TY  - JOUR
AU  - Ibrišimović, Irma
AU  - Iričanin, Bratislav
AU  - Milosavljević, Nataša
AU  - Nedović, Ljubo
AU  - Ralević, Nebojša
PY  - 2023
UR  - https://link.springer.com/chapter/10.1007/978-3-031-39777-6_13
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/6463
AB  - Fuzzy sets are a mathematical notion that allows us to represent uncertainty by assigning a degree of membership to a value in a range. This degree of membership represents the degree to which a particular value belongs to the fuzzy set. Fuzzy numbers are fuzzy sets that satisfy specific conditions, and can be applied in the interpretation of the behavior of neural networks. In many real-world problems, the output of a neural network may not be a precise numerical value, but rather a range of values that are subject to uncertainty. In the context of neural networks, fuzzy numbers can be used to represent uncertainty in the output of the network. Fuzzy numbers are used in applications where the representation of ambiguity and uncertainty in numerical data is explicitly desired. This paper will consider discretizations and defuzzifications triangular fuzzy numbers with the use of MATLABR software. MATLABR functions were applied to the analysis of kidney images, where the fuzzy numbers represent the tendency value of the degree of redness, which serves as an indicator for classifying the image as normal or abnormal based on different characteristics.
T1  - Fuzzy Numbers and Analysis of Radiological Images
DO  - 10.1007/978-3-031-39777-6_13
ER  - 

@article{
author = "Ibrišimović, Irma and Iričanin, Bratislav and Milosavljević, Nataša and Nedović, Ljubo and Ralević, Nebojša",
year = "2023",
abstract = "Fuzzy sets are a mathematical notion that allows us to represent uncertainty by assigning a degree of membership to a value in a range. This degree of membership represents the degree to which a particular value belongs to the fuzzy set. Fuzzy numbers are fuzzy sets that satisfy specific conditions, and can be applied in the interpretation of the behavior of neural networks. In many real-world problems, the output of a neural network may not be a precise numerical value, but rather a range of values that are subject to uncertainty. In the context of neural networks, fuzzy numbers can be used to represent uncertainty in the output of the network. Fuzzy numbers are used in applications where the representation of ambiguity and uncertainty in numerical data is explicitly desired. This paper will consider discretizations and defuzzifications triangular fuzzy numbers with the use of MATLABR software. MATLABR functions were applied to the analysis of kidney images, where the fuzzy numbers represent the tendency value of the degree of redness, which serves as an indicator for classifying the image as normal or abnormal based on different characteristics.",
title = "Fuzzy Numbers and Analysis of Radiological Images",
doi = "10.1007/978-3-031-39777-6_13"
}

Ibrišimović, I., Iričanin, B., Milosavljević, N., Nedović, L.,& Ralević, N.. (2023). Fuzzy Numbers and Analysis of Radiological Images. .
https://doi.org/10.1007/978-3-031-39777-6_13

Ibrišimović I, Iričanin B, Milosavljević N, Nedović L, Ralević N. Fuzzy Numbers and Analysis of Radiological Images. 2023;.
doi:10.1007/978-3-031-39777-6_13 .

Ibrišimović, Irma, Iričanin, Bratislav, Milosavljević, Nataša, Nedović, Ljubo, Ralević, Nebojša, "Fuzzy Numbers and Analysis of Radiological Images" (2023),
https://doi.org/10.1007/978-3-031-39777-6_13 . .

TY  - JOUR
AU  - Milosavljević, Nataša S.
AU  - Ralević, Nebojša M.
PY  - 2023
UR  - https://doi.org/10.1007/s11042-023-17053-7
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/6478
AB  - Many methods have been proposed to detect the originality of an image. One of the most commonly used method, the Copy - move forgery detection (CMFD), is considered here. The contribution of this paper is the application of the new fuzzy distances in clustering using metaheuristics. The family of the used fuzzy distances satisfies the axioms of the fuzzy metric. CMFD method, which includes Variable Neighborhood Search (VNS) and Bee Colony Optimization (BCO) metaheuristics, has been tested and compared with similar methods. The proposed method with the proposed new metric used in this research gave better results than the existing methods. The proposed fuzzy metrics in this paper as well as the problem of $$p-$$median clustering applied to the problem and compared with existing research in this field give better results.
T2  - Multimedia Tools and Applications
T2  - Multimedia Tools and ApplicationsMultimed Tools Appl
T1  - Fuzzy methaheuristic model for copy-move forgery detection on images
DO  - 10.1007/s11042-023-17053-7
ER  - 

@article{
author = "Milosavljević, Nataša S. and Ralević, Nebojša M.",
year = "2023",
abstract = "Many methods have been proposed to detect the originality of an image. One of the most commonly used method, the Copy - move forgery detection (CMFD), is considered here. The contribution of this paper is the application of the new fuzzy distances in clustering using metaheuristics. The family of the used fuzzy distances satisfies the axioms of the fuzzy metric. CMFD method, which includes Variable Neighborhood Search (VNS) and Bee Colony Optimization (BCO) metaheuristics, has been tested and compared with similar methods. The proposed method with the proposed new metric used in this research gave better results than the existing methods. The proposed fuzzy metrics in this paper as well as the problem of $$p-$$median clustering applied to the problem and compared with existing research in this field give better results.",
journal = "Multimedia Tools and Applications, Multimedia Tools and ApplicationsMultimed Tools Appl",
title = "Fuzzy methaheuristic model for copy-move forgery detection on images",
doi = "10.1007/s11042-023-17053-7"
}

Milosavljević, N. S.,& Ralević, N. M.. (2023). Fuzzy methaheuristic model for copy-move forgery detection on images. in Multimedia Tools and Applications.
https://doi.org/10.1007/s11042-023-17053-7

Milosavljević NS, Ralević NM. Fuzzy methaheuristic model for copy-move forgery detection on images. in Multimedia Tools and Applications. 2023;.
doi:10.1007/s11042-023-17053-7 .

Milosavljević, Nataša S., Ralević, Nebojša M., "Fuzzy methaheuristic model for copy-move forgery detection on images" in Multimedia Tools and Applications (2023),
https://doi.org/10.1007/s11042-023-17053-7 . .