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An optimized Parkinson’s disorder identification through evolutionary fast learning network

International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 383-400页

作者： Bouslah Ayoub taleb nora LRI Laboratory Computer Science DepartmentUniversity of BadjiMokhtar AnnabaAnnabaAlgeria Complex System Engineering Laboratory Computer Science DepartmentUniversity of Badji Mokhtar AnnabaAnnabaAlgeria

Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative ***,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches ***,ML approaches required first to refine their parameters and then to work with the best model *** process often requires an expert user to oversee the performance of the ***,an attention is required towards new approaches for better forecasting ***/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification *** core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN ***-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark *** very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same *** results support that the proposed model can achieve the best performances in terms of accuracy and ***/value-A novel efficient PD detectionmodel is proposed,which is called *** A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection ***,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting *** helps the network to train on complex PD datasets with non-linear features and yields superior result.

关键词： Parkinson’s disease(PD) Fast learning network(FLN) Genetic algorithm(GA) Speech and handwriting patterns PD identification system

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Interactions-based method to detect emergent behavior in ongoing simulations

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第4期12卷 77-91页

作者： Ali Boukehila nora taleb Yazid Benazzouz LISCO Laboratory Badji Mokhtar-Annaba UniversityAnnabaAlgeria Amar Telidji Laghouat University LaghouatAlgeria

This study investigates the use of interactions as a metric to detect emergent phenomena in the ongoing agent-based *** proposed algorithm aims to detect the flocking behavior in the Boids *** model is simple to understand and to use and always verifies *** recent years,several metrics have been proposed to investigate emergent behaviors in complex *** main limitations of these studies are,how to choose a proper metric to avoid a high coast of computation,and how to detect emergence as it happens,i.e.,live *** this paper,we propose an interaction-based algorithm to detect emergent behavior in Boids *** three main contributions of this work are:(1)using interaction as metric,(2)detect emergence when it happens,(3)no need to store simulation data for post-mortem analysis.A prior interactions analysis was done to classify the system *** results show that the algorithm was able to detect the rise of the flocking,i.e.,emergence in the Boids *** results are provided to illustrate the effectiveness of the proposed approach.

关键词： Complex and intelligent systems adaptation multi-agent system observation emerging phenomena

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