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• Bibliometric techniques identify the productivity and influence on research. • Fuzzy systems are an area of study with potential for further research. • Zadeh is known as the most influential author in fuzzy research in North American. • The United States is the most productive and influential country in fuzzy research. The objective of this paper is to apply bibliometric techniques to analyze the evolution of fuzzy systems research (FSR) in the United States of America and Canada. The aim is to visualize the progression of FSR since Zadeh's first publication of fuzzy sets and the regional impact that these ideas have in the scientific community. This work obtains data retrieved from the Web of Science (WoS) scientific database. Using a hybrid methodology designed to maximize the obtention of representative results, we analyze 20,317 documents published between 1965 and 2020. The main findings show that Zadeh is the most influential author and that Pedrycz is the most productive author at FSR. Journals such as Fuzzy Sets and Systems (FSS) and IEEE Transactions on Fuzzy Systems (TFS) publish most of the FSR articles. The results show a greater contribution by the United States of America compared to Canada in FSR. This analysis contributes to visualization and evolution in the field of study of FSR in diverse regions. [ABSTRACT FROM AUTHOR]

Abstract In recent years, healthcare spending has risen and become a burden on governments especially in the US. One of the reasons for this increase is hospital readmissions. Decreasing the number of readmissions can reduce the healthcare spending. The first part of this paper examines state-of-the-art applications that aim to reduce the number of readmissions at hospitals. We categorize these applications into three different categories and highlight not only their benefits, but also their weaknesses. Moreover, we describe the limitations and challenges faced by these systems to demonstrate the possible future research paths. The second part of this paper presents novel algorithms to reduce the number of readmissions by applying the concept of personalization and actionable patterns. First, we start by extracting all possible procedure paths (course of treatments) for a given procedure. Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the next procedure. Finally, we present a novel algorithm that provides recommendations (actionable knowledge) to the physicians to put patients on a treatment path that would result in optimal reduction of the number of readmissions on average case. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a method based on fuzzy transforms to establish dependencies between numerical attributes in datasets. We find the best fuzzy partitions of the attribute domains with respect to which we determine the direct and inverse fuzzy transforms. We use two specific regression indexes (which must be smaller than a threshold deduced experimentally) for evaluating dependency between numerical attributes. The experiments are conducted on two well known datasets: “El Nino” (http://kdd.ics.uci.edu/databases/el_nino/el_nino.data.html) and the remote sensing data determined from US Forest Service (Region 2, Resource Information System, http://kdd.ics.uci.edu/databases/covertype/covertype.data.html). Our results are quite in agreement with the regression analysis of the same data. [Copyright &y& Elsevier]