2,979 publications from this institution
In the field of fuzzy inference, the universal triple I algorithm integrated the CRI (Compositional Rule of Inference) algorithm with the triple I algorithm. Later the triple I algorithm was generalized to the QIP (quintuple implication principle) algorithm. Whether the QIP algorithm and the CRI algorithm can be unified has become an interesting question. Therefore, in this study, a fuzzy inference scheme referred to as the universal quintuple implicational (UQI) algorithm is proposed. First, we establish a unified granular computing framework with the UQI algorithm, which is a generalization of the QIP algorithm, the CRI algorithm as well as the universal triple I algorithm. The optimal UQI solutions derived from the fundamental principle of determining inference results are obtained for the FMP (fuzzy modus ponens) problem, in which some specific solutions are also given. Second, the reversible property of the UQI algorithm is verified for FMP, while aiming at the metric derived from the biresiduum operation, the robustness of the UQI algorithm is validated. Third, under the environment of multiple rules, two general cases of FITA (First-Inference-Then-Aggregate) and FATI (First-Aggregate-Then-Inference) are constructed for the UQI algorithm. The corresponding equivalence relation between continuity and interpolation is analyzed. Fourth, the fuzzy system is established based on the UQI algorithm, the singleton fuzzier as well as the centroid defuzzier. Its response ability is analyzed and it is shown that such fuzzy system is a universal approximator. Lastly, we compare the results of the UQI algorithm with the QIP algorithm by five examples for FMP. It is found that the UQI algorithm is able to acquire more and better forms of the fuzzy inference in contrast with the QIP algorithm.
Big data with a large number of observations (samples) have posed genuine challenges for fuzzy clustering algorithms and fuzzy C-means (FCM), in particular. In this article, we propose an original algorithm referred to as a hyperplane division method to split the entire data set into disjoint subsets. By disjoint subsets, we mean that the data subspaces (parts of the entire data space), each of which is supported or spanned by the data points in the corresponding subset, do not overlap each other. The disjoint subsets turned out to be beneficial to the improvement of the quality of the clusters formed by the clustering algorithms. Moreover, considering that either a large number (say, thousands) or a small number (say, a few) of clusters may be pursued in the clustering task, we propose corresponding strategies (based on the hyperplane division method) to make clustering processes feasible, efficient, and effective. By validating the proposed strategies on both synthetic and publicly available data, we show their superiority (in terms of both efficiency and effectiveness) manifested in a visible way over the method of clustering the entire data and over some representative big data clustering methods.
Distributivity between two operations is a property posed many years ago — that is especially interesting in the framework of logical connectives because of its applications to fuzzy logic and approximate reasoning as their applications. Since semi-uninorms have been used in these topics, the study of the distributivity between two semi-uninorms becomes of particular interest that calls for thorough studies. The distributivity between two semi-uninorms, which are non-commutative and non-associative uninorms, has been developed only in the cases when both semi-uninorms are examples of very special classes of semi-uninorms. On the other hand, in general, the distributivity does not rely on the commutativity and associativity. The objective of this work is twofold. The first one is to show new solutions to distributivity equations for semi-uninorms. The second one is to check whether the results concerning the distributivity between two uninorms are valid for semi-uninorms. We investigate the distributivity involving two semi-uninorms when only one semi-uninrom lies in the most studied classes of semi-uninorms, achieving the above two objectives simultaneously.
Document classification, a critical area of research, employs machine and deep learning methods to solve real-world problems. This study attempts to highlight the qualitative and quantitative outcomes of the literature review from a broad range of scopes, including machine and deep learning methods, as well as solutions based on nature, biological, or quantum physics-inspired methods. A rigorous synthesis was conducted using a systematic literature review of 102 papers published between 2003 and 2023. The 20 Newsgroups (bydate version) were used as a reference point of benchmarks to ensure fair comparisons of methods. Qualitative analysis revealed that recent studies utilize Graph Neural Networks (GNNs) combined with models based on the transformer architecture and propose end-to-end solutions. Quantitative analysis demonstrated state-of-the-art results, with accuracy, micro and macro F1-scores of 90.38%, 88.28%, and 89.38%, respectively. However, the reproducibility of many studies may need to be revised for the scientific community. The resulting overview covers a wide range of document classification methods and can contribute to a better understanding of this field. Additionally, the systematic review approach reduces systematic error, making it useful for researchers in the document classification community.
Analysis of functional magnetic resonance imaging data considers temporal correlation of hemodynamic intensity patterns to a known activation paradigm as well as spatial regions of similar responses. Default regions of interest are often obtained by analyst-directed thresholding of intensity or correlation values. This paper presents a method to determine data-driven regions of interest using image erosion to identify structurally significant components in the default regions, namely bridge voxels
