Objective: In the last two decades, there has been a growing interest in exploring surgical
procedures with statistical models to analyze operations at different semantic levels. This …

Objective: The application of artificial intelligence (AI) in health care has led to a surge of
interest in surgical process modeling (SPM). The objective of this study is to investigate the …

In this paper, we explore the possibility of exploiting retrospective clinical data from
Electronic Health Records (EHR) for classification tasks in chronic patients. The different …

Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million
people die from diseases caused by water each year, and 1.1 billion people lack access to …

During the public trials of ChatGPT, it was highlighted that the language model can generate
racially discriminatory responses. This issue, however is not new to AI. Several models and …

Learning unbiased decision boundaries is crucial for time series classification. Real-world
datasets typically exhibit long-tailed natures of class distributions, which results in an …

In Time Series Classification (TSC), temporal pooling methods that consider sequential
information have been proposed. However, we found that each temporal pooling has a …

Wearable Human Activity Recognition (HAR) is an important field of research in smart
assistive technologies. Collecting the data needed to train reliable HAR classifiers is …

Abstract In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-
class instances typically correspond to critical events, such as system faults in power grids or …

Automated seizure detection in electroencephalography (EEG) recordings is a time
consuming task, dependent on the expert performing the review. The rapid development in …