The rapid development in data science and the increasing availability of building
operational data have provided great opportunities for develo** data-driven solutions for …

The wide availability of massive building operational data has motivated the development of
advanced data-driven methods for building energy predictions. Existing data-driven …

Forecasting models that are trained across sets of many time series, known as Global
Forecasting Models (GFM), have shown recently promising results in forecasting …

Deep neural networks such as convolutional neural networks (CNNs) and transformers have
achieved many successes in image classification in recent years. It has been consistently …

Background and objectives: Diabetic retinopathy is a type of diabetes that causes vascular
changes that can lead to blindness. The ravages of this disease cannot be reversed, so …

In recent years, deep neural networks (DNNs) have attracted extensive attention due to their
excellent performance in many fields of vision and speech recognition. With the increasing …

The scarcity of labelled time-series data can hinder a proper training of deep learning
models. This is especially relevant for the growing field of ubiquitous computing, where data …

Both semi-supervised learning and transfer learning aim at lowering the annotation burden
for training models. However, such two tasks are usually studied separately, ie most semi …

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the
computational efficiency of non-equilibrium reacting flow simulations while ensuring …

A new generalized framework for lung cancer detection and classification are introduced in
this paper. Specifically, two types of deep models are presented. The first model is a …