Wheat rust diseases, notably yellow rust, pose a substantial threat to global food security by adversely impacting crop yields. The timely and precise detection of these diseases is imperative for implementing effecti...
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Financial fraud has caused lots of losses for both consumers and banks. One of the modern methods to detect financial fraud is graph neural network, which checks relationships in financial transactions, but graph conv...
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Label distribution learning is a powerful learning paradigm to deal with label polysemy and has been widely applied in many practical tasks. A significant obstacle to the effective utilization of label distribution is...
Compilers play a crucial role in translating high-level programming languages into asssembly or machine code. Many compilers either lack optimization capabilities, or too large and complex. This paper presents design ...
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Feature selection is a highly effective technique for processing high-dimensional datasets. However, existing methods often prioritize the accuracy of constructed models as the main criterion, leading incomplete and u...
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In Spectrum-sliced Elastic Optical Path (SLICE) networks, the Routing and Spectrum Assignment (RSA) problem refers to routing a lightpath from the sender to the receiver for each traffic demand while occupying the sam...
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The orchestration of a service function chain (SFC) is responsible for the coordination of multiple virtual network functions (VNFs) in a sequence and the deployment of VNF nodes and links onto physical resources, for...
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In this article, we propose a method for video stabilization that utilizes grid-based motion extraction and temporal moving Gaussian filtering for smoothing motion. The method determines the parameters for multiple ho...
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The development of deep learning provides a basis for detection techniques in SAR images. Nonetheless, challenges still remain when dealing with small and arbitrarily oriented ship objects. In this study, based on YOL...
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Achieving high-quality image generation remains a challenging problem, particularly when balancing computational efficiency, representational richness, and energy consumption. Traditional deep learning models often la...
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