Complex systems are typically characterized by intricate internal dynamics that are often
hard to elucidate. Ideally, this requires methods that allow to detect and classify in an …

Featured Application Nowadays, many applications and disciplines work on the basis of
stream data. Common examples are the IoT sector (eg, sensor data analysis), or video …

Accuracy is a key focus of current work in time series classification. However, speed and
data reduction are equally important in many applications, especially when the data scale …

Abstract The identification of Obstructive Sleep Apnea (OSA) is done by a Polysomnography
test which is often done in later ages. Being able to notify potential insured members at …

In recent years, computational approaches for extracting customer needs from user
generated content have been proposed. However, there is a lack of studies that focus on …

Multivariate time series record sequences of values using multiple sensors or channels. In
the classification task, we have a class label associated with each multivariate time series …

Nowadays, the deployment of deep learning models on edge devices for addressing real-
world classification problems is becoming more prevalent. Moreover, there is a growing …

The transition to fully autonomous roadways will include a long period of mixed-autonomy
traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs) which …

As machine and deep learning solutions are deployed on edge devices to tackle real-world
classification problems, the approach of early classification during inference is becoming …

Multivariate time series classification (MTSC) is an area of machine learning that deals with
predicting a discrete target variable from multidimensional time dependent data. The …