scholarly journals Predicting CO2 Emissions from Farm Inputs in Wheat Production using Artificial Neural Networks and Linear Regression Models

2016 ◽  
Vol 7 (9) ◽  
Author(s):  
Majeed Safa ◽  
Mohammadali Nejat ◽  
Peter Nuthall ◽  
Bruce Greig
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Co2 Emissions ◽  
Regression Models ◽  
Linear Regression Models ◽  
Wheat Production ◽  
Artificial Neural ◽  
Farm Inputs

scholarly journals Comparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear

2019 ◽  
Vol 11 (14) ◽  
pp. 216 ◽  
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Coefficient Of Determination ◽  
Cactus Pear ◽  
Federal Institute ◽  
The Mean ◽  
Artificial Neural

Behavior analysis and plant expression are the answers the researcher needs to construct predictive models that minimize the effects of the uncertainties of field production. The objective of this study was to compare the simple and multiple linear regression methods and the artificial neural networks to allow the maximum security in the prediction of harvest in ‘Gigante’ cactus pear. The uniformity test was conducted at the Federal Institute of Bahia, Campus Guanambi, Bahia, Brazil, coordinates 14°13′30″ S, 42°46′53″ W and altitude of 525 m. At 930 days after planting, we evaluated 384 basic units, in which were measured the following variables: plant height (PH); cladode length (CL), width (CW) and thickness (CT); cladode number (CN); total cladode area (TCA); cladode area (CA) and cladode yield (Y). For the comparison between the artificial neural networks (ANN) and regression models (single and multiple-SLR and MLR), we considered the mean prediction error (MPE), the mean quadratic error (MQE), the mean square of deviation (MSD) and the coefficient of determination (R2).The values estimated by the ANN 7-5-1 showed the best proximity to the data obtained in field conditions, followed by ANN 6-2-1, MLR (TCA and CT), SLR (TCA) and SLR (CN). In this way, the ANN models with the topologies 7-2-1 and 6-2-1, MLR with the variables total cladode area and cladode thickness and SLR with the isolated descriptors total cladode area and cladode number, explain 85.1; 81.5; 76.3; 74.09 and 65.87%, respectively, of the yield variation. The ANNs were more efficient at predicting the yield of the ‘Gigante’ cactus pear when compared to the simple and multiple linear regression models.

scholarly journals Ultrasound–assisted adsorption of toxic dyes by cottonseed cake: artificial neural networks, regression models and response surface optimization

2018 ◽  
Vol 20 (1) ◽  
pp. 14-24 ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Response Surface ◽  
Regression Models ◽  
Ph Value ◽  
Cottonseed Cake ◽  
Linear Regression Models ◽  
Decolorization Efficiency ◽  
Ultrasound Assisted ◽  
Artificial Neural

In this study, dehydrated cottonseed cake as a low-cost and abundant byproduct in Turkey was utilized as an adsorbent for the decolorization of Reactive Blue 19 (RB19) and Reactive Yellow 145 (RY145) from aqueous solutions based on adsorption and ultrasound-assisted adsorption (UAA). Decolorization efficiency was optimized as a function of changes in process type, initial pH value, adsorbent concentration, temperature, reaction time, and initial dye concentrations of RY145 and RB19 based on response surface methodology (RSM) using Box-Behnken Design. The maximum decolorization efficiency of 99.9% for both RY145 and RB19 was obtained with ultrasound-assisted adsorption under the RSM-optimized conditions (with unity desirability) of 76.98 and 79.40 min reaction times, 233.20 and 254.29 mg L-1 initial dye concentrations, 1.37 and 1.44 g L-1 adsorbent concentrations, and 35.42 and 49.37 °C, respectively. The best-fit multiple non-linear regression models of decolorization efficiency with the highest adjusted coefficients of determination (R2adj) explained 99.52% and 99.48% of variations through adsorption of RY145 and RB19 and 98.14% and 98.01% of variations through UAA of RY145 and RB19, respectively, while artificial neural networks accounted for 99.82%.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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