首页|Research from Center for Medical Science Has Provided New Study Findings on Mach ine Learning (ELMOPP: an application of graph theory and machine learning to tra ffic light coordination)
Research from Center for Medical Science Has Provided New Study Findings on Mach ine Learning (ELMOPP: an application of graph theory and machine learning to tra ffic light coordination)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on artificial intelligence have been published. According to news reporting out of Virginia, United States, by NewsRx editors, research stated, “Purpose - This paper presents the Edge Loa d Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine le arning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patter ns to inform its real-time decisions and better mitigate traffic by predicting f uture traffic flow based on past flow and using those predictions to both maximi ze present traffic flow and decrease future traffic congestion.”
Center for Medical ScienceVirginiaUn ited StatesNorth and Central AmericaAlgorithmsCyborgsEmerging Technologi esMachine LearningMathematical Theories
NETL
NSTL
