In recent years, Instagram has rapidly become one of the world's largest platforms for shedding pictures and videos and running businesses online. However, despite its widespread worldwide popularity, the platform...
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In quantitative trading, investors seek to maximize profits while minimizing risk. To forecast future prices and assess risks, it is crucial to employ price prediction models such as RNN, GRU, and even combinations of...
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Rice variety identification is critical for addressing global food challenges, given rice's status as a staple food source for over half of the world's population. Leveraging recent advances in machine learnin...
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In many applications, including measuring physical activity, understanding sign language, and controlling full-body gestures, human position estimation from video is essential. This has the potential to be utilized fo...
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Precise and prompt product identification is vital for efficient inventory management in the retail grocery industry. While deep learning models have significantly improved in classification accuracy, dynamically chan...
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Despite a range of advantages that social networks provide, cyberbullying is widespread. Several tools have been developed to automatically detect text-based cyberbullying, but with increased use of a combination of i...
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With the exponential rise of Internet of Things (IoT) devices in our modern-day lifestyle, the potential danger of botnet intrusions has become inevitable. These botnet attacks cause extensive damage to both individua...
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Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain st...
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Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. Method To resolve this issue, we propose a novel unrolling rain-guided detail recovery network(URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. Result Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks;thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost *** on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.
The SDN-based network architecture currently lacks a framework for the efficient development and deployment of machine learning (ML) functions within the data plane. This paper addresses this gap by proposing a unifie...
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Plant disease is one prevalent factor that has a significant impact worldwide on food security and production, with plants contributing over 80% of food that is consumed by human, due to the importance of plants for w...
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