Name: Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification From eeg Signals (en Inglés)
SKU: 62474036
Price: 47.90 USD
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Author: Harikumar Rajaguru; Sunil Kumar Prabhakar

portada Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification From eeg Signals (en Inglés)

Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification From eeg Signals (en Inglés)

Formato

Libro Físico

Editorial

Anchor Academic Publishing

Autor

Harikumar Rajaguru; Sunil Kumar Prabhakar

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783960671220

Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification From eeg Signals (en Inglés)

Harikumar Rajaguru; Sunil Kumar Prabhakar (Autor) · Anchor Academic Publishing · Tapa Blanda

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Reseña del libro "Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification From eeg Signals (en Inglés)"

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.