The Experts below are selected from a list of 20511 Experts worldwide ranked by ideXlab platform
Etienne Kerre - One of the best experts on this subject based on the ideXlab platform.
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group decision making with incomplete interval valued fuzzy preference relations based on the Minimum Operator
International Journal of Computers Communications & Control, 2015Co-Authors: Samina Ashraf, Atiq Ur Rehman, Etienne KerreAbstract:This paper presents a new method to estimate the unknown values in incomplete interval-valued fuzzy preference relations (IVFPRs). The method is based on the min-consistency and is used to develop the algorithm for group decision making (GDM) dealing with incomplete IVFPRs.
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a study of interval valued fuzzy morphology based on the Minimum Operator
International Conference on Digital Image Processing, 2010Co-Authors: Mike Nachtegael, Peter Sussner, Tom Melange, Etienne KerreAbstract:Pixels of a grayscale image are classically associated with a single grayscale value. However, capturing grayscale images comes along with two kinds of uncertainty: numerical uncertainty (do we measure the actual value of the pixel or just an approximation?) and spatial uncertainty (does the measured pixel correspond to the actual spatial position or has it shifted?). Interval-valued fuzzy set theory provides a framework to model grayscale images of which the captured grayscale values are uncertain. This is realized by associating every pixel with a closed interval of possible grayscale values instead of with one single value. Based on this image model, a new corresponding morphological framework to process these images (e.g., using dilation and erosion) has been developed. In that way, we are not only able to model the uncertainty that is present during image capturing, but we are also able to process it such that the information regarding the uncertainty is never lost. In this paper, we study the interval-valued fuzzy morphological model based on the Minimum-Operator. Properties that are relevant in the context of image processing, as well as some interesting decomposition and construction properties, are discussed. This study gives an insight in the morphological model and will help researchers when they want to apply it in practice.
Karina Martinezmayorga - One of the best experts on this subject based on the ideXlab platform.
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enhancing acute oral toxicity predictions by using consensus modeling and algebraic form based 0d to 2d molecular encodes
Chemical Research in Toxicology, 2019Co-Authors: Cesar R Garciajacas, Yovani Marreroponce, Fernando Cortesguzman, Jose Suarezlezcano, Felix Martinezrios, Luis A Garciagonzalez, Mario Pupomerino, Karina MartinezmayorgaAbstract:Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with REPA-training ≥ 0.75 ( R = correlation coefficient) and MAEEPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the Minimum Operator and denoted as M19 and M22, as well as a consensus model based on the weighted average Operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAEtest-AD = 0.4044, MAEProTox-AD = 0.4067 and MAET3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAEProTox-AD = 0.3992 and MAET3DB-AD = 0.2286, and MAEProTox-AD = 0.3773 and MAET3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).
Doru Todinca - One of the best experts on this subject based on the ideXlab platform.
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the efficiency of Minimum Operator for fuzzy automata a case study
Symposium on Applied Computational Intelligence and Informatics, 2015Co-Authors: Danieleugen Butoianu, Doru TodincaAbstract:This paper is a continuation of our previous work [1], [2], in which we studied the properties of fuzzy automata with the help of VHDL simulations. In our previous studies we saw that in many cases, the standard combination of min-max norms, which are the most common Operators used in fuzzy logic, often do not yield stable fuzzy automata. In this paper we will investigate some methods of using the min-max norms as well as some situations where they can improve the behavior of a fuzzy automaton. Here we will investigate the impact that the t-norm has on the states and output values of a fuzzy automaton; as such we will be focusing on the “Minimum” Operator.
Cesar R Garciajacas - One of the best experts on this subject based on the ideXlab platform.
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enhancing acute oral toxicity predictions by using consensus modeling and algebraic form based 0d to 2d molecular encodes
Chemical Research in Toxicology, 2019Co-Authors: Cesar R Garciajacas, Yovani Marreroponce, Fernando Cortesguzman, Jose Suarezlezcano, Felix Martinezrios, Luis A Garciagonzalez, Mario Pupomerino, Karina MartinezmayorgaAbstract:Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with REPA-training ≥ 0.75 ( R = correlation coefficient) and MAEEPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the Minimum Operator and denoted as M19 and M22, as well as a consensus model based on the weighted average Operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAEtest-AD = 0.4044, MAEProTox-AD = 0.4067 and MAET3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAEProTox-AD = 0.3992 and MAET3DB-AD = 0.2286, and MAEProTox-AD = 0.3773 and MAET3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).
Gerhard Jager - One of the best experts on this subject based on the ideXlab platform.
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systems of explicit mathematics with non constructive μ Operator part i
Annals of Pure and Applied Logic, 1993Co-Authors: Solomon Feferman, Gerhard JagerAbstract:Feferman, S. and G. Jager, Systems of explicit mathematics with non-constructive μ-Operator. Part I, Annals of Pure and Applied Logic 65 (1993) 243-263. This paper is mainly concerned with the proof-theoretic analysis of systems of explicit mathematics with a non-constructive Minimum Operator. We start off from a basic theory BON of Operators and numbers and add some principles of set and formula induction on the natural numbers as well as axioms for μ. The principal results then state: (i) BON(μ) plus set induction is proof-theoretically equivalent to Peano arithmetic PA; (ii) BON(μ) plus formula induction is proof-theoretically equivalent to the system (Π0∞-CA)