Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
The paper aims to propose a distributed method for machine learning models and its application for medical data analysis. The\ngreat challenge in the medicine field is to provide a scalable image processing model, which integrates the computing processing\nrequirements and computing-aided medical decision making. The proposed Fuzzy logic method is based on a distributed\napproach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one\nmodel. Accordingly, the method is assigned to be implemented on big data analysis and data science prediction models for\nhealthcare applications. The paper focuses on the proposed distributed Type-2 Fuzzy Logic (DT2FL) method and its application\nfor MRI data analysis under a massively parallel and distributed virtual mobile agent architecture. Indeed, the paper presents some\nexperimental results which highlight the accuracy and efficiency of the proposed method....
The main purpose of this paper is to consider the strong law of large numbers for random sets in fuzzy metric space. Since many\nyears ago, limited theorems have been expressed and proved for fuzzy random variables, but despite the uncertainty in fuzzy\ndiscussions, the nonfuzzy metric space has been used. Given that the fuzzy random variable is defined on the basis of random sets,\nin this paper, we generalize the strong law of large numbers for random sets in the fuzzy metric space. The embedded theorem for\ncompact convex sets in the fuzzy normed space is the most important tool to prove this generalization. Also, as a result and by\napplication, we use the strong law of large numbers for random sets in the fuzzy metric space for the bootstrap mean....
With the rapid development of society, the number of college students in our country is on the rise. College students are under\npressure due to challenges from the society, school, and family, but they cannot find a suitable solution. As a result, the\npsychological problems of college students are diversified and complicated. The mental health problem of college students is\nbecoming more and more serious, which requires urgent attention.This article realizes the monitoring of university mental health\nby identifying and analyzing the emotions of college students. This article uses EEG to determine the emotional state of college\nstudents. First, feature extraction is performed on different rhythm data of EEG, and then a fuzzy support vector machine (FSVM)\nis used for classification. Finally, a decision fusion mechanism based on the D-S evidence combination theory is used to fuse the\nclassification results and output the final emotion recognition results. The contribution of this research is mainly in three aspects.\nOne is the use of multiple features, which improves the efficiency of data use; the other is the use of a fuzzy support vector machine\nclassifier with higher noise resistance, and the recognition rate of the model is better. The third is that the decision fusion\nmechanism based on the D-S evidence combination theory takes into account the classification results of each feature, and the\nclassification results assist each other and integrate organically. The experiment compares emotion recognition based on single\nrhythm, multirhythm combination, and multirhythm fusion. The experimental results fully prove that the proposed emotion\nrecognition method can effectively improve the recognition efficiency. It has a good practical value in the emotion recognition of\ncollege students....
In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific\nworkflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the\ndynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient\ndata placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic\ncloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we\npropose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators\n(DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the\nscientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation\nresults show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid\ncloud environments....
In this paper, we introduce the concept of mapping on hesitant fuzzy soft multisets and present some results for this type of\nmappings. The notions of inverse image and identity mapping are defined, and their basic properties are investigated. Hence,\nkinds of mappings and the composition of two hesitant fuzzy soft multimapping with the same dimension are presented. The\nconcept of hesitant fuzzy soft multitopology is defined, and certain types of hesitant fuzzy soft multimapping such as continuity,\nopen, closed, and homeomorphism are presented in detail. Also, their properties and results are studied. In addition, the concept\nof hesitant fuzzy soft multiconnected spaces is introduced....
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