This code example demonstrates how to run through the ModusToolbox™ machine learning (ModusToolbox™-ML) development flow with PSoC™ 6 MCU, where the end user has a pre-trained neural network (NN) ...

Machine learning models were trained with all variables as inputs to classify patients likely to have favorable outcomes. For the deep neural network model, 3 hidden layers with 15 artificial neural ...

Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd., 1 CREATE ...

Integrating these tools with machine learning (ML) can significantly enhance their application potential. This discussion commences by addressing the pressing issues in thermal modelling of SiC ...

Abstract: Fast and reliable solvers for optimal power flow (OPF) problems are attracting surging research interest. As surrogates of physical-model-based OPF solvers, neural network (NN) solvers can ...

"The big picture is that we use a machine learning model to learn what 'normal' looks like for each cavity," Ferguson said. "Then, by continuously comparing new data to that baseline, the system ...

Taiwan-based optical imaging solutions provider Ability Enterprise is accelerating its diversification strategy by entering the machine vision ... two new imaging modules, an infrared (IR ...

Designed to support the entire machine learning lifecycle -- from data ingestion and model training to deployment and monitoring -- Azure ML is empowering developers to integrate predictive ...

The deep-learning algorithm used in this study is shown as Figure 2. The model comprises a gated module, a LSTM module, and a prediction module. Firstly, a gated layer is employed to extract the gated ...