International Journal of Management and Applied Science (IJMAS)
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Statistics report
Nov. 2018
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 54
Paper Published : 3316
No. of Authors : 6857
  Journal Paper




Paper Title :
A Harmony Search-Based Learning Algorithm For Epileptic Seizure Prediction

Author :Kee Huong Lai, Zarita Zainuddin, Pauline Ong

Article Citation :Kee Huong Lai ,Zarita Zainuddin ,Pauline Ong , (2016 ) " A Harmony Search-Based Learning Algorithm For Epileptic Seizure Prediction " , International Journal of Management and Applied Science (IJMAS) , pp. 164-169, Volume-2,Issue-12, Special Issue-1

Abstract : The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. Apart from the traditional gradient descent-based approach, metaheuristic algorithms can also be used to determine these parameters. In this work, the harmony search algorithm is employed to find the optimal solution for both synaptic weight values and bias terms in the learning of wavelet neural network. The standard harmony search algorithm is modified accordingly in the aspect of initialization of harmony memory, as well as during the improvisation stage. The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in terms of sensitivity. Index Terms- Epileptic Seizure Prediction, Harmony Search, Learning Algorithm, Wavelet Neural Network.

Type : Research paper

Indexed : Google Scholar


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