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In this study, the Python language was used to build a linear SVM model with C value of one for identifying slippery roads. Deep learning, based on Artificial Neural Networks ANNs, is a sort of machine learning algorithm that utilizes multiple layers to analyze a high level of features from a set of examples. ANNs, inspired by information processing of biological brains, are computing systems with organized communication nodes. Among the learning methods are supervised, semi supervised, or unsupervised approaches. The term deep refers to the number of layers through which features are progressively extracted from the input. In other words, Credit Assignment Path CAP is included in deep learning architectures. The CAP is a chain of transformations that connects between input and output. The widely recognized deep learning categories are deep neural networks, recurrent neural networks, and convolutional neural networks, which have been applied to various fields such as image processing, speech/audio recognition, time series data analysis, where they have resulted in outstanding performances, in some cases, superior to human capabilities . The deep learning model constructed in this study consists of ten hidden layers with ten nodes in each layer with a rectified linear unit function, an adaptive moment estimation optimizer, and the Xavier initializer. To avoid overfitting, a drop out rate was set at 0. 7.

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