scholarly journals Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

2010 ◽  
Author(s):  
S. N. Abdullah ◽  
X. Zeng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crude Oil ◽  
Oil Price ◽  
Learning Approach ◽  
Crude Oil Price ◽  
Price Prediction ◽  
Machine Learning Approach ◽  
Q Model

Artificial neural networks for the prediction of solvation energies based on experimental and computational data

2020 ◽  
Vol 22 (42) ◽  
pp. 24359-24364
Author(s):  
Jiyoung Yang ◽  
Matthias J. Knape ◽  
Oliver Burkert ◽  
Virginia Mazzini ◽  
Alexander Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ion Pair ◽  
Learning Approach ◽  
Solvation Energies ◽  
Machine Learning Approach ◽  
Computational Data ◽  
Artificial Neural

We present a machine learning approach based on artificial neural networks for the prediction of ion pair solvation energies.

scholarly journals A Machine Learning Approach to Interpolating Indoors Trajectories

2021 ◽  
Author(s):  
Daniel Carvalho ◽  
Daniel Sullivan ◽  
Rafael Almeida ◽  
Carlos Caminha
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Continuous Monitoring ◽  
Learning Approach ◽  
Ensemble Models ◽  
Server Side ◽  
Machine Learning Approach ◽  
Network Overload

In this article we propose a machine learning-based modeling to solve network overload problems caused by continuous monitoring of the trajectories of multiple tracked devices indoors. The proposed modeling was evaluated with hundreds of object coordinate locations tracked in three synthetic environments and one real environment. We show that it is possible to solve the problem of network overload increasing latency in sending data and predicting as server-side trajectories with ensemble models, such as the Random Forest, and using Artificial Neural Networks. We also show that it is possible to predict at least fifteen intermediate coordinates of the paths of the tracked objects with R2 greater than 0.95.

scholarly journals Identification of Mouth Cancer laceration Using Machine Learning Approach

2018 ◽  
Vol 7 (03) ◽  
pp. 01-07
Author(s):  
Prof. Barry Wiling
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Histogram Equalization ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Segmented Image ◽  
Svm Algorithm ◽  
Machine Learning Approach

This Paper describes about Identification of Mouth Cancer laceration Using Machine Learning Approach .The SVM algorithm is used for this purpose. Image segmentation operations are performed using: Resizing an image, Gray scale conversion, Histogram equalization and Classifying the Segmented image using SVM. SVM is used to reduce the complexity faced in the existing system comprising of Texture Segmentation and ANN (Artificial Neural Networks) Algorithm. SVM is a simple Machine Learning algorithm when compared to ANN. The outcome of the paper is to segment and classify the Malignancy from the Non-Malignant region using the classifier SVM. SVM performs the classification based on the dataset that contains the trained images.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 363-388
Author(s):  
Serkan Aras ◽  
Manel Hamdi
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Significant Interaction ◽  
Statistical Tests ◽  
Oil Price ◽  
Training Data ◽  
Forecasting Model ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

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