Making the most from virtual learning environments captivates researchers, enhancing the learning experience and reducing the withdrawal rate. In that regard, this article presents a framework for a withdrawal prediction model for the data of the Open University, one of the largest distance-learning institutions. The main contributions of this work cover two main aspects: relational-to-tabular data transformation and data mining for withdrawal prediction. This main steps of the process are: (1) tackling the unbalanced data issue using the SMOTE algorithm; (2) voting over seven different features' selection algorithms; and (3) learning different classifiers for withdrawal prediction. The experimental study demonstrates that the decision trees exhibit better performance in terms of the F-measure value compared to the other tested models. Furthermore, the data balancing and feature selection processes show a crucial role for guiding the predictive model towards a reliable module.

Nowadays, waste management faces the challenge of providing effective and efficient solutions for waste collection, disposal, and recycling while respecting health and environmental standards. This challenge also includes the lack of understanding of the diverse factors that influence the various stages of waste management, inefficient route planning, insufficient resources, etc. Faster collection, management, and processing of waste are possible with smart containers and IoT technologies allowing waste real-time data provision. Thus, this research proposes a waste management system based on generic and comprehensive generic context ontology and smart containers. The context ontology is conceived to solve the limits and the insufficiencies of waste management by covering all the waste facets for all the stakeholders, optimizing, analyzing, and reusing the waste data and conditions. For given smart container and waste management context, we need to have a global view of the relevant contextual data according to a unified model such as the waste environment data, the waste activity context, the waste computing context, the user context, the collaboration context, etc. One significant advantage of our system is that it provides a unified model for waste management contextual data that could be reused for other waste management systems covering all the properties of this domain. The proposed solution implements an intelligent and adaptive IoT system for waste management according to different waste contexts, waste objectives, and waste activities. The proposed system has been successfully tested under different scenarios in Jeddah City Municipality.

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Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates of occurrence. Therefore, the early prediction of learner withdrawal has become a major challenge, as well as identifying the factors, which contribute to this increasingly occurring phenomenon. In that regard, this manuscript presents a framework for withdrawal prediction model for the data from The Open University, one of the largest distance learning institutions. For that purpose, we start by pre-processing the dataset and tackling the challenge of discretization process and unbalanced data. Secondly, this paper identifies the semantical issues of raw data by introducing new behavioural indicators. Finally, we reckon on machine learning algorithms for withdrawal prediction model to understand the lack of learners' commitment at an early stage.