Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...
A collaboration between the University of Konstanz and Forschungszentrum Jülich has achieved the first fully tunable ...
An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...
Abstract: Deep neural networks (DNNs)-based SAR target recognition models are susceptible to adversarial examples, which significantly reduce model robustness. Current methods for generating ...
MB_DEVICE_ADDR1 CID_INP_DATA_0, Data_channel_0 Data channel 1 MB_DEVICE_ADDR1 CID_HOLD_DATA_0, Humidity_1 Humidity 1 MB_DEVICE_ADDR1 CID_INP_DATA_1 Temperature_1 ...
Abstract: Accurate and reliable wind power forecasting models are essential for stable power system operations and large-scale grid integration. Nevertheless, predicting wind power remains challenging ...
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