In this paper, we consider the concept of extended Choquet integral generalized by a copula, called
CC
-integral. In particular, we adopt a
CC
-integral that uses a copula defined by a parameter
α
, ...which behavior was tested in a previous work using different fixed values. In this contribution, we propose an extension of this method by learning the best value for the parameter
α
using a genetic algorithm. This new proposal is applied in the fuzzy reasoning method of fuzzy rule-based classification systems in such a way that, for each class, the most suitable value of the parameter
α
is obtained, which can lead to an improvement on the system’s performance. In the experimental study, we test the performance of 4 different so called
C
α
C
-integrals, comparing the results obtained when using fixed values for the parameter
α
against the results provided by our new evolutionary approach. From the obtained results, it is possible to conclude that the genetic learning of the parameter
α
is statistically superior than the fixed one for two copulas. Moreover, in general, the accuracy achieved in test is superior than that of the fixed approach in all functions. We also compare the quality of this approach with related approaches, showing that the methodology proposed in this work provides competitive results. Therefore, we demonstrate that
C
α
C
-integrals with
α
learned genetically can be considered as a good alternative to be used in fuzzy rule-based classification systems.
Major depression is characterized for symptoms at the psychological, behavioral and physiological levels. The chronic mild stress model has been used as an animal model of depression. The consumption ...of sweet food, locomotor activity, body weight, lipid and protein oxidation levels and superoxide dismutase and catalase activities in the rat hippocampus, prefrontal cortex and cortex were assessed in rats exposed to chronic mild stress. Our findings demonstrated a decrease on sweet food intake, no effect on locomotor activity, lack of body weight gain, increase in protein (prefrontal, hippocampus, striatum and cortex) and lipidic peroxidation (cerebellum and striatum), and an increase in catalase (cerebellum, hippocampus, striatum, cortex) and a decrease in superoxide dismutase activity (prefrontal, hippocampus, striatum and cortex) in stressed rats. In conclusion, our results support the idea that stress produces oxidants and an imbalance between superoxide dismutase and catalase activities that contributes to stress-related diseases, such as depression.
This paper presents the HybriD-GM model conception, from modeling to consolidation. The D-GM environment is also extended, providing efficient parallel executions for quantum computing simulations, ...targeted to hybrid architectures considering the CPU and GPU integration. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables granularity control, optimizing hardware resources in distributed computations organized as tree data structures. In the HybriD-GM evaluation, simulations of Shor's and Grover's algorithms achieve significant performance improvements in comparison to the previous D-GM version, and also with other related works, for example, LIQUi|⟩ and ProjectQ simulators.
Edge detection is a crucial process in numerous stages of computer vision. This field of study has recently gained momentum due to its importance in various applications. The uncertainty, among other ...characteristics of images, makes it difficult to accurately determine the edge of objects. Furthermore, even the definition of an edge is vague as an edge can be considered as the maximum boundary between two regions with different properties. Given the advancement of research in image discontinuity detection, especially using aggregation and pre-aggregation functions, and the lack of systematic literature reviews on this topic, this paper aims to gather and synthesize the current state of the art of this topic. To achieve this, this paper presents a systematic review of the literature, which selected 24 papers filtered from 428 articles found in computer databases in the last seven years. It was possible to synthesize important related information, which was grouped into three approaches: (i) based on both multiple descriptor extraction and data aggregation, (ii) based on both the aggregation of distance functions and fuzzy C-means, and (iii) based on fuzzy theory, namely type-2 fuzzy and neutrosophic sets. As a conclusion, this review provides interesting gaps that can be explored in future work.
RESUMO Objetivo Conhecer as modificações do padrão do sono em insones usuários crônicos de benzodiazepínicos (BZDs) após introdução da trazodona. Métodos Em um grupo de 11 pacientes, foi estabelecido ...esquema para retirada gradual do BZD com introdução progressiva da trazodona. Foram realizadas duas polissonografias, sendo a primeira com dose de BZD habitual do paciente e a segunda após supensão do BZD e com 150 mg de trazodona de liberação prolongada. Questionários de qualidade do sono (Pittsburgh), sonolência diurna (Epworth) e sintomas depressivos (Hamilton) e ansiosos (Beck) foram aplicados. Resultados Cinco indivíduos concluíram o estudo, tendo sido acompanhados por pelo menos seis semanas. Nesses pacientes, a trazodona aumentou significativamente a eficiência do sono e sono REM e diminuiu o tempo desperto após início do sono. Houve melhora da qualidade do sono, porém não houve alteração dos sintomas depressivos e ansiosos. Conclusão Trazodona de liberação prolongada demonstrou ser uma opção terapêutica para insones usuários crônicos de BZDs com retirada eficaz do ansiolítico. Houve melhora na qualidade do sono por questionário e polissonografia. Maior número de pacientes será necessário para determinar os benefícios da trazodona nesse tipo de intervenção.
This paper introduces the family of CF-integrals, which are pre-aggregations functions that generalizes the Choquet integral considering a bivariate function F that is left 0-absorbent. We show that ...CF-integrals are 1→-pre-aggregation functions, studying in which conditions they are idempotent and/or averaging functions. This characterization is an important issue of our approach, since we apply these functions in the Fuzzy Reasoning Method (FRM) of a fuzzy rule-based classification system and, in the literature, it is possible to observe that non-averaging aggregation functions usually provide better results. We carry out a study with several subfamilies of CF-integrals having averaging or non-averaging characteristics. As expected, the proposed non-averaging CF-integrals obtain more accurate results than the averaging ones, thus, offering new possibilities for aggregating accurately the information in the FRM. Furthermore, it allows us to enhance the results of classical FRMs like the winning rule and the additive combination.
•We make a revision of recent generalizations of the Choquet integral that appear in the literature.•We show some of the most relevant theoretical features of these extensions.•We also discuss some ...applications where these extensions have provided good results.
In 2013, Barrenechea et al. used the Choquet integral as an aggregation function in the fuzzy reasoning method (FRM) of fuzzy rule-based classification systems. After that, starting from 2016, new aggregation-like functions generalizing the Choquet integral have appeared in the literature, in particular in the works by Lucca et al. Those generalizations of the Choquet integral, namely CT-integrals (by t-norm T), CF-integrals (by a fusion function F satisfying some specific properties), CC-integrals (by a copula C), CF1F2-integrals (by a pair of fusion functions (F1, F2) under some specific constraints) and their generalization gCF1F2-integrals, achieved excellent results in classification problems. The works by Lucca et al. showed that the aggregation task in a FRM may be performed by either aggregation, pre-aggregation or just ordered directional monotonic functions satisfying some boundary conditions, that is, it is not necessary to have an aggregation function to obtain competitive results in classification. The aim of this paper is to present and discuss such generalizations of the Choquet integral, offering a general panorama of the state of the art, showing the relations and intersections among such five classes of generalizations. First, we present them from a theoretical point of view. Then, we also summarize some applications found in the literature.
This paper introduces the concept of Choquet-like Copula-based aggregation function (CC-integral) and its application in fuzzy rule-based classification systems. The standard Choquet integral is ...expanded by distributing the product operation. Then, the product operation is generalized by a copula. Unlike the generalization of the Choquet integral by t-norms using its standard form (i.e., without distributing the product operator), which results in a pre-aggregation function, the CC-integral satisfies all the conditions required for an aggregation function. We build some examples of CC-integrals considering different examples of copulas, including t-norms, overlap functions and copulas that are neither t-norms nor overlap functions. We show that the CC-integral based on the minimum t-norm, when applied in fuzzy rule-based classification systems, obtains a performance that is, with a high level of confidence, better than that which adopts the winning rule (maximum). We concluded that the behavior of CC-integral is similar to the best Choquet-like pre-aggregation function. Consequently, the CC-integrals introduced in this paper can enlarge the scope of the applications by offering new possibilities for defining fuzzy reasoning methods with a similar gain in performance.
It is known that the human visual system performs a hierarchical information process in which early vision cues (or primitives) are fused in the visual cortex to compose complex shapes and ...descriptors. While different aspects of the process have been extensively studied, such as lens adaptation or feature detection, some other aspects, such as feature fusion, have been mostly left aside. In this work, we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years. We propose to use generalizations of the Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection datasets.
Preaggregation Functions: Construction and an Application Lucca, Giancarlo; Sanz, Jose Antonio; Dimuro, Gracaliz Pereira ...
IEEE transactions on fuzzy systems,
2016-April, 2016-4-00, 20160401, Volume:
24, Issue:
2
Journal Article
Peer reviewed
Open access
In this paper, we introduce the notion of preaggregation function. Such a function satisfies the same boundary conditions as an aggregation function, but, instead of requiring monotonicity, only ...monotonicity along some fixed direction (directional monotonicity) is required. We present some examples of such functions. We propose three different methods to build preaggregation functions. We experimentally show that in fuzzy rule-based classification systems, when we use one of these methods, namely, the one based on the use of the Choquet integral replacing the product by other aggregation functions, if we consider the minimum or the Hamacher product t-norms for such construction, we improve the results obtained when applying the fuzzy reasoning methods obtained using two classical averaging operators such as the maximum and the Choquet integral.
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