A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN integration. It improves dynamic lesion detection, temporal ...
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"This is one of the most disturbing things we're seeing," an FBI official said. FBI officials say they are growing increasingly concerned about a loose network of violent predators who befriend ...
Discover the key types of network traffic and their role in optimizing performance plus real-world examples to see how they impact data flow. Identifying network traffic types is vital because it ...
The use of network meta-analysis (NMA) in sport and exercise medicine (SEM) research continues to rise as it enables the comparison of multiple interventions that may not have been assessed in a ...
Background: Ethno-racial disparities in cardiometabolic diseases are driven by socioeconomic, behavioral, and environmental factors. Bayesian networks offer an approach to analyze the complex ...
In-context learning (ICL) enables LLMs to adapt to new tasks by including a few examples directly in the input without updating their parameters. However, selecting appropriate in-context examples ...
Relay protection rejection and misoperation exist in the existing distribution network, which will affect the fault diagnosis results. To diagnose faults in distribution networks, this paper presents ...
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