Elman Neural Network (ENN) is a feedback neural network that is enhanced by Elman in 1990. ENN is based on the study of the backpropagation neural network (BPNN). The physical layout of the Elman neural network is divided broadly into 4 layers: the input layer, the hidden layer, the Undertake layer, and the output layer. The purpose of undertake layer is to memorize the hidden layer output. As it is based on a backpropagation neural network, the output of the hidden layer connects with its input via the delay and memory of undertake layer.
Published in Chapter:
Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification
Rehan Ullah (The University of Agriculture, Peshawar, Pakistan), Abdullah Khan (The University of Agriculture, Peshawar, Pakistan), Syed Bakhtawar Shah Abid (The University of Agriculture, Peshawar, Pakistan), Siyab Khan (The University of Agriculture, Peshawar, Pakistan), Said Khalid Shah (Department of Computer Science, University of Science and Technology, Bannu, Pakistan), and Maria Ali (The University of Agriculture, Peshawar, Pakistan)
Copyright: © 2020
|Pages: 41
DOI: 10.4018/978-1-7998-2521-0.ch009
Abstract
DNA sequence classification is one of the main research activities in bioinformatics on which, many researchers have worked and are working on it. In bioinformatics, machine learning can be applied for the analysis of genomic sequences like the classification of DNA sequences, comparison of DNA sequences. This article proposes a new hybrid meta-heuristic model called Crow-ENN for leukemia DNA sequences classification. The proposed algorithm is the combination of the Crow Search Algorithm (CSA) and the Elman Neural Network (ENN). DNA sequences of Leukemia are used to train and test the proposed hybrid model. Five other comparable models i.e. Crow-ANN, Crow-BPNN, ANN, BPNN and ENN are also trained and tested on these DNA sequences. The performance of models is evaluated in terms of accuracy and MSE. The overall simulation results show that the proposed model has outperformed all the other five comparable models by attaining the highest accuracy of over 99%. This model may also be used for other classification problems in different fields because it can achieve promising results.