Research on hesitant fuzzy matroid based on satisfaction function

In this paper, we mainly extended the study of fuzzy matroid related problems to research the fuzzy decision method. Considering the ambiguity of actual event information and evaluation, we chose hesitant fuzzy set as the extended data set. To construct the hesitant fuzzy matroid, we defined the satisfaction function of hesitant fuzzy set combining hesitant fuzzy index entropy and score function, and defined the mapping function of fuzzy matroid through this function. We also defined the algorithm of hesitant fuzzy matroid and proved the theory of rank, basis of hesitant fuzzy matroid.

An Automatic MR Brain Image Segmentation Method Using a Multitask Quadratic Regularized Clustering Algorithm

In the diagnosis of clinical medicine, medical image processing plays a vital role and has become a hot issue in image processing. Magnetic resonance imaging not only provides convenience for treatment, but also brings help to the rehabilitation of patients. However, there are some unfavorable factors in MRI brain images, such as blurred boundary data, weak anti-noise ability, and so on. The classical fuzzy clustering algorithm has strong advantages, but the improved method is relatively simple, only adjusting the degree of membership or changing the distance algorithm to enhance the clustering effect. Therefore, this paper proposes a new multitask quadratic regularized clustering (MT-QRC) algorithm for MRI brain image segmentation, which improves the single-task clustering performance by transferring relevant knowledge between tasks. The proposed MT-QRC algorithm introduces the spatial information item controlled by the quadratic regularization term to replace the fuzzy index, which reduces the limitation of the fuzzy index in clustering and enhances the parameter flexibility.

Research on ARKFCM Algorithm Based on Membership Constraint and Bias Field Correction in Neonatal HIE Image Segmentation Method

First, this paper presents the algorithm of adaptively regularized kernel-based fuzzy C-means based on membership constraint (G-ARKFCM). Under the idea of competitive learning based on penalizing opponents, a new membership constraint function penalty item is introduced for each sample point in the segmented image, so that the ARKFCM algorithm is no longer limited to the fuzzy index m = 2. Secondly, the multiplicative intrinsic component optimization (MICO) is introduced into G-ARKFCM to obtain the GM-ARKFCM algorithm, which can correct the bias field when segmenting neonatal HIE images. Compared with other algorithms, the GM-ARKFCM algorithm has better segmentation quality and robustness. The GM-ARKFCM algorithm can more completely segment the neonatal ventricles and surrounding white matter and can retain more information of the original image.

Entropy-Based Multiview Data Clustering Analysis in the Era of Industry 4.0

In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of each view is averaged. To solve the above-mentioned problems, we used the Havrda-Charvat entropy and fuzzy index to construct a new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM. The corresponding results show that the Co-MVFCM has the best clustering performance among all the comparison clustering algorithms.

Level Operators over Intuitionistic Fuzzy Index Matrices

The index matrix (IM) is an extension of the ordinary matrix with indexed rows and columns. Over IMs’ standard matrix operations are defined and a lot of other ones that do not exist in the standard case. Intuitionistic fuzzy IMs (IFIMs) are modification of the IMs, when their elements are intuitionistic fuzzy pairs (IFPs). Extended IFIMs are IFIMs whose indices of the rows and columns are evaluated by IFPs. Different operations, relations and operators over IFIMs, and some specific ones, are defined for EIFIMs. In the paper, twelve new level operators are defined for EIFIMs and in the partial case, over IFIMs. The proposed level operators fall into two groups: operators that change the values of the EIFIM elements and operators that change the IFPs associated to the indices of the rows and columns. The basic properties of the operators are studied.

THE PROBLEM OF IMPLEMENTATION OF MONITORING AND EVALUATION OF INNOVATIVE ACTIVITY IN PUBLIC MANAGEMENT OF SOCIO-ECONOMIC PROCESSES

The article considers the system of criteria for evaluating innovation activity in the system of public management of socio-economic processes. It is offered to use not exact values of this or that characteristic, and their indistinct analogues for the description of processes in social systems. It is determined that new knowledge and innovations determine the pace of scientific and technological progress, the viability of enterprises and the competitiveness of economies. This necessitates the search for strategies to stimulate innovation activity in the field of public administration, focused on the formation of a comprehensive system of motivations, the creation of a modern organizational and economic model of activity, the introduction of monitoring and evaluation technologies. At the same time, there is a lack of a systematic approach to the assessment of innovation activity in the public administration system, which is accompanied by an increase in the risks of innovation and negatively affect the introduction of new technologies. In this regard, the problem of selection and implementation of an effective system for monitoring and rating construction is relevant. Existing in theory and practice approaches to innovation management and assessment of the level of its development do not contain clear, unambiguously interpreted criteria for choosing a strategy, as well as tools and methods to stimulate innovation. In addition, insufficient attention is paid to the organizational aspects of the implementation of innovation strategies. The list of the main characteristics for classification of innovative activity and construction of rating monitoring is resulted. Each of the subsystems is characterized by its fuzzy index. Thus, a combination of indexes describing various aspects of system operation is a universal code that can be used to classify and rank. The principles of analysis of quantitative indicators are also used as a methodological basis in the formation of the methodology for assessing innovation activity. Indicators should be used in the development of further strategies and the introduction of rating technologies to monitor the use of modern management techniques, the use of managerial innovations, digitalization of public administration, etc.