scholarly journals A MACHINE LEARNING APPROACH TO IMPROVING FORECASTING ACCURACY OF HOTEL DEMAND: A COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND TRADITIONAL MODELS

2020 ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Learning Approach ◽  
Forecasting Accuracy ◽  
Machine Learning Approach

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.

Comparative analysis of a deep learning approach with various classification techniques for credit score computation

2020 ◽  
Vol 13 ◽  
Author(s):  
Arvind Pandey ◽  
Shipra Shukla ◽  
Krishna Kumar Mohbey
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Probit Regression ◽  
Credit Score ◽  
Machine Learning Approach

Background: Large financial companies are perpetually creating and updating customer scoring techniques. From a risk management view, this research for the predictive accuracy of probability is of vital importance than the traditional binary result of classification, i.e., non-credible and credible customers. The customer's default payment in Taiwan is explored for the case study. Objective: The aim is to audit the comparison between the predictive accuracy of the probability of default with various techniques of statistics and machine learning. Method: In this paper, nine predictive models are compared from which the results of the six models are taken into consideration. Deep learning-based H2O, XGBoost, logistic regression, gradient boosting, naïve Bayes, logit model, and probit regression comparative analysis is performed. The software tools such as R and SAS (university edition) is employed for machine learning and statistical model evaluation. Results: Through the experimental study, we demonstrate that XGBoost performs better than other AI and ML algorithms. Conclusion: Machine learning approach such as XGBoost effectively used for credit scoring, among other data mining and statistical approaches.

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 Assessment of bilateral knee pain from MR imaging using deep neural networks

2018 ◽  
Author(s):  
Gary H. Chang ◽  
David T. Felson ◽  
Shangran Qiu ◽  
Terence D. Capellini ◽  
Vijaya B. Kolachalama
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Neural Networks ◽  
Deep Learning ◽  
Knee Pain ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Mr Images ◽  
Bilateral Knee ◽  
Machine Learning Approach

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

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