This study aimed to automate the extraction of local corneal topography (CT) features in myopic children undergoing orthokeratology (OK), evaluate their causal effects on axial length (AL) control, and develop a predictive model for AL progression.We...
Deep learning for continuous physiological signals, such as electrocardiography or oximetry, has achieved remarkable success in supervised learning scenarios where training and testing data are drawn from the same distribution. However, when evaluati...
Prostate cancer exhibits a strong familial association, and its heritability indicates a significant contribution from germline variants. While genome-wide association studies (GWAS) have identified common germline variants associated with prostate c...
This paper proposes a hybrid ensemble classifier with denoising autoencoder (ECDAE) framework to address reliability and robustness challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRNs). The proposed framework first emp...
Traditional asset allocation rules, while effective in stable phases, tend to erode once markets enter volatile regimes or undergo structural breaks. Research in deep reinforcement learning (DRL) has usually emphasized raw-return rewards, leaving asi...
In the field of 3D skeleton action recognition, research on self-supervised learning methods has primarily focused on spatio-temporal feature modeling. However, these methods rely heavily on modeling single motion features, which limits their ability...
BACKGROUND: Acute Ischemic Stroke (AIS) remains a critical global health challenge that requires continuous improvement in diagnostic strategies. Timely and accurate diagnosis is essential for effective reperfusion therapies such as intravenous throm...
This study proposes a transfer learning framework for non-invasive glucose prediction using diffuse-reflectance near-infrared (NIR) spectroscopy, along with an in vitro phantom model that incorporates a pump-driven circulation system. Lipofundin and ...
In recent times, in modern smart city environments, securing and maintaining facial biometric security is crucial for preventing unauthorized access to citizen data and safeguarding it from spoofing. This research proposes a multimodal deep learning ...
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