scholarly journals Prediction Models to Control Aging Time in Red Wine

Molecules ◽  
2019 ◽  
Vol 24 (5) ◽  
pp. 826 ◽  
Author(s):  
Gonzalo Astray ◽  
Juan Mejuto ◽  
Víctor Martínez-Martínez ◽  
Ignacio Nevares ◽  
Maria Alamo-Sanza ◽  
...  
Keyword(s):  
Aging Time ◽  
Computational Models ◽  
Red Wine ◽  
Prediction Models ◽  
Network Models ◽  
Support Vector ◽  
Red Wines ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural Network Models

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

scholarly journals Prediction Models to Control Aging Time in Red Wine

2019 ◽  
Author(s):  
G Astray ◽  
JC Mejuto ◽  
V Martinez-Martinez ◽  
I Nevares ◽  
M Alamo-Sanza ◽  
...  
Keyword(s):  
Aging Time ◽  
Computational Models ◽  
Red Wine ◽  
Prediction Models ◽  
Network Models ◽  
Support Vector ◽  
Red Wines ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural Network Models

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur along aging time can be permitted to discriminate wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certificate wines. In this research different models have developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit to determine the aging time, with an average absolute percentage deviation below 1% and it can conclude that these two models have demonstrated its capacity as a valid tool to predict the wine age.

Prediction of Concrete Strength Using Support Vector Machines Algorithm

2020 ◽  
Vol 986 ◽  
pp. 9-17
Author(s):  
Ashraf Shaqadan
Keyword(s):  
Compressive Strength ◽  
Physical Properties ◽  
Prediction Models ◽  
Concrete Strength ◽  
Network Models ◽  
Support Vector ◽  
Ann Model ◽  
Neural Network Models ◽  
Data Set ◽  
Different Levels

A laboratory analysis of concrete samples requires significant experimental time and cost. In addition, advancement in data mining provide valuable tool for researchers to extract information regarding relations among experiment and physical properties in a more elaborate way to improve prediction models performance and guide concrete mix design. A 90 samples data set is developed and used in this research. The experiment is designed to study the effect of natural silica addition at different levels on physical properties of concrete mainly compressive strength. Compressive strength is measured after 3 and 28 days for different levels of milling time. Support vector regression and neural network models are developed for predicting the compressive strength of concrete using five input variables including silica additive fraction. The SVR model metrics are compared with ANN model and showed good correlation coefficient of 0.929 but less than ANN. The advantage of SVR over ANN is shown in the developed regression model which can be interpreted physically. The silica fraction variable ranked third after curing time and cement ratio variable which indicates its importance.

scholarly journals Flood forecasting using support vector machines

2007 ◽  
Vol 9 (4) ◽  
pp. 267-276 ◽  
Author(s):  
D. Han ◽  
L. Chan ◽  
N. Zhu
Keyword(s):  
Network Models ◽  
Flood Forecasting ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Series ◽  
Neural Network Models ◽  
Optimum Selection ◽  
Svm Model ◽  
Vector Machines ◽  
Artificial Neural Network Models

This paper describes an application of SVM over the Bird Creek catchment and addresses some important issues in developing and applying SVM in flood forecasting. It has been found that, like artificial neural network models, SVM also suffers from over-fitting and under-fitting problems and the over-fitting is more damaging than under-fitting. This paper illustrates that an optimum selection among a large number of various input combinations and parameters is a real challenge for any modellers in using SVMs. A comparison with some benchmarking models has been made, i.e. Transfer Function, Trend and Naive models. It demonstrates that SVM is able to surpass all of them in the test data series, at the expense of a huge amount of time and effort. Unlike previous published results, this paper shows that linear and nonlinear kernel functions (i.e. RBF) can yield superior performances against each other under different circumstances in the same catchment. The study also shows an interesting result in the SVM response to different rainfall inputs, where lighter rainfalls would generate very different responses to heavier ones, which is a very useful way to reveal the behaviour of a SVM model.

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