Fuzzy logic begins with the concept of a fuzzy set. A fuzzy set is a boundless set. This can include elements that have only a minor membership rating. This is a good way to show vague concepts (e.g. fast runners, hot weather, tall men, etc.) that are very common in linguistics and form the basis of human reasoning. A membership function (MF) is a function that determines how each point of the input space is mapped to a membership value (or membership degree) between 0 and 1. The input space is commonly known as the speech world. A fuzzy set is a classic set. If X is a world of discourse and its elements are denoted by x, a fuzzy set A in X is defined as a set of ordered pairs: A = {x, μA (x) │xεX}, Where μA (x) is the membership function (MF), x is called x in A. The membership function plots each element X to the membership value between 0 and 1. Triangular, bell and Gaussian functions are common types of membership functions:
Integrated innovative product design and supply chain tactical planning within a blockchain platform
Published in International Journal of Production Research, 2020
Sajjad Rahmanzadeh, Mir Saman Pishvaee, Mohammad Reza Rasouli
Fuzzy logic relies on strong mathematical concepts and it has been successfully used in numerous fields such as control systems, engineering, optimisation, image processing, and industrial automation. Fuzzy mathematical programming is classified into two different categories namely flexible programming and Possibilistic Programming (PP). Flexible programming is used to deal with flexibility in constraints and objectives value and PP is used to cope with lack of knowledge about the value of input parameters when reliable historical data is not available (Pishvaee and Torabi 2010). In recent years, many articles have been presented based on the PP methods in order to deal with fuzzy coefficients of the objective function and constraints (e.g. Bodaghi, Jolai, and Rabbani 2018; Gholamian et al. 2015; Karlsson, Larsson, and Rönnbäck 2018; Naderi, Pishvaee, and Torabi 2016). Among different PP methods, fuzzy chance-constrained programming (FCCP) is one the most attractive and frequently used methods according to its ability in adjusting the confidence level of uncertain constraints (Pishvaee, Razmi, and Torabi 2014).
A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making
Published in Inverse Problems in Science and Engineering, 2020
Patricia da Silva Lopes Alexandrino, Guilherme Ferreira Gomes, Sebastião Simões Cunha
Traditional logic works only with true or false (exact values), on the other hand, fuzzy logic (fuzzy set) works with ‘degrees of truth’ or ‘degrees of false’ (imprecise information). Expressions like ‘more or less’ and ‘maybe’ can be mapped with fuzzy logic. In fuzzy logic, due to the imprecise nature of the decision maker’s judgment, the i-th objective function of a solution on Pareto front is represented by a membership function [5]. The values of membership function designate the level of achievement of the objective functions of some problem, and these values are between 0 and 1. There are several kinds of membership functions, such as, linear, triangular, trapezoidal, or exponential membership functions [24–26]. In this work, a trapezoidal membership function was used (Equation (3)). where and are the minimum and maximum values of the i-th objective function, respectively. The values of and can be find after ranking the nondominated solutions on Pareto front. The worst solution for i-th objective solution is .