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- W4308654624 abstract "The purpose of this study is to use machine learning approaches to improve the accuracy of modern road accident prediction systems like the K-Nearest Neighbour Algorithm and Artificial Neural Networks Algorithm. Materials and techniques used include the K-Nearest Neighbour technique and the Artificial Neural Networks algorithm with sample size N=10, iterated 20 times in parallel to test the accuracy of forecasting road accidents. p0.05 indicates the significance of the K-Nearest Neighbour method. When comparing the results of the two algorithms, it is discovered that the K-Nearest Neighbour approach (81.22%) outperforms the Artificial Neural Networks algorithm (69.22%) in terms of accuracy in forecasting road accidents." @default.
- W4308654624 created "2022-11-13" @default.
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- W4308654624 date "2022-10-13" @default.
- W4308654624 modified "2023-10-18" @default.
- W4308654624 title "Comparative Analysis using K - Nearest Neighbour with Artificial Neural Network to Improve Accuracy for Predicting Road Accidents" @default.
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- W4308654624 doi "https://doi.org/10.1109/icecaa55415.2022.9936227" @default.
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