To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

We propose $\textit {Mish} $, a novel self-regularized non-monotonic activation function
which can be mathematically defined as: $ f (x)= x\tanh (softplus (x)) $. As activation …

Deep learning is a field in artificial intelligence that works well in computer vision, natural
language processing and audio recognition. Deep neural network architectures has number …

Understanding how neural networks learn features, or relevant patterns in data, for
prediction is necessary for their reliable use in technological and scientific applications. In …

The success of convolutional neural networks (CNNs) in various applications is
accompanied by a significant increase in computation and parameter storage costs. Recent …

This study introduces a novel architecture for electricity price forecasting, comprising four
modules designed for prediction and information fusion. Three modules are dedicated to …

Pattern recognition and classification of images are key challenges throughout the life
sciences. We combined two approaches for large-scale classification of fluorescence …

Pre-trained language models learn informative word representations on a large-scale text
corpus through self-supervised learning, which has achieved promising performance in …

Abstract Machine learning algorithms are being increasingly used to process large volumes
of wildlife imagery data from unmanned aerial vehicles (UAVs); however, suitable algorithms …