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Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification

Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification

Rehan Ullah, Abdullah Khan, Syed Bakhtawar Shah Abid, Siyab Khan, Said Khalid Shah, Maria Ali
Copyright: © 2020 |Pages: 41
ISBN13: 9781799825210|ISBN10: 1799825213|EISBN13: 9781799825227
DOI: 10.4018/978-1-7998-2521-0.ch009
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MLA

Ullah, Rehan, et al. "Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification." Mobile Devices and Smart Gadgets in Medical Sciences, edited by Sajid Umair, IGI Global, 2020, pp. 173-213. https://doi.org/10.4018/978-1-7998-2521-0.ch009

APA

Ullah, R., Khan, A., Shah Abid, S. B., Khan, S., Shah, S. K., & Ali, M. (2020). Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification. In S. Umair (Ed.), Mobile Devices and Smart Gadgets in Medical Sciences (pp. 173-213). IGI Global. https://doi.org/10.4018/978-1-7998-2521-0.ch009

Chicago

Ullah, Rehan, et al. "Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification." In Mobile Devices and Smart Gadgets in Medical Sciences, edited by Sajid Umair, 173-213. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2521-0.ch009

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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.

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