There are many deep learning frameworks available, such as TensorFlow, PyTorch, Keras, MXNet, and more. Each framework has its own advantages and disadvantages, such as ease of use, performance ...

With healthcare systems under pressure to deliver faster, more accurate care, innovative technologies like blockchain and AI ...

A deep learning-based Scan-vs-BIM framework automates structural integrity evaluations, significantly reducing processing ...

Abstract: This paper presents an in-depth analysis of multi-modal, deep learning-based frameworks for fault detection within big research infrastructures, with a specific focus on synchrotron ...

In this article, we propose a novel deep learning framework that incorporates variable time delay (VTD) estimation and uncertainty quantification into quality prediction. The framework employs a ...

The input of the DeepTXsolver is the parameters of the mechanism model, and the output is the corresponding stationary distribution solution of the model. The input to the DeepTXinferrer is the ...

The reliability and functionality of enterprise software are more critical than ever in today’s digital economy.

A new study led by Griffith University has unveiled a machine learning-based framework to accurately estimate groundwater ...

Artificial Intelligence (AI) chatbots have transformed how businesses interact with customers, offering real-time assistance, ...

The growing adoption of machine learning across industries highlights the critical need to secure ML systems, as ...

Keras to build different models with Python, TensorFlow, Theano and other Deep Learning frameworks under the hood + Kafka Streams as generic Machine Learning infrastructure to deploy, execute and ...