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 Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Crystals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 663
Author(s):  
Natasha Dropka ◽  
Martin Holena
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crystal Growth ◽  
Real Time ◽  
Electronic Materials ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Approach ◽  
Artificial Neural

In this review, we summarize the results concerning the application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible. This important machine learning approach thus makes it possible to determine optimized parameters for high-quality up-scaled crystals in real time.

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 A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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