A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics

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 deci...

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Bibliographic Details
Published in:Advances in fuzzy systems 2020, Vol.2020 (2020), p.1-10
Main Authors: Benchara, Fatéma Zahra, Youssfi, Mohamed
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Algorithms
Artificial intelligence
Big Data
Clustering
Collaboration
Computation
Cooperation
Data analysis
Data science
Datasets
Decision making
Decision making models
Fraud
Fuzzy logic
Image processing
Machine learning
Prediction models
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Summary: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.
ISSN:1687-7101
1687-711X
DOI:10.1155/2020/6539123