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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Journal of Coaching and Sports Science
Amr Mohmed Soliman
King Khalid University
Education Electrical & Electronics Engineering Social Sciences

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Mindfulness and its relationship with psychological well-being among gifted student-athletes Khalid Hammouri; Mahmoud R Azam; Amr Mohmed Soliman
Journal of Coaching and Sports Science Vol. 5 No. 1 (2026): Journal of Coaching and Sports Science
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jcss.v5i1.963

Abstract

Background: This study examined the level of mindfulness and its relationship with psychological well-being among gifted student-athletes in Jordan, in light of the growing interest in the psychological role of mindfulness in supporting academic and athletic performance, as well as emotional balance, among this population. Aims: The study aimed to identify levels of mindfulness and psychological well-being among gifted student-athletes, to explore the nature of the relationship between these two variables, and to examine whether statistically significant differences in psychological well-being exist by gender. Methods: The study sample consisted of 145 male and female gifted student-athletes, selected using simple random sampling. Ryff’s Psychological Well-Being Scale – Short Form was employed. Data were analyzed using appropriate statistical methods, including means and standard deviations, tests of difference, and correlation coefficients. Results: The findings indicated moderate levels of mindfulness (M = 3.22, SD = 0.35) and psychological well-being (M = 3.17, SD = 0.54). No statistically significant gender differences in psychological well-being were found (p > 0.05). In contrast, a strong, positive, and statistically significant correlation was identified between mindfulness and psychological well-being. Conclusion: The results highlight the practical value of integrating mindfulness-based training programs within educational and athletic environments for gifted students, as these programs enhance emotional balance, psychological resilience, and athletic performance. The study also provides a theoretical contribution by clarifying the relationship between mindfulness and psychological well-being within the Arab context, thereby supporting the development of evidence-based educational and athletic practices.
Deep learning-based automated brain tumor detection using a proposed convolutional neural network architecture Ghaida Alsharef; Raghad Asiri; Lama Alsultan; Leena Alshehri; Akram M. Zeki; Ahmad AL Moustafa; Amr Mohmed Soliman; Safaa Matter; Mahmoud Khattab
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11511

Abstract

Brain tumors pose a critical threat to human health, often resulting in significant neurological impairments and high mortality rates. Early and accurate detection is essential for improving patient outcomes; however, conventional diagnostic methods, such as magnetic resonance imaging (MRI) interpretation by radiologists, are prone to subjectivity and inconsistency. This study proposes a deep learning (DL)-based solution to automate brain tumor detection using a customized convolutional neural network (CNN) architecture tailored for multi-class classification of glioma, meningioma, pituitary tumors, and non-tumor cases. The model incorporates residual blocks, activation functions, and batch normalization to extract and generalize complex spatial features from MRI images. A dataset of 6,000 labeled MRI images was preprocessed and augmented to enhance robustness. The model was evaluated using standard metrics: accuracy, precision, recall, and F1-score. Comparative analysis against ResNet50, InceptionV3, and MobileNetV2 demonstrated superior performance of the proposed model, achieving a classification accuracy of 99.14% and F1-scores of up to 1.00 across some tumor classes. These results validate the model's potential as a reliable and scalable diagnostic aid, offering improved accuracy and reduced diagnostic time. This work sets the foundation for intelligent, accessible, and clinically deployable brain tumor detection systems.
Co-Authors Ahmad AL Moustafa Akram M. Zeki Ghaida Alsharef Khalid Hammouri Lama Alsultan Leena Alshehri Mahmoud Khattab Mahmoud R Azam Raghad Asiri Safaa Matter