In the past 25 years, computer scientists and statisticians developed machine learning
algorithms capable of modeling highly nonlinear transformations and interactions of input …

Abnormal driving may cause serious danger to both the driver and the public. Existing
detectors of abnormal driving behavior are mainly based on shallow models, which require …

This study focuses on driver-behavior identification and its application to finding embedded
solutions in a connected car environment. We present a lightweight, end-to-end deep …

Identifying vehicle maneuvers in the context of Connected Vehicles (CV) system brings huge
potentials to enhance traffic safety. However, this process requires various advanced …

Identifying driving styles using classification models with in-vehicle data can provide
automated feedback to drivers on their driving behavior, particularly if they are driving safely …

Driver behavior profiling has been gaining increased attention due to its relevance in many
applications. For instance, car insurance telematics and fleet management entities have …

This paper addresses driver identification and verification using Deep Learning (DL) on tri-
axial accelerometer signals from drivers' smartphones. The proposed driver identification …

Recent years have seen increasing utilization of deep learning methods to analyze large
collections of medical data and signals effectively in the Internet of Medical Things (IoMT) …

Abstract Cyber-Physical Systems (CPS) serves as an interdisciplinary effort that incorporates
cyber vector as well as physical vector. The latter can generate exponentially growing …

1. Remotely tracking distinct behaviours of animals using acceleration data and machine
learning has been carried out successfully in several species in captive settings. In order to …