Abstract Machine learning and deep learning models are commonly developed using
programming languages such as Python, C++, or R and deployed as web apps delivered …

The number of devices, from smartphones to IoT hardware, interconnected via the Internet is
growing all the time. These devices produce a large amount of data that cannot be analyzed …

The amount of accessible computational devices over the Internet offers an enormous but
latent computational power. Nonetheless, the complexity of orchestrating and managing …

In order to calculate the node big data contained in complex networks and realize the
efficient calculation of complex networks, based on voluntary computing, taking ICE …

Use of Deep Learning (DL) in commercial applications such as image classification,
sentiment analysis and speech recognition is increasing. When training DL models with …

The detection of malicious domains often relies on machine learning (ML), and proposals for
browser-based detection of malicious domains with high throughput have been put forward …

We present" DistML. js", a library designed for training and inference of machine learning
models within web browsers. Not only does DistML. js facilitate model training on local …

The development and widespread of Internet browsers and technologies make them a tool
that can be used for many scientific problems. This raises the question of whether Internet …

This research study addresses the difficulty often encountered when deploying Machine
Learning (ML) models into real-world applications. More than just building and training the …

Hasta ahora, la mayoría de los modelos de computación distribuida se han enfocado en el
procesamiento de la información en la nube. Por otro ldao, los dispositivos de borde (más …