References
- Kumar, A., Mukherjee, S. and Luhach, A.K., 2019. Deep learning with perspective modeling for early detection of malignancy in mammograms. Journal of Discrete Mathematical Sciences and Cryptography, 22(4), pp.627-643. doi: https://doi.org/10.1080/09720529.2019.1642624
- Rani, S. and Masood, S., 2020. Predicting congenital heart disease using machine learning techniques. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), pp.293-303. doi: https://doi.org/10.1080/09720529.2020.1721862
- Zheng, B., Yoon, S.W., Lam, S.S., 2014. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41 (4 PART 1), 1476–1482. doi: https://doi.org/10.1016/j.eswa.2013.08.044
- Garcia-Laencina, P.J.; Sancho-Gomez, J.-L.; Figueiras-Vidal, A.R. Pattern classification with missing data: A review. Neural Comput. Appl. 2010, 19, 263–282. doi: https://doi.org/10.1007/s00521-009-0295-6
- Baraldi, A.N. and Enders, C.K., 2010. An introduction to modern missing data analyses. Journal of school psychology, 48(1), pp.5-37. doi: https://doi.org/10.1016/j.jsp.2009.10.001
- Ghorbani, R. and Ghousi, R., 2019. Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science, 3(2), pp.47-70. doi: https://doi.org/10.5267/j.ijdns.2019.1.003
- Jiao, Y. and Zhao, S., 2016. Object tracking from airborne video using particle filters algorithm on dynamic feature fusion. Journal of Discrete Mathematical Sciences and Cryptography, 19(3), pp.787-799. doi: https://doi.org/10.1080/09720529.2016.1178938
- Kshirsagar, D. and Kumar, S., 2020. An ensemble feature reduction method for web-attack detection. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), pp.283-291. doi: https://doi.org/10.1080/09720529.2020.1721861
- Rostami, M., Forouzandeh, S., Berahmand, K. and Soltani, M., 2020. Integration of multi-objective PSO based feature selection and node centrality for medical datasets. Genomics, 112(6), pp.4370-4384. doi: https://doi.org/10.1016/j.ygeno.2020.07.027
- Brezočnik, L., Fister, I. and Podgorelec, V., 2018. Swarm intelligence algorithms for feature selection: a review. Applied Sciences, 8(9), p.1521. doi: https://doi.org/10.3390/app8091521
- Bai, B.M., Mangathayaru, N. and Rani, B.P., 2015, An approach to find missing values in medical datasets. In Proceedings of the International Conference on Engineering & MIS 2015 (pp. 1-7).
- Rachmawan I. E. W. and A. R. Barakbah, “Optimization of missing value imputation using reinforcement programming,” in Proc. Int. Electron. Symp. (IES), Sep. 2015, pp. 128–133.
- Dzulkalnine, M.F. and Sallehuddin, R., 2019. Missing data imputation with fuzzy feature selection for diabetes dataset. SN Applied Sciences, 1(4), pp.1-12. doi: https://doi.org/10.1007/s42452-019-0383-x
- Hu, Z. and Du, D., 2020. A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction. Plos one, 15(9), p.e0237724. doi: https://doi.org/10.1371/journal.pone.0237724
- Liu, C.H., Tsai, C.F., Sue, K.L. and Huang, M.W., 2020. The feature selection effect on missing value imputation of medical datasets. Applied Sciences, 10(7), pp.1-12.
- Ramezani, R., Maadi, M. and Khatami, S.M., 2018. A novel hybrid intelligent system with missing value imputation for diabetes diagnosis. Alexandria engineering journal, 57(3), pp.1883-1891. doi: https://doi.org/10.1016/j.aej.2017.03.043
- Mostafa, S.M., Eladimy, A.S., Hamad, S. and Amano, H., 2020. CBRG: A Novel Algorithm for Handling Missing Data Using Bayesian Ridge Regression and Feature Selection Based on Gain Ratio. IEEE Access, 8, pp.216969-216985. doi: https://doi.org/10.1109/ACCESS.2020.3042119
- Alhroob, A., Alzyadat, W., Almukahel, I. and Altarawneh, H., 2020. Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach. population, 11(2), pp.703-709.
- Franzin, A., Sambo, F., Di Camillo, B., 2017. bnstruct: an R package for Bayesian Network structure learning in the presence of missing data. Bioinformatics 33 (8), 1250–1252.
- Dauwels, J., Garg, L., Earnest, A. and Pang, L.K., 2012, Tensor factorization for missing data imputation in medical questionnaires. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2109-2112.
- Yang, F., Shang, F., Huang, Y., Cheng, J., Li, J., Zhao, Y. and Zhao, R., 2017. LFTF: A framework for efficient tensor analytics at scale. Proceedings of the VLDB Endowment, 10(7), pp.745-756. doi: https://doi.org/10.14778/3067421.3067424
- Vazifehdan, M., Moattar, M.H. and Jalali, M., 2019. A hybrid Bayesian network and tensor factorization approach for missing value imputation to improve BC recurrence prediction. Journal of King Saud University-Computer and Information Sciences, 31(2), pp.175-184. doi: https://doi.org/10.1016/j.jksuci.2018.01.002
- Kavitha, D. and Radha, V., 2021. Texnet: A Deep Convolutional Neural Network Model To Recognize Text In Natural Scene Images. Journal Of Engineering Science And Technology, 16(2), Pp.1782-1799.