In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

To reduce the false detection rate of vehicle targets caused by occlusion, an improved
method of vehicle detection in different traffic scenarios based on an improved YOLO v5 …

Connected and automated vehicles (CAVs) are expected to revolutionize the transportation
industry, mainly through allowing for a real-time and seamless exchange of information …

Road accidents are one of the most relevant causes of injuries and death worldwide, and
therefore, they constitute a significant field of research on the use of advanced algorithms …

With the rapid development of urbanization, the boom of vehicle numbers has resulted in
serious traffic accidents, which led to casualties and huge economic losses. The ability to …

Road accidents are a significant negative outcome of transportation systems, causing
injuries, fatalities, traffic congestion, and economic losses. As cities expand and the number …

With the boom in Internet techniques and computer science, a variety of big data have been
introduced into forecasting research, bringing new knowledge and improving prediction …

Severe class imbalance between majority and minority classes in Big Data can bias the
predictive performance of Machine Learning algorithms toward the majority (negative) class …

Abstract The era of Big Data has arrived. Recently, under the environment of intelligent
transportation systems (ITS) and connected/automated vehicles (CAV), Big Data has been …