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Abstract Physical sciences are essential to the predictive modeling of complex environmental systems, particularly environmental monitoring and sustainability applications, as they provide the deterministic foundation re...

Abstract Purpose This study introduces a new Convolutional Neural Network (CNN) architecture, EyeDiagNet, for the classification of fundus images into four classes: normal, cataract, diabetic retinopathy (DR), and glauco...

Abstract Precise extraction of medical phenotypes and entities from electronic health record (EHR) text is vital for numerous clinical research tasks, including cohort classification, tracking temporal patterns in diseas...

Abstract Early small fires in coal mines are of critical importance for mine safety, as most incidents begin as small fire sources. These fires essentially constitute a task of small-object detection. Detecting such targ...

Abstract Cloud computing is quite popular because of its scalability and flexibility merits. However, faces significant security risks from malware, which exploits infrastructure weaknesses to cause unauthorized access, ...

Abstract Recently, numerous lightweight image super-resolution methods with low computational complexity have been proposed, achieving a promising trade-off between reconstruction quality and efficiency. However, most ex...

Abstract While multi-access edge computing (MEC) enables flexible task offloading, the inherent dynamism and complexity of real-world environments, accompanied by various uncertainties, exacerbate the challenges in offlo...

Abstract Accurate differentiation of pulmonary emphysema from chest X-rays remains challenging due to imbalanced data, image noise, and the limitations of traditional deep networks in managing diagnostic uncertainty. In ...

Abstract The rapid development of China’s financial markets, particularly the expansion of the ETF ecosystem, has made dynamic sector rotation strategies increasingly feasible and cost-effective for investors. At the s...

Abstract Feature selection is a critical step in machine learning, as it helps minimize redundant features, thereby enhancing model performance and interpretability. In practice, partially labeled datasets are common. Wh...