A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics
Joint Authors
Benchara, Fatéma Zahra
Youssfi, Mohamed
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-25
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Benchara, Fatéma Zahra& Youssfi, Mohamed. 2020. A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics. Advances in Fuzzy Systems،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1126379
Modern Language Association (MLA)
Benchara, Fatéma Zahra& Youssfi, Mohamed. A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics. Advances in Fuzzy Systems No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1126379
American Medical Association (AMA)
Benchara, Fatéma Zahra& Youssfi, Mohamed. A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics. Advances in Fuzzy Systems. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1126379
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126379