Elfadil Abdalla Mohamed has received his MSc and Ph.D. in Computer Science from University of Technology Malaysia (UTM), Malaysia, in 2002. He is currently working as an Assistant Professor at College of Information Technology, Ajman University, United Arab Emirates. His main areas of research interest are in data mining and database.
The recent COVID-19 pandemic has forced educational institutions worldwide to adopt e-learning. UAE higher education institutions have implemented e-learning systems and programs to cope with this unprecedented situation. This paper measured the strength of association between key aspects of e-learning systems and programs and students’ motivation to learn in Ajman University (AU). Cronbach’s coefficient alpha was used to test the internal consistency reliability of key aspects of e-learning (EL-8) and students’ motivation to learn (SML-16). Exploratory factor analysis was used to test the validity of, and coherence of patterns in, the data. Parametric and non-parametric methods were used to investigate the strength of association between key aspects of e-learning and students’ motivation to learn in AU. The results indicated that motivation variables were more strongly correlated with both e-teaching materials and e-assessments key aspects relative to others such as e-discussion, and e-grade checking and feedback
Protein complexes are groups of two or more polypeptide chains that bind to form noncovalent networks of protein interactions. Over the past decade, researchers have created a number of means of computing the ways in which protein complexes and their members can be identied through these interaction networks. Although most of the existing methods identify protein functional complexes from the protein-protein interaction networks (PPIs) at a fairly decent level, the applicability of advanced graph network methods has not yet been adequately investigated. This paper proposes various graph convolutional network (GCN) methods to improve the detection of protein complexes. We rst formulate the protein complex detection problem as a node classication problem. Then, we developed a Neural Overlapping Community Detection (NOCD) model to cluster the nodes (proteins) using a complex afliation matrix. A representation learning approach, that combines a multi-class GCN feature extractor (to obtain the nodes' features) and a mean shift clustering algorithm (to perform the clustering), is also utilized. We convert the dense-dense matrix operations into dense-sparse or sparse-sparse matrix operations to improve the efciency of the multi-class GCN network by reducing space and time complexities. The proposed solution signicantly improves the scalability of the existing GCN. Finally, we apply clustering aggregation to nd the best protein complexes. A grid search is then performed on various detected complexes obtained via three well-known protein detection methods, namely ClusterONE, CMC, and PEWCC, with the help of the Meta-Clustering Algorithm (MCLA) and the Hybrid Bipartite Graph Formulation (HBGF). We test the proposed GCN-based methods on various publicly available datasets and nd that they perform signicantly better than previous state-of-the-art methods. The code/data are available for free download from https://github.com/Analystharsh/GCN_complex_detection.
Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either disease-related cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer's disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC D 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC D 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATN-criteria ( 6.27 ± 1.82 vs 5.32 ± 1.9; p < 0:05). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer's disease from non-Alzheimer's dementia. To find and justify a reliable threshold levels, further research is required.