An Improved ANN Model for Prediction of Solar Radiation Using Machine Learning Approach

2020 ◽  
pp. 233-242
Author(s):  
Rita Banik ◽  
Priyanath Das ◽  
Srimanta Ray ◽  
Ankur Biswas
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Learning Approach ◽  
Ann Model ◽  
Machine Learning Approach

scholarly journals Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

2021 ◽  
Vol 11 (24) ◽  
pp. 11710
Author(s):  
Matteo Miani ◽  
Matteo Dunnhofer ◽  
Fabio Rondinella ◽  
Evangelos Manthos ◽  
Jan Valentin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Optimization ◽  
Automatic Identification ◽  
Learning Approach ◽  
Ann Model ◽  
Data Set ◽  
Bituminous Mixtures ◽  
Novel Approach ◽  
The Neural Network ◽  
Machine Learning Approach

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

scholarly journals A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0180239 ◽  
Author(s):  
Qingtao Zhou ◽  
Alejandro Flores ◽  
Nancy F. Glenn ◽  
Reggie Walters ◽  
Bangshuai Han
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Learning Approach ◽  
Arid Ecosystem ◽  
Machine Learning Approach ◽  
Data Products ◽  
Derived Data ◽  
Semi Arid ◽  
The U.S

scholarly journals Integrating metabolomic data with machine learning approach for discovery of Q-markers from Jinqi Jiangtang preparation against type 2 diabetes

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Lele Yang ◽  
Yan Xue ◽  
Jinchao Wei ◽  
Qi Dai ◽  
Peng Li
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Herbal Medicines ◽  
Partial Least Square ◽  
Least Square ◽  
Learning Approach ◽  
Ann Model ◽  
Machine Learning Approach ◽  
Quality Markers

Abstract Background Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little research has been done to identify and classify its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation. Methods This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishment of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled by using the untargeted UPLC-LTQ-Orbitrap metabolomic approach. The chemical features obtained which were associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV). Results Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of partial least square (PLS) model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. With this machine learning model, 10 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted. Conclusions This proposed artificial intelligence approach is desirable for quick and easy identification of Q-markers with bioactivity from JQJT preparation.

scholarly journals Integrating Metabolomic Data With Machine Learning Approach for Discovery of Q-markers From Jinqi Jiangtang Preparation Against Type 2 Diabetes

2020 ◽  
Author(s):  
Lele Yang ◽  
Yan Xue ◽  
Jinchao Wei ◽  
Qi Dai ◽  
Peng Li
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Herbal Medicines ◽  
Calibration Model ◽  
Learning Approach ◽  
Ann Model ◽  
Metabolomic Data ◽  
Machine Learning Approach ◽  
Quality Markers

Abstract Background: Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little was known about its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation. Methods: This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishing of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled using an untargeted UPLC-LTQ-Orbitrap metabolomic approach. The obtained chemical features associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection, respectively. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).Results: Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of PLSR calibration model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. Using this machine learning model, 15 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.Conclusions: The proposed artificial intelligence approach is suitable for quick and easy discovery of Q-markers with bioactivity from herbal medicines.

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