Vehicular Ad Hoc Networks (VANETs) is a subsystem of the proposed Intelligent Transportation System (ITS) that enables vehicles to communicate over the wireless communication infrastructure. VANETs are used in multiple applications, such as improving traffic safety and collision prevention. The use of VANETs makes the network vulnerable to various types of attacks, such as Denial of Service (DoS) and Distributed Denial of Service (DDoS). Many researchers are now interested in adding a high level of security to VANETs. Machine Learning (ML) methods were used for constructing a high level of security capabilities based on Intrusion Detection Systems (IDSs). Furthermore, the vast majority of existing research is based on NSL-KDD or KDD-CUP99 datasets. Recent attacks are not present in these datasets. As a result, we employed a realistic dataset called ToN-IoT that derived from a large-scale, heterogeneous IoT network. This work tested various ML methods in both binary and multi-class classification problems. We used the Chi-square (Chi2) technique was used for feature selection and the Synthetic Minority Oversampling Technique (SMOTE) for class balancing. According to the results, the XGBoost method outperformed other ML methods.

Wood is an important raw material used in various activities, such as building, furniture, and fuel. The timber industry is significant in many countries and has a significant financial impact. There are diverse categories of wood, each with its unique properties, and experts typically perform wood species identification through visual inspection, which is a tedious and time-consuming process. To eliminate the need for manual detection, a deep learning-based wood Classifica-tion System was proposed in this paper. The system uses a transfer learn-ing-based convolutional neural network model that handles feature extraction. Compared to other transfer learning models such as VGG16, ResNet50, and DenseNet201, the proposed EfficientNetB7 model achieved a high validation accuracy of 99.824%, which suggests that it can be used to aid unskilled agents in wood categorization. This new strategy can save time and effort in the identi-fication of wood species, making it an efficient method for the timber industry.