Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level

Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO...
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level
Harikumar Rajaguru, Sunil Kumar Prabhakar
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes’ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.

This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ...
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Produktinformationen

Artikelnummer:
SW9783960676225
Autor:
Harikumar Rajaguru, Sunil Kumar Prabhakar
Verlag:
Anchor Academic Publishing
Seitenzahl:
47
Veröffentlichung:
17.02.2017
ISBN:
9783960676225
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