KNNHI: Resilient KNN algorithm for heterogeneous incomplete data classification and K identification using rough set theory

The original K-nearest neighbour ( KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.

Influential Pathways of Employees’ Career Growth: Linkage of Psychological and Organizational Factors Based on Qualitative Comparative Analysis

Implementing the “hierarchical diagnosis and treatment” system highlights the important role of general practitioners as “residents’ health gatekeepers.” Still, the low level of career growth always limits the realization of their service value. Inertial thinking uses a single factor to explain the complexity of career growth in previous studies; in fact, it isn’t easy to assess whether the factor is a sufficient and necessary condition for a high level of career growth. Herein, we have used a set theory perspective to analyze the mechanism of influencing high-level career growth by combining psychological and organizational factors. This research aims to analyze causal complexity relationship between these conditions and results is analyzed in detail. We choose fuzzy-set qualitative comparative analysis (fsQCA) with a sample of 407 GPs to test 5 antecedent conditional variables that can affect their career growth. The variables include professional identity, self-efficacy, achievement motivation, training mechanism, and incentive mechanism. To ensure the universality and diversity of data, the samples were selected from community medical institutions in different regions of China. The results show that three pathways can affect the high career growth of GPs, and the optimal pathway A2 is the linkage matching of high incentive mechanism, high professional identity, high achievement motivation, and high self-efficacy. At the same time, we find that professional identity plays an alternative role in the three pathways. When professional identity is at a high level, as long as achievement motivation and self-efficacy are superior, or achievement motivation, self-efficacy, and achievement motivation are superior, a high level of career growth can be achieved. We broke the shackles of previous studies that only focused on the impact of single factors on the career growth of GPs. From the perspective of set theory, we use configurational thinking to construct Influential pathways of high career growth of GPs by integrating antecedents. The results can provide effective support for improving GPs’ service ability and realizing their service value to protect residents’ health.

Efficiency Assessment of Tax Measures in the European Countries Against the Effects of COVID-19

Ongoing global COVID-19 pandemic is not only health crisis but the economic challenge. The future of society depends on how successfully the authorities find a balance between imposition of stringent restrictions and economic development. Tax policies play a role in reducing losses caused by the COVID-19 lockdowns. All countries are taking tax measures to mitigate the impact of the effects of COVID-19 pandemic on society. While the COVID-19 pandemic has not yet been defeated, it is too early to draw conclusions about which tax measures against the effects of COVID-19 are efficient. On the other hand, correct trajectory of economic recovery can be missed if not to analyze the other countries experience. The object of this study is tax measures in the European countries against the effects of COVID-19. The subject of the study is the fuzzy set theory to assess the efficiency of tax measures in the European countries against the effects of COVID-19. The aim of the study is to find out which European countries have been more succeeded in tax measures implementing and type of their immediate crisis response. The analysis is carried out in 29 European countries. The result of the study allows to state that the number of tax measures against the effects of COVID-19 does not affect their efficiency and the most popular type of immediate crisis response has been the business cash-flow enhances.

Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application

<abstract> <p>One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts. Likewise, a lot of examples are exposed under the invented measures based on PHF data in the environment of medical diagnosis to demonstrate the stability and elasticity of the explored works. Finally, the sensitive analysis of the presented works is also implemented and illuminated their graphical structures.</p> </abstract>

Обзор и классификация пространственно-временных векторов дискретных состояний семифазного преобразователя

The subject of the research is the control algorithms for a seven-phase converter that implement space-vector voltage modulation of a seven-phase motor as an alternative to a three-phase engine in modern electric traction. The study used elements of set theory, combinatorics, Fourier series expansion and vector analysis. Checking research results was implemented on a special stand for experimental studies of spatial vector voltage modulation of a seven-phase motor.