Abstract: Fault diagnosis holds significant practical importance for high performance and reliable control of induction motors. However, existing deep learning-based fault diagnosis methods demand a ...
Purpose This scoping review aimed to map and synthesize evidence on technological advancements using Artificial Intelligence in the diagnosis and management of dysphagia. We followed the PRISMA ...
Combining microscopy and machine-learning techniques leads to faster, more precise analyses of critical coating materials ...
ABSTRACT: In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to ...
ABSTRACT: In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to ...
This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
Abstract: Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of ...
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