Software dependability analysis under neutrosophic environment using optimized Elman recurrent neural network-based classification algorithm and Mahalanobis distance-based ranking algorithm
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10479-024-05888-8.pdf
Reference59 articles.
1. Ab Aziz, M. F., Mostafa, S. A., Cik, C. F., Mohammed, M. A., Elhoseny, M., & Abualkishik, A. (2021). Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2021.115441
2. Abaei, G., Selamat, A., & Al Dallal, J. (2020a). A fuzzy logic expert system to predict module fault proneness using unlabeled data. Journal of King Saud University Computer and Information Sciences, 32(6), 684–699. https://doi.org/10.1016/j.jksuci.2018.08.003
3. Abaei, G., Selamat, A., & Al, J. (2020b). A fuzzy logic expert system to predict module fault proneness using unlabeled data. Journal of King Saud University Computer and Information Sciences, 32(6), 684–699. https://doi.org/10.1016/j.jksuci.2018.08.003
4. Ansari, A. Q., Biswas, R., & Aggarwal, S. (2013). Neutrosophic classifier: An extension of fuzzy classifer. Applied Soft Computing Journal, 13(1), 563–573. https://doi.org/10.1016/j.asoc.2012.08.002
5. Avižienis, A., Laprie, J. C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11–33. https://doi.org/10.1109/TDSC.2004.2
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