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The ability to measure students' engagement in an educational setting may facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this paper, a real-time automatic student engagement measure is proposed through investigating two of the main components of engagement: the behavioral engagement and the emotional engagement. A biometric sensor network (BSN) consisting of web cameras, a wall-mounted camera and a high-performance computing machine was designed to capture students' head poses, eye gaze, body movements, and facial emotions. These low-level features are used to train an AI-based model to estimate the behavioral and emotional engagement in the class environment. A set of experiments was conducted to compare the proposed technology with the state-of-the-art frameworks. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. In addition, it offers superior flexibility to work in any educational environment. Further, this approach allows a quantitative comparison of teaching methods.

Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides "filet"-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.