A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics. (25th August 2020)

• Record Type: Journal Article
• Authors: Benchara, Fatéma Zahra; Youssfi, Mohamed
• Other Names: Dvorák Antonin Academic Editor.
• Is Part Of: Advances in fuzzy systems. Volume 2020(2020)
• Publication Date: 2020-08-25
• Subjects: Fuzzy systems -- Periodicals; Systèmes flous; Fuzzy systems; Periodicals; 511.313
• DOI: 10.1155/2020/6539123
• Languages: English
• ISSNs: 1687-7101

Abstract

Abstract : The paper aims to propose a distributed method for machine learning models and its application for medical data analysis. The great challenge in the medicine field is to provide a scalable image processing model, which integrates the computing processing requirements and computing-aided medical decision making. The proposed Fuzzy logic method is based on a distributed approach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one model. Accordingly, the method is assigned to be implemented on big data analysis and data science prediction models for healthcare applications. The paper focuses on the proposed distributed Type-2 Fuzzy Logic (DT2FL) method and its application for MRI data analysis under a massively parallel and distributed virtual mobile agent architecture. Indeed, the paper presents some experimental results which highlight the accuracy and efficiency of the proposed method.