Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Islamic Guidance and Counseling Journal The Indonesian Journal of Computer Science
Saleem, Mohammad
Unknown Affiliation
Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Muslim Student’s Dispositional Mindfulness and Mental Wellbeing: The Mediating Role of Core Self-Evaluation Saleem, Mohammad; Rizvi, Touseef; Bashir, Irfan
Islamic Guidance and Counseling Journal Vol 5 No 1 (2022): Islamic Guidance and Counseling Journal
Publisher : Institut Agama Islam Ma'arif NU (IAIMNU) Metro Lampung in collaboration with Asosiasi Bimbingan dan Konseling Indonesia (ABKIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/igcj.v5i1.2175

Abstract

This study analyses the mediating role of Core Self-evaluation (CSE) on the relationship between dispositional mindfulness and mental wellbeing. A sample of 184 Muslim students (Mage = 22.08) studying in the different universities completed the self-report measures of Mindful Attention Awareness Scale (MAAS), the Core Self-evaluations Scale (CSES), and the Warwick–Edinburgh Mental Well-being Scale (SWEMWBS). The collected responses are subjected to multiple regression and mediation analyses. The results revealed that dispositional mindfulness and core self-evaluations significantly predict mental wellbeing. It is found that core self-evaluation fully mediates the effect of dispositional mindfulness on mental wellbeing. Moreover, it is also observed that measures of dispositional mindfulness, core self-evaluation, and mental well-being are indifferent with respect to students’ gender. Therefore, the study highlights the importance of core self-evaluation and explains a possible process by which depositional mindfulness enhances Muslim students' mental well-being.
Deep Learning in Medical Image Analysis Article Review Ibrahim, Media Ali; askar, shavan; saleem, Mohammad; Ali, Daban; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3842

Abstract

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation. Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.
Co-Authors Abdullah, Nihad Ali, Daban askar, shavan Bashir, Irfan Ibrahim, Media Ali Rizvi, Touseef