Computing, Mathematics and Engineering
Darren is a post-doctoral research data scientist and full-stack web developer.
He has joined the Gulbali Institute to develop decision support and machine-learning tools for agriculture in a FoodAgility CRC research project, in conjunction with Sunrice and AgriFutures.
Darren created the CLOWD (Combined Location Online Weather Data) web app - free to use - https://clowd.csu.edu.au.
and the RiversNearMe web app for NSW water-level and flow-rate data - free to use - https://riversnearme.csu.edu.au
He completed a PhD in data science/machine-learning (graduated 8 February 2021). His research topic: new data-mining and exploration techniques on smartphones, including mixed-reality and distributed data mining (received the Faculty Outstanding Thesis Prize for 2021 academic year).
Yates, D., & Islam, Z. (2022). Data mining on smartphones: An introduction and survey. ACM Computing Surveys, 55(5), 1-38. Article 101. Advance online publication. https://doi.org/10.1145/3529753
Yates, D. (2021). Development and Implementation of Locally-Executed Data Mining on Smartphones. [Doctoral Thesis, Charles Sturt University].
Yates, D., & Islam, M. Z. (2021). FastForest: Increasing random forest processing speed while maintaining accuracy. Information Sciences, 557, 130-152. Advance online publication. https://doi.org/10.1016/j.ins.2020.12.067
Yates, D., Islam, Z., Zhao, Y., Nayak, R., Estivill-Castro, V., & Kanhere, S. (2021). PostMatch: A framework for efficient address matching. In Y. Xu, A. Lord, R. Nayak, G. Williams, R. Wang, Y. L. Boo, & Y. Zhao (Eds.), Data mining: 19th Australasian conference on data mining, AusDM 2021, proceedings (Vol. 1504, pp. 136-151). (Communications in Computer and Information Science; Vol. 1504). Springer. Advance online publication. https://doi.org/10.1007/978-981-16-8531-6_10
Yates, D., Islam, Z., & Gao, J. (2019). DataLearner: A data mining and knowledge discovery tool for android smartphones and tablets. In J. Li, S. Wang, S. Qin, X. Li, & S. Wang (Eds.), Advanced data mining and applications: 15th international conference, ADMA 2019, proceedings (Vol. 11888, pp. 828-838). (Lecture Notes in Computer Science; Vol. 11888). Springer. https://doi.org/10.1007/978-3-030-35231-8_61