In our model, the flow of ... re developing a deep learning model that should be able to predict the plasma’s behavior in a split second, but this is still a work in progress. In the past couple of ...

Currently, the standard method for coordinating distributed generation under network constraints is to solve the optimal power flow (OPF ... the proposed algorithm outperforms the latest deep ...

Deep learning (DL ... Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the ...

It is believed that implicit bias is a key factor in the ability of deep-learning algorithms to generalize. Decades of research in learning theory suggest that in order to avoid overfitting one should ...

Deep learning model architecture. FLAIR indicates fluid-attenuated inversion recovery; T1w, T1-weighted; and T2w, T2-weighted. The single-sequence scheme used single sequence (T1w, T2w, or FLAIR) to ...

Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model ... Figure 1 ...

The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow–volume curves in identifying the ventilatory patterns. Further, the performance of the ...

The idea is to allow any company to deploy a deep-learning model ... data flow ends up being a bottleneck for computation, capping the speed at which GPUs can run deep-learning algorithms.