Abstract: Over 80% of mishaps are caused by a lack of identifying the accident on time ... Based on the performance of machine learning algorithms, comparative analysis is performed and the results ...

The objective of this project is to develop a method based on Machine Learning algorithms to detect traffic accidents in real-time with the use of traffic cameras. Early accident detection systems can ...

One key part of Microsoft’s big bet on machine learning is ... engine that selects a detection model that fits the time-series data being used. By choosing an algorithm at runtime, Microsoft ...

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant ... and kernel-based learning algorithms by the algorithm type. In addition, the ...

Other use cases are being explored, ranging from image generation to disease detection ... machine learning works, followed by a short guide to implementing and training a machine learning algorithm.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products ... to brake to prevent an accident. The study was published ...

Scientists have recently detailed how automation and machine learning can ... Hartings said when the algorithm is fully realized and implemented, automated SD detection would allow any ...

others with infections including COVID-19 or autoimmune diseases including lupus and Type 1 diabetes - the algorithm the researchers developed, called Mal-ID for machine learning for immunological ...