Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sahar Safarian ◽  
Seyed Mohammad Ebrahimi Saryazdi ◽  
Runar Unnthorsson ◽  
Christiaan Richter
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Bioethanol Production ◽  
Syngas Fermentation ◽  
Artificial Neural Network Modeling ◽  
Artificial Neural

Porosity Estimation of Mesoporous TiO 2 ‐ZnO Nanocrystalline by Artificial Neural Network Modeling

2021 ◽  
Author(s):  
Soroush Soltani ◽  
Taha Roodbar Shojaei ◽  
Nasrin Khanian ◽  
Thomas Shean Yaw Choong ◽  
Nilofar Asim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Artificial Neural Network Modeling ◽  
Artificial Neural ◽  
Porosity Estimation

Improved estimation of energy expenditure by artificial neural network modeling

2008 ◽  
Vol 33 (6) ◽  
pp. 1213-1222 ◽  
Author(s):  
Dean Charles Hay ◽  
Akinobu Wakayama ◽  
Ken Sakamura ◽  
Senshi Fukashiro
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Validation Data ◽  
Artificial Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Estimation of energy expenditure in daily living conditions can be a tool for clinical assessment of health status, as well as a self-measure of lifestyle and general activity levels. Criterion measures are either prohibitively expensive or restricted to laboratory settings. Portable devices (heart rate monitors, pedometers) have gained recent popularity, but accuracy of the prediction equations remains questionable. This study applied an artificial neural network modeling approach to the problem of estimating energy expenditure with different dynamic inputs (accelerometry, heart rate above resting (HRar), and electromyography (EMG)). Nine feed-forward back-propagation models were trained, with the goal of minimizing the mean squared error (MSE) of the training datasets. Model 1 (accelerometry only) and model 2 (HRar only) performed poorly and had significantly greater MSE than all other models (p < 0.001). Model 3 (combined accelerometry and HRar) had overall performance similar to EMG models. Validation of all models was performed by simulating untrained datasets. MSE of all models increased when tested with validation data. While models 1 and 2 again performed poorly, model 3 MSE was lower than all but 2 EMG models. Squared correlation coefficients of measured and predicted energy expenditure for models 3 to 9 ranged from 0.745 to 0.817. Analysis of mean error within specific movement categories indicates that EMG models may be better at predicting higher-intensity energy expenditure, but combined accelerometry and HRar provides an economical solution, with sufficient accuracy.

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