Driver behavior is receiving increasing attention because of the staggering number of road
accidents. Many road safety reports regard human behavior as the most important factor in …

The prevalence of smartphones among vehicle drivers presents exciting opportunities in
assessing pavement roughness in a more efficient and cost-effective manner, compared …

In the era of Internet of things (IoT), network Connection of an enormous number of
agriculture machines and service centers is an expectation. However, it will be with a …

Abstract Today's Agriculture vehicles (AgV) s are expected to encompass mainly the three
requirements of customers; economy, the use of High technology and reliability. In this …

This paper proposes a method for vehicle driving behavior recognition based on a six-axis
motion processor. This method uses deep-learning technology to learn the sample data …

This paper focuses on developing a computationally economic lightweight artificial
intelligence (AI) technology for smartphones. Until date, no commercial system is available …

In a wide range of ML applications, the training data contains privacy-sensitive information
that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML …

The current paper implements a methodology for automatically detecting vehicle maneuvers
from vehicle telemetry data under naturalistic driving settings. Previous approaches have …

Model inversion attack (MIA) is a cyber threat with an increasing alert even for deep-learning-
based recognition systems (DLRSs). By targeting a DLRS under a scenario of attacker …

Industry 4.0 can be considered the 21st century's industrial revolution and will soon be the
new form of manufacturing delight. The definitive customer would experience manufacturing …