Machine-learning prediction for quasiparton distribution function matrix elements

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...

Transparent Machine Learning Algorithm Offers Useful Prediction Method for Natural Gas Density

Machine-learning algorithms aid predictions for complex systems with multiple influencing variables. However, many neural-network related algorithms behave as black boxes in terms of revealing how the prediction of each data record is performed. This drawback limits their ability to provide detailed insights concerning the workings of the underlying system, or to relate predictions to specific ...

Stock Price Prediction using Machine Learning and Swarm Intelligence

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

Machine Learning for Protein Function

Systematic identification of protein function is a key problem in current biology. Most traditional methods fail to identify functionally equivalent proteins if they lack similar sequences, structural data or extensive manual annotations. In this thesis, I focused on feature engineering and machine learning methods for identifying diverse classes of proteins that share functional relatedness bu...

Sparse Greedy Matrix Approximation for Machine Learning