DLSI - Comunicaciones Congresos, Conferencias, ...
https://hdl.handle.net/10481/15211
2024-03-28T10:41:36Z
2024-03-28T10:41:36Z
Fuzzy Modelling of Local Linearity in Contours
Chamorro-Martínez, Jesús
Martínez-Jiménez, Pedro Manuel
Garrido, Antonio
Soto-Hidalgo, José Manuel
https://hdl.handle.net/10481/88926
2024-02-10T11:06:22Z
Fuzzy Modelling of Local Linearity in Contours
Chamorro-Martínez, Jesús; Martínez-Jiménez, Pedro Manuel; Garrido, Antonio; Soto-Hidalgo, José Manuel
Shape analysis, and particulary the contour study, is a fundamental task for object recognition in images. In this paper, a fuzzy approach for representing the linearity property of a contour segment is proposed. Linearity is a key property related to which degree a contour segment is a curve or a straight line; in addition, it is the basis for modelling other properties like curvature, salience or concavity/convexity. In this framework, firstly, the idea of linearity vs non-linearity, and the meaning of its fulfilment, will be analyzed. Secondly, the definition of a membership function according to that meaning will be proposed on the basis of the coefficient of determination. Finally, we will show the goodness of our proposal by analyzing linearity in a set of shapes with different characteristics.
This is the peer reviewed version of the following article: Jesús Chamorro-Martínez, Pedro Manuel Martínez-Jiménez, Antonio Garrido, José Manuel Soto-Hidalgo, “Fuzzy modelling of local linearity in contours” (2018), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), Rio de Janeiro (Brasil), 8-13 Julio 2018. DOI: 10.1109/FUZZ-IEEE.2018.8491659
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A decision making model to evaluate the reputation in social networks using HFLTS
Montes Soldado, Rosa Ana
Sánchez López, Ana María
Villar Castro, Pedro
Herrera Triguero, Francisco
https://hdl.handle.net/10481/88873
2024-03-04T11:23:01Z
A decision making model to evaluate the reputation in social networks using HFLTS
Montes Soldado, Rosa Ana; Sánchez López, Ana María; Villar Castro, Pedro; Herrera Triguero, Francisco
We present Teranga Go!, a social network with a linguistic fuzzy model which deals with HFLTS information as a practical application of decision making problems. It is defined to help members to select to whom interact based on collective information regarding real interactions with any user. In this way, we provide a tool intended to build trust among members of a sharing economy community given that is a major drawback from online transactions. As a workbench to run the linguistic decision making model, a web site and a mobile application for iOS and Android offer access to a carpooling service named Teranga Go! that seek to foster the mobility of international migration flows from Europe to Africa, based on concepts of collaborative economy and participatory consumption. The novelty of the site is the possibility of using hesitant linguistic expressions to assess a set of qualitative criteria and the use of the community members as the pool of experts. Unlike many multi criteria decision making problems we do not rank alternatives, we just qualify them using the retrieved opinions, which target a given user, and are collected over any interaction with this person along the time. Based on Computing with Words methodology where inputs are words and output are also words, we obtain from the model a linguistic value that is used to represent a karma property present in the user profile.
Fuzzy-Citation-KNN: a fuzzy nearest neighbor approach for multi-instance classification
Villar Castro, Pedro
Montes Soldado, Rosa Ana
Sánchez López, Ana María
Herrera Triguero, Francisco
https://hdl.handle.net/10481/88864
2024-03-04T11:21:06Z
Fuzzy-Citation-KNN: a fuzzy nearest neighbor approach for multi-instance classification
Villar Castro, Pedro; Montes Soldado, Rosa Ana; Sánchez López, Ana María; Herrera Triguero, Francisco
This contribution deals with multi-instance classification, where the labeled data samples are bags composed on instances instead of labeled instances as in standard classification. Every bag contains a number of traditional instances (described by a number of attributes) and the number of instances is not usually the same in all the bags. So, the whole bag is labeled but the instances that compose the bag are not individually labeled. We propose a fuzzy sets based extension of the well known algorithm called Citation-KNN, a reference method in multi-instance classification. Citation-KNN uses two types of examples in the classification rule: neighbors and citers of the bag to be classified. We analyze two versions of our proposal, one of them using both neighbors and citers, and the other one using only neighbors. Our approach uses the Hausdorff distance and it is based on the FuzzyKNN algorithm. Several data-sets from KEEL data-set repository are used in the experimental study and we compare our proposals with the original Citation-KNN algorithm.
A First Approach to a Fuzzy Classification System for Age Estimation based on the Pubic Bone
Villar Castro, Pedro
Alemán Aguilera, María Inmaculada
Castillo, Laura
Damas Arroyo, Sergio
Cordón García, Óscar
https://hdl.handle.net/10481/88863
2024-03-04T11:17:08Z
A First Approach to a Fuzzy Classification System for Age Estimation based on the Pubic Bone
Villar Castro, Pedro; Alemán Aguilera, María Inmaculada; Castillo, Laura; Damas Arroyo, Sergio; Cordón García, Óscar
The study of human remains suffers from a lack of information for determining a reliable estimation of the age of an individual. One of the most extended methods for this task was proposed in the twenties of the past century and is based on the analysis of the pubic bone. The method describes some age changes occurring in the pubic bone and establishes ten different age ranges with a description of the morphological aspect of the bone in each one of them. These descriptions are sometimes vague and there is not a systematic way for using the method. In this contribution we propose two different preliminary fuzzy rule-based classification system designs for age estimation from the pubic bone that consider the main morphological characteristics of the bone as independent and linguistic variables. So, we have identified the problem variables and we have defined the corresponding linguistic labels making use of forensic expert knowledge, that is also considered to design a decision support fuzzy system. A brief collection of pubic bones labeled by forensic anthropologists has been used for learning the second fuzzy rule-based classification system by means of a fuzzy decision tree. The experiments developed report a best performance of the latter approach.
A Parallel Cellular Automaton Model For Adenocarcinomas in Situ with Java: Study of One Case
Tomeu Hardasmal, Antonio J.
Salguero Hidalgo, Alberto Gabriel
Capel Tuñón, Manuel Isidoro
https://hdl.handle.net/10481/88758
2024-02-09T07:25:02Z
A Parallel Cellular Automaton Model For Adenocarcinomas in Situ with Java: Study of One Case
Tomeu Hardasmal, Antonio J.; Salguero Hidalgo, Alberto Gabriel; Capel Tuñón, Manuel Isidoro
Adenocarcinomas are tumors that originate in the lining epithelium of the ducts that form the endocrine glands of the human body. Infiltrating breast and one of the most frequent neoplasms among female population, and the early detection of the disease is then fundamental and, for this reason, a profound knowledge of the biology of tumor at this phase is essential. Among the distinct tools that contribute to this knowledge, computational simulation is more frequently used every day. The availability of fast and efficient computations that allow the simulation of tumor dynamics in situ, under a wide range of different parameters, is an important research topic. Based on cellular automata, this paper proposes a generic simulation model for the Adenocarcinomas In Situ (CIS). We applied it to the breast ductal adenocarcinoma in situ (DCIS), modeling our cells with the genomic load that we currently know that the tumor starts, and proposing a numerical coding method for the genome that allows efficient computational management. We propose a parallelization scheme using data parallelism, and we show the acceleration achieved in multiple nodes of our cluster of processors.