Intelligent Data Mining for Translator Correctness Prediction
This paper presents a new approach to predictive data analytics, called Radius of Neighbors (RN), and its mobile application, a multilingual RN-Chatter, devoted to improve communication among people, speaking different languages. RN is a modeless method of unsupervised machine learning, what makes it a fairly simple but effective way of analyzing big amounts of data while keeping acceptable speed of execution and taking up little run-time space. In the first preparatory stage of our research, we discovered that RN gives better results than well-known K-Nearest Neighbors (KNN) in some cases. We then extended our research to simulating the adjustments of floating radiuses, various volumes of the training data sets and ups and downs of the dimensions to tune RN for its optimum accuracy. We took experimental approach of not only extending the number of dimensions, but, instead, shrinking and modifying them in order to keep the predicted value' neighbors close by.
Proceedings - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
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Rossikova, Yulia; Li, J. Jenny; and Morreale, Patricia, "Intelligent Data Mining for Translator Correctness Prediction" (2016). Kean Publications. 1727.