D. Wang, J. R. Li, Y. H. Zhang, L. Chen, T. Huang, and Y. D. Cai, ‘Identification of differentially expressed genes between original breast cancer and xenograft using machine learning algorithms’, Genes (Basel)., vol. 9, no. 3, pp. 1–15, 2018, https://doi.org/10.3390/genes9030155.

Article  CAS  Google Scholar 

A. Yaqoob, M. A. Mir, G. V. V. J. Rao, and G. G. Tejani, ‘Transforming Cancer Classification : The Role of Advanced Gene Selection’, pp. 1–19, 2024.

I. A. Gheyas and L. S. Smith, ‘Feature subset selection in large dimensionality domains’, Pattern Recognit., vol. 43, no. 1, pp. 5–13, 2010, https://doi.org/10.1016/j.patcog.2009.06.009.

Article  Google Scholar 

A. Yaqoob, N. K. Verma, R. M. Aziz, and M. A. Shah, ‘RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques’, J. Cancer Res. Clin. Oncol., vol. 150, no. 10, p. 455, 2024, https://doi.org/10.1007/s00432-024-05968-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

A. Yaqoob, N. K. Verma, R. M. Aziz, and M. A. Shah, ‘Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights’, Cancer Immunol. Immunother., vol. 73, no. 12, p. 261, 2024, https://doi.org/10.1007/s00262-024-03843-x.

Article  PubMed  PubMed Central  Google Scholar 

A. Yaqoob, N. K. Verma, and R. M. Aziz, ‘Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach’, Multimed. Tools Appl., 2024. https://doi.org/10.1007/s11042-024-20146-6

A. Yaqoob, ‘Combining the mRMR technique with the Northern Goshawk Algorithm (NGHA) to choose genes for cancer classification’, Int. J. Inf. Technol., 2024. https://doi.org/10.1007/s41870-024-01849-3

M. Ali and T. Aittokallio, ‘Machine learning and feature selection for drug response prediction in precision oncology applications’, Biophys. Rev., vol. 11, no. 1, pp. 31–39, 2019, https://doi.org/10.1007/s12551-018-0446-z.

Article  CAS  PubMed  Google Scholar 

A. Yaqoob, N. Kumar, V. Rabia, and M. Aziz, ‘Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm’, J. Med. Syst., 2024. https://doi.org/10.1007/s10916-023-02031-1

A. Yaqoob, R. M. Aziz, N. K. Verma, P. Lalwani, and A. Makrariya, ‘A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification’, 2023.

V. C. Vinod and A. H. S, ‘Nature inspired meta heuristic algorithms for optimization problems’, Computing, vol. 104, no. 2, pp. 251–269, 2022, https://doi.org/10.1007/s00607-021-00955-5.

Article  Google Scholar 

A. Yaqoob, R. Musheer Aziz, and N. K. verma, ‘Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review’, Human-Centric Intell. Syst., 2023, https://doi.org/10.1007/s44230-023-00041-3.

A. Yaqoob, M. A. Bhat, and Z. Khan, ‘Dimensionality Reduction Techniques and their Applications in Cancer Classification : A Comprehensive Review’, vol. 1, no. 2, pp. 34–45, 2023.

A. Marik, S. Chattopadhyay, and P. K. Singh, ‘A hybrid deep feature selection framework for emotion recognition from human speeches’, Multimed. Tools Appl., vol. 82, no. 8, pp. 11461–11487, 2023, https://doi.org/10.1007/s11042-022-14052-y.

Article  Google Scholar 

D. E. Kvasov and M. S. Mukhametzhanov, ‘Metaheuristic vs. deterministic global optimization algorithms: The univariate case’, Appl. Math. Comput., vol. 318, pp. 245–259, 2018, https://doi.org/10.1016/j.amc.2017.05.014.

Article  Google Scholar 

H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro, and A. Liotta, ‘Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning’, Comput. Methods Programs Biomed., vol. 223, p. 106951, 2022, https://doi.org/10.1016/j.cmpb.2022.106951.

Article  PubMed  Google Scholar 

M. Ragab, A. Albukhari, J. Alyami, and R. F. Mansour, ‘Ensemble Deep-Learning-Enabled Clinical Decision Support Ultrasound Images’, Biology (Basel)., vol. 11, p. 439, 2022.

Article  PubMed  PubMed Central  Google Scholar 

B. Sahu, S. N. Mohanty, and S. K. Rout, ‘EAI Endorsed Transactions on Scalable Information System s A H ybrid Approach for Breast Cancer Classification and Diagnosis’, EAI Endorsed Trans. Scalable Inf. Syst., pp. 1–8, 2019.

N. Sharma, K. P. Sharma, M. Mangla, and R. Rani, ‘Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding’, Multimed. Tools Appl., vol. 82, no. 3, pp. 4011–4029, 2023, https://doi.org/10.1007/s11042-022-13419-5.

Article  Google Scholar 

J. Wu, ‘Breast Cancer Type Classification Using Machine Learning’, 2021.

M. Sewak, P. Vaidya, C.-C. Chan, and Z.-H. Duan, ‘SVM Approach to Breast Cancer Classification’, in Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007), 2007, pp. 32–37. https://doi.org/10.1109/IMSCCS.2007.46.

Y. N. Tan, V. P. Tinh, P. D. Lam, N. H. Nam, and T. A. Khoa, ‘A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework’, IEEE Access, vol. 11, pp. 27462–27476, 2023, https://doi.org/10.1109/ACCESS.2023.3257562.

Article  Google Scholar 

G. Kyrou, V. Charilogis, and I. G. Tsoulos, ‘Improving the Giant-Armadillo Optimization Method’, Analytics, vol. 3, no. 2, pp. 225–240, 2024, https://doi.org/10.3390/analytics3020013.

Article  Google Scholar 

O. Alsayyed et al., ‘Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems’, Biomimetics, vol. 8, no. 8, 2023, https://doi.org/10.3390/biomimetics8080619.

K. Jensen et al., ‘A Low Cost, Modular Robotics Tool Carrier For Precision Agriculture Research’, Proceeding Int. Confeence Precis. Agric., 2012.

M. C. Matos, A. Pinheiro, R. S. Davis, and P. J. Esteves, ‘Evidence for Extensive Duplication and Subfunctionalization of FCRL6 in Armadillo (Dasypus novemcinctus)’, Int. J. Mol. Sci., vol. 24, no. 5, 2023, https://doi.org/10.3390/ijms24054531.

Y. Guo, W. Zhan, and W. Li, ‘Application of Support Vector Machine Algorithm Incorporating Slime Mould Algorithm Strategy in Ancient Glass Classification’, Appl. Sci., vol. 13, no. 6, 2023, https://doi.org/10.3390/app13063718.

K. R. Pradeep and N. C. Naveen, ‘Lung Cancer Survivability Prediction based on Performance Using Classification Techniques of Support Vector Machines, C4.5 and Naive Bayes Algorithms for Healthcare Analytics’, Procedia Comput. Sci., vol. 132, pp. 412–420, 2018, https://doi.org/10.1016/j.procs.2018.05.162.

Article  Google Scholar 

A. Bilal et al., ‘Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification’, PLoS One, vol. 19, no. 1, p. e0295951, Jan. 2024, [Online]. Available: https://doi.org/10.1371/journal.pone.0295951

M. Amrane, S. Oukid, I. Gagaoua, and T. Ensarİ, ‘Breast cancer classification using machine learning’, in 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 2018, pp. 1–4. https://doi.org/10.1109/EBBT.2018.8391453

A. Bilal, A. Imran, T. I. Baig, X. Liu, E. Abouel Nasr, and H. Long, ‘Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization’, Sci. Rep., vol. 14, no. 1, pp. 1–25, 2024, https://doi.org/10.1038/s41598-024-61322-w.

A. A. Ahmed, M. A. S. Ali, and M. Selim, ‘Bio-inspired based techniques for thermogram breast cancer classification’, Int. J. Intell. Eng. Syst., vol. 12, no. 2, pp. 114–124, 2019, https://doi.org/10.22266/IJIES2019.0430.12.

Article  Google Scholar 

T. I. A. Mohamed, A. E. Ezugwu, J. V. Fonou-Dombeu, A. M. Ikotun, and M. Mohammed, ‘A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data’, Sci. Rep., vol. 13, no. 1, pp. 1–19, 2023, https://doi.org/10.1038/s41598-023-41731-z.

Article  CAS  Google Scholar 

F. S. Khan, M. I. Abbasi, M. Khurram, M. N. Haji Mohd, and M. D. Khan, ‘Breast cancer histological images nuclei segmentation and optimized classification with deep learning’, Int. J. Electr. Comput. Eng., vol. 12, no. 4, pp. 4099–4110, 2022, https://doi.org/10.11591/ijece.v12i4.pp4099-4110.

B. Wei, Z. Han, X. He, and Y. Yin, ‘Deep learning model based breast cancer histopathological image classification’, in 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2017, pp. 348–353. https://doi.org/10.1109/ICCCBDA.2017.7951937.