Search Results (10)

In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining efficiency. Frequent itemsets are extracted from the database by establishing BV and performing vector inner product operations. These frequent itemsets form the problem space for the ant colony algorithm, which generates the maximum frequent itemset. Initially, data from the total scores of players during the 2022–2024 regular season was analyzed to obtain the optimal lineup. The results obtained from the Apriori algorithm (AA) were used as a standard for comparison with the Confidence-Debiased Adversarial Fuzzy Apriori Method (CDAFAM), the AA based on deep learning (DL), and the proposed algorithm regarding their results and required time. A dataset of disease symptoms was then used to determine diseases based on symptoms, comparing accuracy and time against the original database as a standard. Finally, simulations were conducted using five batches of radar data from the observation platform to compare the time and accuracy of the four algorithms. The results indicate that both the proposed algorithm and the AA based on DL achieve approximately 10% higher accuracy compared with the traditional AA. Additionally, the proposed algorithm requires only about 25% of the time needed by the traditional AA and the AA based on DL for target recognition. Although the CDAFAM has a similar processing time to the proposed algorithm, its accuracy is lower. These findings demonstrate that the proposed algorithm significantly improves the accuracy and speed of target recognition. Full article

In this paper, we expand the theory of semi-vector spaces and semi-algebras, both over the semi-field of nonnegative real numbers ${\mathbb{R}}_0^{+}$. More precisely, we prove several new results concerning these theories. We introduce to the literature the concept of eigenvalues and eigenvectors of a semi-linear operator, describing how to compute them. The topological properties of semi-vector spaces, such as completeness and separability, are also investigated here. New families of semi-vector spaces derived from the semi-metric, semi-norm and semi-inner product, among others, are exhibited. Furthermore, we show several new results concerning semi-algebras. After this theoretical approach, we apply such a theory in fuzzy automata. More precisely, we describe the semi-algebra of A-fuzzy regular languages and we apply the theory of fuzzy automata for counting patterns in DNA sequences. Full article

Public travel is an important support for urban citizens’ production and life. As a collective choice behavior, there are different action logics behind the common sharing travel and public sharing travel in China. It is beneficial to provide public travel services to citizens and improve the performance of urban governance by sorting out the different public travel types and their inner choice logics. A fuzzy set of qualitative, comparative analyses reveals that citizens’ choice of common sharing travel or public sharing travel consists of two paths, in which user size, rule attainment, convenience, and travel distance are important triggers. The government and enterprises should improve the combined supply of each factor to enhance citizens’ public travel experience, guide citizens’ public travel choice behavior, and help the development of a common sharing travel industry so as to promote the construction of green transportation, public transport cities, and smart cities. Full article

The aim of this paper is to provide a suitable definition for the concept of fuzzy inner product space. In order to achieve this, we firstly focused on various approaches from the already-existent literature. Due to the emergence of various studies on fuzzy inner product spaces, it is necessary to make a comprehensive overview of the published papers on the aforementioned subject in order to facilitate subsequent research. Then we considered another approach to the notion of fuzzy inner product starting from P. Majundar and S.K. Samanta’s definition. In fact, we changed their definition and we proved some new properties of the fuzzy inner product function. We also proved that this fuzzy inner product generates a fuzzy norm of the type Nădăban-Dzitac. Finally, some challenges are given. Full article

In this paper, we find the solution of the following quadratic functional equation $n{\sum }_{1\le i<j\le n}Q\left(x_i,-,x_j\right)={\sum }_{i=1}^{n}Q\left({\sum }_{j\ne i},x_j,-,(n-1),x_i\right)$, which is derived from the gravity of the n distinct vectors $x_1,\cdots ,x_n$ in an inner product space, and prove that the stability results of the $\mathcal{A}$-quadratic mappings in $\mu$-complete convex fuzzy modular ∗-algebras without using lower semicontinuity and $\beta$-homogeneous property. Full article

The paper is concerned with complex fuzzy numbers and complex fuzzy inner product spaces. In the classical complex number set, a complex number can be expressed using the Cartesian form or polar form. Both expressions are needed because one expression is better than the other depending on the situation. Likewise, the Cartesian form and the polar form can be defined in a complex fuzzy number set. First, the complex fuzzy numbers (CFNs) are categorized into two types, the polar form and the Cartesian form, as type I and type II. The properties of the complex fuzzy number set of those two expressions are discussed, and how the expressions can be used practically is shown through an example. Second, we study the complex fuzzy inner product structure in each category and find the non-existence of an inner product on CFNs of type I. Several properties of the fuzzy inner product space for type II are proposed from the modulus that is newly defined. Specfically, the Cauchy-Schwartz inequality for type II is proven in a compact way, not only the one for fuzzy real numbers. In fact, it was already discussed by Hasanhani et al; however, they proved every case in a very complicated way. In this paper, we prove the Cauchy-Schwartz inequality in a much simpler way from a general point of view. Finally, we introduce a complex fuzzy scalar product for the generalization of a complex fuzzy inner product and propose to study the condition for its existence on CFNs of type I. Full article

Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory. Full article

First, we show that the non-trivial fuzzy inner product space under the linearity condition does not exist, which means a fuzzy inner product space with linearity produces only a crisp real number for each pair of vectors. If the positive-definiteness is added to the condition, then the Cauchy–Schwartz inequality is also proved. Full article

The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal. Full article

Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1). Full article