In this research, a set of data mining techniques are applied to target to balance between the industry requirement and user satisfaction. The proposed approach aims at exploring the most significant attributes for the industry services’ evaluation. The exploration goal ensures a double-sided benefit for both the industry and the user. From one perspective, it raises the evaluation accuracy level for the main service’s attributes which is most important to the user and consequently leads to higher user satisfaction. On the other side, it minimizes the user’s collaboration effort in the evaluation process which raises the user’s collaboration willingness. The proposed approach has been applied to the IoT services industry in Saudi Arabia. The results proved that eliminating insignificant attributes has provided minimal user effort with retaining the required evaluation accuracy and the success percentage reached 90%.

Scrum has emerged as the most widely used and desired Agile approach for providing corporate strategic competency by establishing a solid foundation for project management. However, there are several issues confronted during its implementation. Some researchers tried to solve specific areas of Scrum issues except only research that covers several aspects without resolving all of them. So, this study presents the EFSP model for improving maintainability, security and reusability. Methodologically, in this study, we carry out the following tasks: (i) apply Mark or 7C model on requirements, and (ii) identify Scrum aspects (artifacts and/or activities) that should be expanded as follows: adding the concept of systematic reusability into sprint planning, classifying the requirements into four layers according to clean architecture into sprint backlog, and evolutionary model into sprint. This model offers solutions to these problems while maintaining the simplicity and flexibility of Scrum. The system evaluation results have achieved an improvement in maintainability by reducing technical debt from 1.6% to 0.9%, security from 10 to 3, timeliness from 5 to 2, and improving team productivity from 1.24 to 2.78. The EFSP model may be utilized to develop a standard in other projects.

Engaging students’ personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student’s progress, moreover, it could predict the student’s level which consequently leads to identifying the required student material to raise his current education level. Although the topic has been vital before the COVID-19 pandemic, however, the importance of the topic has increased exponentially ever since. The research supports the decision-makers in educational institutions as considering personalized data for the student’s educational tasks and activities proved the positive impact of raising the student level. The paper proposes a framework that considers the students’ personal data in predicting their learning skills as well as their educational level. The research included engaging five well-known clustering algorithms, one of the most successful classification algorithms, and a set of 10 features selection techniques. The research applied two main experiment phases, the first phase focused on predicting the students’ learning skills, and the second focused on predicting the students’ level. Two datasets are involved in the experiments and their sources are mentioned. The research revealed the success of the clustering and prediction tasks by applying the selected techniques to the datasets. The research concluded that the highest clustering algorithm accuracy is enhanced k-means (EKM) and the highest contributing features selection method is the evolutionary computation method.