Learning Management Systems (LMSs) are increasingly utilized for the administration, tracking, and reporting of educational activities. One such widely used LMS in higher education institutions around the world is Blackboard. This is due to its capabilities of aligning items of learning content, student-student and student-teacher interactions, and assessment tasks to specified goals and student learning outcomes. This study aimed to determine how certain Key Performance Indicators (KPIs) based on student interactions with Blackboard helped to forecast the learning outcomes of students. A mixed-methods study design was used which included analysis of four deep learning models for predicting student performance. Data were collected from reports on seven general preparation courses. They were analyzed using a documentary analysis approach to establish possible predictive KPIs associated with the electronic Blackboard report. Correlational analyses were performed to examine the extent to which these factors are linearly correlated with the performance indicators of students. Results indicated that a predictive model which combined convolutional neural networks and long short-term memory (CNN-LSTM) was the optimal method among the four models tested. The main conclusion drawn from this finding is that the combined CNN-LSTM approach may lead to interventions that optimize and expand use of the Blackboard LMS in universities.

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient.

In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively.

Waste Management System (WMS) is applied in smart cities and is supported by the Internet of Things (IoT). WMS involves several responsibilities, such as collection, disposal, and utilization of waste in relevant facilities. Efficient waste management has a considerable impact on the quality of life of citizens in smart cities. The interaction between wireless sensor networks and mobile ad-hoc networks (MANETs) with IoT provides excellent mobility for users, and reduces the deployment costs of networks. Data transfer via the intermediate connected devices must be maintained to shorten the distance between the devices. Intelligent routing algorithm is proposed to find efficient paths that comply with the WMS requirement constraints and avoid invalid paths that cause increased computation time. Chromosome intersection operation on genetic-based routing algorithm, intersection activation function, and intersection node table are proposed to avoid the similarity and redundancy of generated paths and keep the validity of the paths. This study improves the path finding, obtains good results, and increases the rate of efficient paths. Results show that the selected path is efficient in terms of distance, number of hops and number of iterations by using the proposed method. In addition, this proposed method outperforms DSR, which offers alternative paths with more efficiency.

Analyzing unusual events is significantly important for video surveillance to ensure people safety. These events are characterized by irregular patterns that do not conform to the expected behavior in the surveillance scenes. We present a novel irregularity-aware semi-supervised deep learning model (IA-SSLM) for detection of unusual events. While most existing works depend on the availability of large amount of labeled data for training, our proposed method utilizes a semi-supervised deep model to automatically learn feature representations from limited number of labeled data samples. Our method extracts meaningful information from both labeled and unlabeled data during the training stage to improve the performance. For this purpose, we explore the concept of consistency regularization and entropy minimization to output confident predictions on unlabeled data. For experimental analysis, we consider various standard and diverse datasets. The results show that our IA-SSLM method outperforms several reference methods using different performance metrics.

Scalability is one of the system’s characteristics highlighted in the recent literature, and it is directly related to issues that are encountered in state-of-the-practice technology. The scalability of a system is challenging because monolithic legacy systems are hard to scale due to the high level of component dependencies. To the best of our knowledge, there is no published work available that can identify the components from a monolithic legacy system in the context of dependent and independent components and scale them accordingly. The main contribution of this paper is the proposal of a novel approach for the exclusive identification of dependent and independent monolithic legacy system components. The proposed approach also helps to remove the dependency among components of monolithic legacy systems. As a result, it establishes a precise method that identifies all the components of an application and removes the dependency among components, helping to increase the scalability of the resulting application. This approach was validated by several experiments, and the key findings were the identification of dependent and independent components, the identification of relationships among components, and the identification of the abstract level architecture of the monolithic legacy system. In future work, the proposed method will be enhanced toward the recovery of the whole system’s architecture.