Signal Processing and Machine Learning for Air Traffic Delay Prediction
نویسندگان
چکیده
منابع مشابه
Machine Learning for Traffic Prediction
Using machine learning for predicting traffic is described in the context of a competition organized using the TunedIT platform. A heuristic is proposed for reconstructing the route of a car in a street graph from a temporal stream of its coordinates. A resilient propagation neural network for approximating the average velocity on a given street from irregular time series of instantaneous veloc...
متن کاملMachine Learning for Signal Processing
The 25th MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions. Prospective authors are invited to submit papers on relevant algorithms and applications incl...
متن کاملExtreme Learning Machine for Graph Signal Processing
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such...
متن کاملGuest Editorial: Machine Learning for Signal Processing
Signal processing including analysis, understanding, detection, estimation, and modelling of the events and trends, the way they evolve, and the abnormalities and anomalies affecting them have attracted many researchers around the globe. Signal processing theory originates from mathematical foundation with astonishing applications which help information technologists discover and invent new rea...
متن کاملTrends in Machine Learning for Signal Processing
SP AND ERROR CORRECTION FOR NONVOLATILE MEMORY DEVICES Nonvolatile storage devices in the form of NAND flash memories and solidstate drives have become the storage techniques of choice in many mobile and portable devices. The continued density growth of these devices has been mainly driven by aggressive technology scaling and the use of multilevel per-cell techniques. However, bit errors are be...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transportation research procedia
سال: 2021
ISSN: ['2352-1457', '2352-1465']
DOI: https://doi.org/10.1016/j.trpro.2021.11.062