16 citations · 57 across the 15 of their papers we have counts for
30 papers
Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey
Gita Sarafraz, Armin Behnamnia, Mehran Hosseinzadeh +3
Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite th…
RC-RNN: Reconfigurable Cache Architecture for Storage Systems Using Recurrent Neural Networks
Shahriar Ebrahimi, Reza Salkhordeh, Seyed Ali Osia +3
Solid-State Drives (SSDs) have significant performance advantages over traditional Hard Disk Drives (HDDs) such as lower latency and higher throughput. Significantly higher price p…
Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks
Mohammadreza Doostmohammadian, Houman Zarrabi, Hamid R. Rabiee +2
This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measure…
Analysis of Contractions in System Graphs: Application to State Estimation
Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah +2
Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state vari…
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian
Nasrin Taghizadeh, Ehsan Doostmohammadi, Elham Seifossadat +2
We have released Sina-BERT, a language model pre-trained on BERT (Devlin et al., 2018) to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT…
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI
Reza Shirkavand, Sana Ayromlou, Soroush Farghadani +7
Early detection of dementia through specific biomarkers in MR images plays a critical role in developing support strategies proactively. Fazekas scale facilitates an accurate quant…