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Ma, G., & Pan, X. (2021). Research on a visual comfort model based on individual preference in china through machine learning algorithm. Sustainability, 13(14), 7602.Search in Google Scholar
Kim, J. N., Homero Gil de Zúiga, Oh, Y. W., & Park, C. H. (2021). Machine cleaning of online opinion spam: developing a machine-learning algorithm for detecting deceptive comments:. American Behavioral Scientist, 65(2), 389-403.Search in Google Scholar
Janani, B., & S. Vijayarani, M. (2019). Artificial bee colony algorithm for feature selection and improved support vector machine for text classification. Interlending & document supply, 47(3), 154-170.Search in Google Scholar
Ling, Q. H., Song, Y. Q., Han, F., Zhou, C. H., & Lu, H. (2018). An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity. Cognitive Systems Research, S1389041717302929.Search in Google Scholar
JBD Soland. (2020). Chapter 3: using machine learning to advance early warning systems-promise and pitfalls. Teachers College Record, 122.Search in Google Scholar
Beyzaei, N., Bao, S., Bu, Y., Hung, L., & Ipsiroglu, O. S. (2020). Is fidgety philip’s ground truth also ours? the creation and application of a machine learning algorithm. Journal of Psychiatric Research, 131(8).Search in Google Scholar
Arribas-Bel, D., Miquel-ngel Garcia-López, & Viladecans-Marsal, E. (2019). Building(s and) cities: delineating urban areas with a machine learning algorithm. Journal of Urban Economics, 103217.Search in Google Scholar
A, I. L., & B, Y. J. S. (2020). Machine learning for enterprises: applications, algorithm selection, and challenges - sciencedirect. Business Horizons, 63( 2), 157-170.Search in Google Scholar
Mostafaeipour, A., Rafsanjani, A. J., Ahmadi, M., & Dhanraj, J. A. (2021). Investigating the performance of hadoop and spark platforms on machine learning algorithms. The Journal of Supercomputing, 77(2), -.Search in Google Scholar
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., & Pham, B. T. (2020). Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Science Reviews.Search in Google Scholar
Iv, W. C. S., & Krawczyk, B. (2020). Multi-class imbalanced big data classification on Spark. Knowledge-Based Systems, 212.Search in Google Scholar
Wen, X., & Juan, H. (2020). Psubclus: a parallel subspace clustering algorithm based on Spark. IEEE Access, PP(99), 1-1.Search in Google Scholar
Sun, B., & Alkhalifah, T. (2020). Ml-descent: an optimization algorithm for fwi using machine learning. Geophysics.Search in Google Scholar
Jogarah, K. K., Soopaul, K., Beeharry, Y., & Hurbungs, V. (2018). Hybrid machine learning algorithms for fault detection in android smartphones. Transactions on Emerging Telecommunications Technologies, e3272.Search in Google Scholar
Ali, R., Lee, S., & Chung, T. C. (2017). Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Expert Systems with Applications, 71, 257-278.Search in Google Scholar
Piri, S., Delen, D., & Liu, T. (2018). A synthetic informative minority over-sampling (simo) algorithm leveraging support vector machine to enhance learning from imbalanced datasets. Decision Support Systems, S016792361730218X.Search in Google Scholar
Kamburugamuve, S., Wickramasinghe, P., Ekanayake, S., & Fox, G. C. (2017). Anatomy of machine learning algorithm implementations in mpi, Spark, and flink. International Journal of High Performance Computing Applications, 109434201771297.Search in Google Scholar
Ding, S., Zhang, Z., Sun, Y., & Shi, S. (2022). Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithm. Neurocomputing, 480, 146-156.Search in Google Scholar
Huimin, Y. (2021). Research on parallel support vector machine based on spark big data platform. Scientific Programming.Search in Google Scholar