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H. Owida

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Review Open access Oct 2026

Virtual laboratories and online learning in engineering education: a review

The rapid expansion of virtual laboratories and online learning in engineering education, accelerated by the COVID-19 pandemic, has created an urgent need for clearer pedagogical guidance on how digital laboratory modalities should be used. This review addresses the unresolved educational problem of ensuring that virtu...

H. Owida, Areen M. Arabiat · 0 citations
#federated learning Review Open access Oct 2026

Internet of things-enabled biomedical engineering: current applications, challenges, and emerging directions

The internet of medical things (IoMT) is a structured integrative review of the internet of things (IoT) that focuses on the medical field, specifically on the integration of physiological sensors, medical devices, communication networks, and the clinical information systems to monitor and track patients' health and co...

H. Owida, Areen M. Arabiat · 0 citations
Open access 2026

Deep ANN feature fusion with multi-stage data processing and visualization for enhanced breast cancer detection in ultrasound images

This paper proposes a new dynamic feature fusion technique called adaptive gated cross attention fusion (AGCAF), which utilizes a learnable gating function and two-way cross attention to dynamically weight each contribution of features from the DeIT and ViT backbones.

S. Abuowaida, H. Owida, H. Mashagba et al. · 0 citations
Open access Jul 2026

TRT-GLA: Tri-Representation Transformers with Global-Local Attention for High-Fidelity Multi-Modal MRI Super-Resolution

A Tri-Representation hybrid framework for MRI SR, TRT-GLA is proposed that redefines the MRI SR task as a joint spatial–spectral–structural high-resolution image generation problem, and establishes the utility of TRT-GLA in supporting quantitative analysis through a downstream tumor segmentation experiment that is clin...

Suhaila Abuowaida, H. Owida, T. Hamadneh et al. · 0 citations
Open access Aug 2026

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

The RF model’s ability to effectively estimate cancer risk levels among individuals, allowing for earlier discovery and more effective medical care is demonstrated, resulting in reliable generalization and accuracy estimates.

Areen M. Arabiat, H. Owida, Suhaila Abuowaida et al. · 0 citations

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