Analysis of Genetic Mutations Using Nature-Inspired Optimization Methods and Classification Approach-Reference-Cited by-同舟云学术

Analysis of Genetic Mutations Using Nature-Inspired Optimization Methods and Classification Approach

Published:2023-12-02 Issue: Volume: Page:39-65
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Container-title:Nature-Inspired Methods for Smart Healthcare Systems and Medical Data
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Author:

Thakare Anuradha,Narkhede Pradnya,Adrakatti Sahil S.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-45952-8_3

Reference30 articles.

1. Anter, A. M., & Hassenian, A. E. (2018). Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation. Journal of Computational Science, 25, 376–387.

2. ElSoud, M. A., & Anter, A. M. (2016). Computational intelligence optimization algorithm based on meta-heuristic social-spider: Case study on CT liver tumor diagnosis. International Journal of Advanced Computer Science and Applications, 7(4), 466.

3. Anter, A. M., Hassanien, A. E., ElSoud, M. A., & Kim, T. H. (2015, July). Feature selection approach based on social spider algorithm: Case study on abdominal CT liver tumor. In 2015 seventh international conference on advanced communication and networking (ACN) (pp. 89–94). IEEE.

4. Anter, A. M., Moemen, Y. S., Darwish, A., & Hassanien, A. E. (2020). Multi-target QSAR modelling of chemo-genomic data analysis based on extreme learning machine. Knowledge-Based Systems, 188, 104977.

5. Thakare, A., Anter, A. M., & Abraham, A. (2023). Seizure disorders recognition model from EEG signals using new probabilistic particle swarm optimizer and sequential differential evolution. Multidimensional Systems and Signal Processing, 34, 1–25.