q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients' prioritization based on prioritized Maclaurin symmetric mean aggregation operators.

Due to the fuzziness of the medical field, q-rung orthopair fuzzy 2-tuple linguistic (q-RF2L) set is the privileged way to aid medical professionals in conveying their assessments in the patient prioritization problem. The theme of the present study is to put forward a novel approach centered around the merging of prioritized averaging (PA) and the Maclaurin symmetric mean (MSM) operator within q-RF2L context. According to the prioritization of the professionals and the correlation among the defined criteria, we apply both PA and MSM to assess priority degrees and relationships, respectively. Keeping the pluses of the PA and MSM operators in mind, we introduce two aggregation operators (AOs), namely q-RF2L prioritized Maclaurin symmetric mean and q-RF2L prioritized dual Maclaurin symmetric mean operators. Meanwhile, some essential features and remarks of the proposed AOs are discussed at length. Based on the formulated AOs, we extend the weighted aggregated sum product assessment methodology to cope with q-RF2L decision-making problems. Ultimately, to illustrate the practicality and effectiveness of the stated methodology, a real-world example of patients' prioritization problem is addressed, and an in-depth analysis with prevailing methods is performed.

Understanding students' evaluations of professors using non-negative matrix factorization.

In this paper, we use Nonnegative Matrix Factorization (NMF) and several other state of the art statistical machine learning techniques to provide an in-depth study of university professor evaluations by their students. We specifically use the Kullback-Leibler divergence as our loss function in keeping with the type of the data and extract revealing patterns consistent with the educational objectives underlying the questionnaire design. In particular, the application of our techniques to a dataset gathered at Gazi University in Turkey reveals compelling patterns such as the strong association between the student's seriousness and dedication (measured by attendance) and the kind of scores they tend to assign to the courses and the corresponding professors. Insights emerging from our study suggest that more aspects of students' evaluations should be explored at greater depths.

Optimal bandwidth estimators of kernel density functionals for contaminated data.

In this study, we provide simulation-based exploration and characterization of the two most crucial kernel density functionals that play a central role in kernel density estimation, considering the probability density functions that are members of the location-scale family. Kernel density functional estimates are known to rely on the choice of preliminary bandwidth. Normal-scale estimators are commonly used to obtain preliminary bandwidth estimates, with the assumption that the data come from normal distribution. Here, we present an alternative approach, called the Cauchy-scale estimators, to obtain preliminary bandwidth estimates. In this approach, data are assumed to come from a Cauchy distribution. Furthermore, analysis results related to the sampling distribution of bandwidth estimators based on the normal- and Cauchy-scale approaches are presented. As a case study, we provide a comprehensive characterization of different contamination levels with a simulation study constructed for the random samples from normal distributions with various parameters and various contamination levels. The proposed preliminary bandwidth selection shows lower variance in both mixture and contaminated data in our simulations. Besides, functional bandwidth presents results similar to the simulation results in the applications we made on the real data set.