Network-based security has emerged as an increasingly critical challenge in the domain of
the Internet of Things (IoT). A number of network intrusion detection systems (NIDS), typically …

With the rapid evolution of the Internet of Things, many real-world applications utilize
heterogeneously connected sensors to capture time-series information. Edge-based …

The Internet of Things (IoT) has facilitated many applications utilizing edge-based machine
learning (ML) methods to analyze locally collected data. Unfortunately, popular ML …

Many real-world applications of the Internet of Things (IoT) employ machine learning (ML)
algorithms to analyze time series information collected by interconnected sensors. However …

TinyML models often operate in remote, dynamic environments without cloud connectivity,
making them prone to failures. Ensuring reliability in such scenarios requires not only …

Alcohol consumption has a significant impact on individuals' health, with even more
pronounced consequences when consumption becomes excessive. One approach to …

Deep learning has been widely adopted for human activity recognition (HAR) while
generalizing a trained model across diverse users and scenarios remains challenging due …