In the last few years, methods falling within the family of randomization-based machine
learning models have grasped a great interest in the Artificial Intelligence community, mainly …

Abstract Machine learning and deep learning have become the most useful and powerful
tools in the last years to mine information from large datasets. Despite the successful …

The rapid progress in sensor technology has empowered smart home systems to efficiently
monitor and control household appliances. AI-enabled smart home systems can forecast …

Abstract The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for co** with the complex and opaque nature of these architectures …

Wildfires can be devastating, causing significant damage to property, ecosystem disruption,
and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-time …

Echo state networks (ESNs) belong to the class of recurrent neural networks and have
demonstrated robust performance in time series prediction tasks. In this study, we …

In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS)
platforms, and rapid advances in cloud and edge computing, the demand for efficient and …

Abstract The 2022 National Defense Strategy of the United States listed climate change as a
serious threat to national security. Climate intervention methods, such as stratospheric …

As a traditional neural network architecture, Echo State Network (ESN) has achieved good
results in time series forecasting. However, in the past ten years, few people have conducted …

This editorial briefly analyses, describes, and provides a short summary of a set of selected
papers published in a special issue focused on deep learning methods and architectures …