The blossoming development of connected vehicles and the prolific infusion of artificial
intelligence, machine learning, and deep learning computation mechanisms in-car …

The complexity and hazards of autonomous vehicle systems have posed a significant
challenge in predictive maintenance. Since the incompetence of autonomous vehicle …

In today's data-driven landscape, empowering users through self-service Business
Intelligence (BI) is crucial for informed decision-making. This paper explores the …

A) reinforced the learning fault-detection fault diagnostics (DNN), transfer learning for
complex recommender systems (DNN), and conversation contextualizing (DNN Hokey's …

Autonomous driving (AD) is an emerging technology promising to revolutionize the future of
transportation. Apart from offering an opportunity for improved road safety through the …

Purpose: This ongoing research explores the strategy and frameworks employed by key
organizations and provides a current roadmap for enterprise information management …

In the automotive domain, the trend of ECUs becoming central units facilitating deployment
of functionalities sliced up across domains and their interlinking makes MC soberly complex …

However, the development of fully autonomous driving, as anticipated shortly, still poses
several partly unsolved computational, technological, and physical problems. The main one …

Data lineage refers to data sources and the data derived from them, along with the
transformations that may be acquired from these sources. Data lineage is important to …

Recent work has shown that contemporary machine learning models are capable of highly
human-like performance on a variety of real-world tasks, including complex multi-agent …