Mr. Qiushi Xu Profile

Qiushi Xu

at McMaster University

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Proceedings Article | 7 October 2022 Paper

KNN vs. DNN: auto chatbot

Qiushi Xu

Proceedings Volume 12344, 123440Y (2022) https://doi.org/10.1117/12.2655749

KEYWORDS: Neural networks, Speech recognition, Detection and tracking algorithms, Data modeling, Machine learning, Clouds, Brain, Wireless communications, Multilayers, Logic

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Machine learning has played a very important role these days. A very common area related to daily life is auto chatbots, such as Apple Siri, Google Home, Mi Xiaoai, and others. These technologies all use deep learning as the foundation to achieve the goal of communicating with humans. Engineers and computer scientists put a massive amount of effort into trying to improve the quality of chatbots by improving the models that drive this feature. From previous research, KNN (K-nearest neighbors) and DNN (Deep Neural Network) are two widely used models in the machine learning area. To find out which is more efficient to manage auto-chat while understanding deeper how chatbots actually manage to recognize human language and quickly come up with corresponding answers, the two learning models were applied to a self-made chatbot. By comparing the effectiveness of applying K-nearest neighbors and Deep Neural Network, the paper finds that KNN runs faster and takes less space since it is a comparatively simpler algorithm. In terms of correctness, in this experiment, both algorithms turned out to have a similar percentage of correctness. This might be caused by a comparatively small dataset.

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