Explainable AI (XAI) methods are gaining prominence in medical imaging, addressing the critical need for transparency and trust in AI-driven diagnostic tools. Mammography, as the cornerstone of early breast cancer detection, holds immense potential for improving outcomes when integrated with AI solutions. However, widespread adoption of AI in clinical settings depends on explainability, which enhances clinicians' confidence in these tools. By exploring various XAI techniques and evaluating their strengths and weaknesses, researchers can significantly advance precision medicine. This review synthesizes existing research on XAI in medical imaging, focusing on mammography, a domain often overlooked in XAI studies. It provides a comparative analysis of XAI techniques employed in mammography, assessing their diagnostic efficacy and identifying research gaps, such as the lack of specialized evaluation frameworks. Additionally, the review examines evaluation methods for XAI in medical imaging and proposes modifications tailored to mammography diagnostics. Insights from XAI advancements in other fields are also explored for their potential to enhance interpretability and clinical relevance in breast cancer detection. The study concludes by highlighting critical research gaps and proposing directions for developing reliable, effective AI models that integrate XAI to transform breast cancer diagnostics.

In recent years, computer language area has witnessed important evolvement with applications in different domains. Machine Translation MT technology, considered as a subfield, has received important development with different approaches and techniques. Although, many MT systems and tools that support Arabic already exist; however, the quality of the translation is moderate and needs some improvement. In addition, the high demand for effective technologies to process and translate information from/to Arabic motivated the researchers in Arabic Machine Translation (AMT) to propose new approaches and solutions following the mainstream method, notably neural machine translation (NMT). In this paper, we provide a comprehensive review and compare different NMT approaches mainly for Arabic-English (and English-Arabic) machine translation research works. The discussed approaches address different linguistic and technical challenges and problems while demonstrating great success compared to traditional methods. The results of this work can serve the researchers and professional to be up-to-date and provide them with the necessary resources for modelling and improving of the AMT. These resources include corpora, toolkits, techniques and new models. The obtained results outline various findings, critics, and open issues in this area.

Mobile serious games are very important tools for self-learning, training, and edutainment. They can be used to improve the children’s learning skills to grasp new concepts rapidly in an enjoyable manner. Fun, motivation, and engagement are the most important features in designing attractive serious games. Therefore, designers should always find the best strategies and techniques to keep children playing the game with motivation and enthusiasm. The process is very long and requires different expertise from all the stakeholders. Arcade games are the most played games for fun and amusement in the last decades. Their approach is based on simplicity, easiness, repetitiveness, and soft challenges. The players of arcade games need only to improve their scores in every episode and avoid frustration and hard challenges as in other games. Hence, this arcade-based approach can be used in designing attractive serious games for young children and adolescents. We propose in this research a new serious game for diabetic children to teach them about the disease in a very easy manner. The game uses the arcade approach coupled with useful information about diabetes where the children discover them incrementally during the play. The longer time the child spends on the game, the most she/he can learn and enhance her/his knowledge on the disease. The game success came from its design and implementation with the presence of diabetes children, advisors, and nutritionists. We compare the game with the well-known DEX pet-based game for diabetes awareness on different aspects including user experience, usability, and learning outcomes. The study is conducted on twenty children from a diabetes camp who accepted voluntarily to participate in this research. We evaluate their educational aspects, using an improved version of the MEEGA+ model. We demonstrate the importance of the proposed game in enhancing the children knowledge on diabetes and adopt a new healthy lifestyle.

Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.