Methods A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and ...

In a study published in PNAS Nexus, researchers from Lawrence Livermore National Laboratory (LLNL) described a new deep learning model, called ST-GasNet, capable of predicting toxic plume behavior in ...

Accurately predicting the occurrence, amplitude, and propagation speed of traffic flow shockwaves are essential for dynamic traffic control to mitigate traffic congestion. However, traditional ...

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CONTAIN™ represents the next breakthrough in AI-powered ore sorting from TOMRA Mining – a deep learning solution purpose-built to classify complex inclusion-type ores with unprecedented ...

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Furthermore, a traffic flow shockwave prediction model, based on wavelet transform and predicted trajectories, is developed to predict shockwave occurrence, timing, propagation speed, and amplitude.

An AI model trained on analyzing ultrasound images and molecular testing results may assist health care professionals treating indeterminate thyroid nodules, researchers stated in a letter to the ...

In addition, a deep learning model was devised using ResNet101 and transfer learning. The clinical, radiomics, and deep learning models were integrated to establish a comprehensive multifactorial ...

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