Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in which cameras in closed-circuit television (CCTV) systems can provide richer measurements. However, existing camera-based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to develop a novel deep-learning-based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep-learning model to detect the number of occupants and determine their locations in videos. This model consists of two main modules, namely, feature extraction and three-stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch convolutional calculation to extract shallow and semantic features, and constructs feature pyramids through a bidirectional feature network. The second module performs a three-stage detection procedure with three sequential and homogeneous detectors which have increasing Intersection over Union (IoU) thresholds. Empirical experiments evaluate the detection performance of the approach with CCTV videos from a university building. Experimental results show that the approach achieves the superior detection performance when compared with baseline models.

Online health-related discussion provides a rich source of information for both informing the public and providing feedback to health professionals to detect trends and inform policy. However, there are few studies that focus on analysing sentiment in medical forum discourse. Online health communities devoted to specific medical conditions and health-related problems support people with similar conditions, enabling them to exchange personal experiences. Analysing sentiment expressed by members of a health community in medical forum discourse can be valuable for identifying a particular aspect of the information space. In this paper, we identify sentiments expressed on online medical forums discussing Lyme disease. There are two goals in our research. First, to identify a set of categories that can represent a comprehensive connotation of emotions expressed in the discussions, while also being adequately distinct for the purposes of machine learning. Second, to identify the sentiments expressed by participants in individual posts. Three types of feature (content-free, content-specific and meta-level) are extracted and inductive learning algorithms utilized to build a feature-based classification model for an automated multi-class classification model. The experimental results demonstrate the effectiveness of our approach.