scholarly journals A Data-Driven Deep Learning Model to Pattern Recognition for Medical Diagnosis, by using Model Aggregation and Model Selection

2020 ◽  
Vol 177 ◽  
pp. 387-395
Author(s):  
Choukri Djellali ◽  
Mehdi adda ◽  
Mohamed Tarik Moutacalli
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Model Selection ◽  
Medical Diagnosis ◽  
Learning Model ◽  
Data Driven ◽  
Model Aggregation ◽  
Deep Learning Model

Errata for "Cerebro: a data system for optimized deep learning model selection"

2021 ◽  
Vol 14 (6) ◽  
pp. 863-863
Author(s):  
Supun Nakandala ◽  
Yuhao Zhang ◽  
Arun Kumar
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Learning Model ◽  
Communication Cost ◽  
Data System ◽  
Training Method ◽  
Fine Grained ◽  
Correct Communication ◽  
Deep Learning Model

We discovered that there was an inconsistency in the communication cost formulation for the decentralized fine-grained training method in Table 2 of our paper [1]. We used Horovod as the archetype for decentralized fine-grained approaches, and its correct communication cost is higher than what we had reported. So, we amend the communication cost of decentralized fine-grained to [EQUATION]

scholarly journals Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5987
Author(s):  
Jerol Soibam ◽  
Achref Rabhi ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis ◽  
Rebei Bel Fdhila
Keyword(s):  
Heat Transfer ◽  
Deep Learning ◽  
Void Fraction ◽  
Wall Temperature ◽  
Flow Boiling ◽  
Learning Model ◽  
Data Driven ◽  
Subcooled Boiling ◽  
Deep Learning Model ◽  
Quantities Of Interest

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

scholarly journals Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3629
Author(s):  
Dongkwon Han ◽  
Sunil Kwon
Keyword(s):  
Deep Learning ◽  
Hydraulic Fracturing ◽  
Physical Phenomenon ◽  
Principal Component ◽  
Learning Model ◽  
Gas Production ◽  
Data Driven ◽  
Percentage Error ◽  
Proxy Model ◽  
Deep Learning Model

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariate data, and when predicting with a DNN model using 6 variables calculated through variable importance analysis. Hence, to develop a reliable deep learning model, sensitivity analysis of hyperparameters was performed to determine one-hot encoding, dropout, activation function, learning rate, hidden layer number and neuron number. As a result, the best prediction of the mean absolute percentage error of the cumulative gas production improved to at least 0.2% and up to 9.1%. The novel approach of this study can also be applied to other shale formations. Furthermore, a useful guide for economic analysis and future development plans of nearby reservoirs.

Deep Learning Model Selection Platform for Object Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 66-73
Author(s):  
Hansol Lee ◽  
Younggwan Kim ◽  
Jiman Hong
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Object Detection ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Stochastic Detection of Interior Design Styles Using a Deep-Learning Model for Reference Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7299 ◽  
Author(s):  
Jinsung Kim ◽  
Jin-Kook Lee
Keyword(s):  
Deep Learning ◽  
Interior Design ◽  
Learning Model ◽  
Data Driven ◽  
Data Preparation ◽  
Reference Images ◽  
Data Driven Approach ◽  
Image Set ◽  
Design Style ◽  
Deep Learning Model

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style of given interior design reference images using a deep learning-based data-driven approach. The proposed method can be summarized as follows: (1) data preparation based on a general design style definition, (2) implementing an interior design style recognition model using a pre-trained VGG16 model, (3) training and evaluating the trained model, and (4) demonstration of stochastic detection of interior design styles for reference images. The result shows that the trained model automatically recognizes the design styles of given interior images with probability values. The recognition results, model, and trained image set contribute to facilitating the management and utilization of an interior design references database.

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