Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …

By leveraging deep learning-based technologies, industrial artificial intelligence (IAI) has
been applied to solve various industrial challenging problems in Industry 4.0. However, for …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

Differential privacy is a strong notion for privacy that can be used to prove formal
guarantees, in terms of a privacy budget, ε, about how much information is leaked by a …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …

Deep Learning has recently become hugely popular in machine learning for its ability to
solve end-to-end learning systems, in which the features and the classifiers are learned …

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell
failures, facilitate battery deployment, and promote low-carbon economies. Despite the …

Unlearning the data observed during the training of a machine learning (ML) model is an
important task that can play a pivotal role in fortifying the privacy and security of ML-based …