Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.

The unprecedented growth of the Internet, its pervasive accessibility and ease of use has increased students’ dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics such as the individual’s learning capability and readiness for specific learning materials. As a result, the same set of search-keywords deliver the same search results. This situation hinders the optimisation of the Web search engines in supporting the heterogeneity of its users in their learning endeavours. This paper aims to address the issue. It attempts to augment Web search engines with personalised recommendations of search results which match students’ learning competencies and behaviours. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students.

The implementation of blended learning in higher educational institutions is increasing due to its perceived effectiveness in affording the benefits of both face-to-face traditional mode and the fully online mode of instructions. However, the leading challenge associated with the online component of blended learning is students’ inability to properly self-regulate their learning activities as self-regulation has been an integral competence for the success in online environments. Research on supporting students’ self-regulation in the online component of blended learning has been on the rise, but these approaches have primarily focused on designing systems and techniques to support students self-regulated learning and have not provided solutions to self-regulation strategies, more importantly online peer-learning self-regulation strategy. The lack of online peer learning self-regulation strategy has been the hallmark of the challenges in blended learning instruction. This study proposed an approach for scaffolding students peer-learning self-regulation strategy in the online component of blended learning. The study involves forming online peer-learning groups based on learning potential and affinity structures; leveraging the design of learning system features to instigate group dynamic elements for facilitating and motivating small group peer-learning, as well as promoting pro-social behavior by offering incentives for combating the inherent problems of reluctance to students’ peer-learning participation. An experiment involving 120 university Students shows that effective online peer-learning self-regulation strategy scaffold involves preparing and inspiring students so as to prevent social loafing and refusal to participate; and secondly to facilitate and fully engage students in the actual online peer-learning discussion. The experimental results show significant improvement of students’ academic performance by using this approach. In addition, our study proposed recommendations for future research.