Xiaobing Wei, Filip Biljecki, Pengyuan Liu, Binyu Lei, N. Weghe, Haosheng Huang
Ahmed Jawad Kadhim, Tayseer S. Atia
Empirical comparisons between cloud-based and on-premise database deployments under realistic e-commerce workloads remain limited. This study presents a controlled experimental evaluation of Microsoft SQL Server 2022 (on-premise) versus Azure SQL Database, using an identical .NET…
Mohammed Aqeel Ismail, Okuthe P. Kogeda
The increasing complexity and volume of mobile network traffic present significant challenges to maintain consistent Quality of Service (QoS) across diverse applications. Accurate traffic classification enables application-aware resource allocation by distinguishing applications…
The increasing complexity of modern network infrastructures has intensified the need for reliable and efficient intrusion detection systems. While advanced deep learning approaches have demonstrated strong performance, their high computational cost and limited interpretability re…
Hanaa Alzahrani, Maram Almotairi, Arwa Basbrain
Clothing classification by occasion is an important area in computer vision and artificial intelligence (AI). This task is particularly challenging because of the subtle visual similarities among clothing categories such as formal, party, and casual attire. Variations in color, f…
Beatriz Duro, Anabela Gomes, Fernanda Brito Correia, Ana Rosa Borges, Jorge Bernardino
Student dropout in Higher Education remains a persistent challenge with significant academic, social and economic consequences. Predictive analytics using traditional Machine Learning and Deep Learning have been increasingly explored to support early identification of students at…
Said Al Jaadi, Laila Al Wahaibi, Mohammed Al-Hinai, Haneen Hafiz Gaffar, Abdullah M. Al Alawi
Inpatient deterioration, marked by ICU transfer or mortality, remains a critical challenge in hospital settings. While traditional early warning systems (EWS) have limitations, machine learning (ML) offers a promising approach for the early identification of at-risk patients. Thi…