In recent decades, predicting the performance of students in the academic field has revealed the attention by researchers for enhancing the weaknesses and provides support for future students. In order to facilitate the task, educational data mining (EDM) techniques are utilized for constructing prediction models built from student academic historical records. These models present the embedded knowledge that is more readable and interpretable by humans. Hence, in this paper, the contributions are presented in three folds that include the following: (i) providing a thorough analysis about the selected features and their effects on the performance value using statistical analysis techniques, (ii) building and studying the performance of several classifiers from different families of machine learning (ML) techniques, (iii) proposing an ensemble meta-based tree model (EMT) classifier technique for predicting the student performance. The experimental results show that the EMT as the ensemble technique gained a high accuracy performance reaching 98.5% (or 0.985). In addition, the proposed EMT technique obtains a high performance, which is a superior result compared to the other techniques.

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated.

With the increasing in mobile application systems and a high competition between companies, that led to\ud increase in the number of mobile application projects.\ud Mobile software development is a group of process for creating software for mobile devices with limited\ud resources like small screen, low-power. The development of mobile applications is a big challenging\ud because of rapidly changing business requirements and technical constraints for mobile systems. So,\ud developers faced the challenge of a dynamic environment and the Changing of mobile application\ud requirements. Moreover, Mobile applications should adapt appropriate software development methods that\ud act in response efficiently to these challenges.\ud However, at the moment, there is limited knowledge about the suitability of different software practices for\ud the development of mobile applications. According to many researchers ,Agile methodologies was found to\ud be most suitable for mobile development projects as they are short time, require flexibility, reduces waste\ud and time to market.\ud Finally, in this research we are looking for a suitable process model that conforms to the requirement of\ud mobile application, we are going to investigate agile development methods to find a way, making the\ud development of mobile application easy and compatible with mobile device features. \u