# Actionable Pattern-Driven Analytics and Prediction
## 2 Contributions  
The first paper [1] proposes a driver behavior analysis system using one spatial stream ConvNet to extract the spatial features and one temporal stream ConvNet to capture the driver's motion information. The two-dimensional (2D) ConvNet is used to construct the spatial and temporal ConvNet streams, and they were pre-trained by the large-scale ImageNet. In order to integrate different modalities, the feature-level fusion methodology was applied, and a fusion network was designed to integrate the spatial and temporal features for further classification. Moreover, a self-compiled dataset of 10 actions in the vehicle was established. Results showed that the proposed system can increase the accuracy rate by nearly 30% compared to the two-stream CNN model with a score-level fusion. The second paper [2] proposes an integrated deep network consisting of a detection and identification module for person search. In real situations, person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and most existing works take cropped pedestrian images either from manual labelling or a perfect detection assumption. Thus, a framework for the practical surveillance scenarios in which the scene images are captured is designed. Additionally, the detection module based on the Faster R-CNN is used to detect persons in a scene image. For identifying and extracting discriminative features, a multi-class CNN network is trained with the auto-detected bounding boxes from the detection module instead of the manually cropped data. The distance metric is then learned from the discriminative features output by the identification module. According to the experimental results of the test performed in the scene images, the multi-class CNN network for the identification module can provide a 62.7% accuracy rate, which is higher than that for the two-class CNN network. The third paper [3] focuses on the ontology-based tourist knowledge representation and recommendation method. The designed model is used to search for popular attractions from the online travel reviews (OTR) content and construct a tourist knowledge structure for these travelers. When the tourists do not need to know the keywords of the popularattraction name, they only need to acquire their current location; the ORT content will then recommend the next attraction to the traveler, which helps the traveler make the correct travel decision. The evaluation result showed that the method proposed in this study can help travelers to quickly make travel decisions and is better than the traditional searching methods. The fourth paper [4] builds an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners' personality patterns. For learner pattern mining, the relationships between the students' learning results and the referred personalities are discovered to provide the learners with valuable information before learning commences. For filtering learner personality patterns, students with similar personality patterns are filtered to predict the potential learning results. Through the actual system, a number of subjective and objective evaluations were conducted, and the evaluation results reveal that the proposed system is highly effective and reliable. The fifth paper [5] proposes an electric energy consumption prediction model utilizing the combination of convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM), which is named the EECP-CBL model, to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, the Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions, including the forward and backward states, to make predictions.