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.
Highlights
Today, computer science technologies apply artificial intelligence and data science models in order to design new intelligent applications, such as fraud detection and recommendation engine. ese applications have to deal with big data sets, which need to be processed for extracting meaningful information and predict unknown patterns
To highlight the aim of this paper, we start with a brief overview of machine learning models [4, 5] and their ability to perform prediction, pattern recognition, and decision making. ese models provide the computers by the teaching behavior to learn from datasets and lead humans to convey their field expertise to them in order to design and implement smart systems, which are not explicitly programmed. ese, later, are able to collaborate with humans and bring relevant solutions
Reinforcement learning: the algorithms of this type make decisions according to their past experience e clustering algorithms are based on complex computing tasks and large datasets. ey have a variety of applications such as image segmentation, medical imaging, and anomaly detection. us, it seems that these methods play a great role in designing and implementing effective machine learning models. is means that their performance depends on the clustering’s algorithm scalability. e distributed clustering method is a new paradigm that allows performing tasks in distributed nodes
Summary
Computer science technologies apply artificial intelligence and data science models in order to design new intelligent applications, such as fraud detection and recommendation engine. ese applications have to deal with big data sets, which need to be processed for extracting meaningful information and predict unknown patterns. Ese applications have to deal with big data sets, which need to be processed for extracting meaningful information and predict unknown patterns To do so, they introduce the use of machine learning models based on complex algorithms such as classification and clustering algorithms. Clustering algorithms which are widely applied in the medicine field have been used by many researchers for MRI image classification As an illustration, they proposed a parallel Fuzzy c-means method for image segmentation analysis in [1] and for clustering large data sets on a parallel SPMD architecture using MPI tools in [2]. E paper focused on presenting a cooperative machine learning model based on the distributed type-2 Fuzzy method that combines computational processing requirements and the cognitive aspect. We will focus on presenting the proposed distributed DT2FL method (Section 4) and demonstrating its promising advantages through medical image analysis application (Section 5)
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