Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …

Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of
applications like delivery systems, self-driving taxis, and ambulances. AVs utilize the power …

The digitalization revolution plays a crucial role in every government administration. It
manages a considerable volume of user information and is currently seeing an increase in …

This paper evaluates the ability of some state-of-the-art Machine Learning models, namely
SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to …

Mechanical characterization of dynamic DNA nanodevices is essential to facilitate their use
in applications like molecular diagnostics, force sensing, and nanorobotics that rely on …

Deep learning (DL) is one of the most promising technological developments emerging in
the fourth industrial revolution era for businesses to improve processes, increase efficiency …

Autoencoder-based anomaly detection approaches can be used for precluding scope
compliance failures of the automotive perception. However, the applicability of these …

Currently, road surface conditions ahead of autonomous vehicles are not well detected by
the existing sensors on those autonomous vehicles. However, driving safety should be …

Rapid developments in automatic driving technology have given rise to new experiences for
passengers. Safety is a main priority in automatic driving. A strong familiarity with road …

The breakthrough success of many deep learning approaches is mainly due to the
availability of large-scale labeled datasets. However, large-scale labeled datasets are not …