Abstract
Aim: To improve the predictive accuracy and cost analysis for malicious behaviors in automated vehicle systems using the Hybrid Fuzzy C-Means algorithm (HFCM) and Neural Network algorithm (NN). Materials and Methods: Accuracy is performed with two groups Fuzzy C-Means Algorithm and the Neural Network algorithm of sample size per group (N = 125). G power 80% threshold 0.05%, CI 95%. Mean and Standard deviation. Result: Independent sample T-Test was carried out using Fuzzy C-Means and Neural Network. C-means (92.1%) perform better than NN (89.6%). There is a statistically significant difference between Fuzzy C-means and with (p<0.01). Conclusion: The results conclude that the proposed Fuzzy C-Means algorithm helps to identify and detect with better accuracy and cost percentage at event systems.
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