Fig 1.
Illustration of communication in vehicular network.
Fig 2.
Proposed framework.
Table 1.
Summary of dataset records for each class.
Table 2.
Summary of dataset records for each class.
Table 3.
Summary of data partitioning and label distribution for binary classification.
Table 4.
Summary of data partitioning label distribution for multi-label classification.
Fig 3.
Fully connected neural network.
Table 5.
Summary of hyperparameters.
Table 6.
Experimental environment.
Fig 4.
Loss function and F1 score without feature selection.
Fig 5.
Training and validation graphs in terms of F1-score and loss for the five experiments.
(a) Loss function and F1 score CFS95. (b) Loss function and F1 score CFS90. (c) Loss function and F1 score PCA95. (d) Loss function and F1 score PCA90.
Table 7.
Summary of results for binary classification performance and model quantization for various feature engineering and dimensionality reduction experiments.
Table 8.
Summary of results for multi-label classification performance and model quantization for various feature engineering and dimensionality reduction experiments.