This paper aims to apply a YOLOv3 technique for detecting and recognizing Thai traffic signs in real-time environments. The Thai Traffic Sign Dataset (TTSD) was collected by car cameras to store the video images using the resolution of 1920 × 1080 pixels using 60 frames per second, and a 1280 × 720 pixels and 30 frames per second. In addition, the data was collected in the rural area of Maha Sarakham Province and Kalasin Province. The dataset was generated and distributed for general traffic sign detection and recognition. Two architectures (YOLOv3 and YOLOv3 Tiny) are compared with 50 classes of road signs and 200 badges in each class, containing 9,357 images. The experiment shows that the mean average precision (mAP) of YOLOv3 (88.10

Social network has become a very popular communication for Thai people, especially Facebook. Unfortunately, this popularity also attracts deceiver spreading malicious messages to other users. Some messages lead to deception. This paper studies Thai messages posted on Facebook that lead to deception. We try to investigate different approaches to detect deceptive messages and find dominant words. To detect deceptive messages, the dataset is retrieved from Facebook pages. Next, content-based and context-based features are extracted from the dataset. Two algorithms, i.e. SVM and KNN, are applied to perform a prediction. We construct the experiments to investigate context-based and content-based features for detecting deceptive messages. The experimental results show that the context-based features gives the best performance and the F-measure for predicting deceptive messages achieves 99 % when using SVM classifier. In addition, dominant words in deceptive messages and truthful messages are reported in our work.