scholarly journals Soft Computing Prediction of Oil Extraction from Huracrepitan Seeds

2020 ◽  
Vol 69 (11-12) ◽  
pp. 653-658
Author(s):  
Kenechi Nwosu-Obieogu ◽  
Felix Aguele ◽  
Linus Chiemenem
Keyword(s):  
Performance Indicators ◽  
Seed Oil ◽  
Fuzzy Inference ◽  
Extraction Process ◽  
Oil Yield ◽  
Solvent Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Yield Sensitivity

This study analyses the extraction process parameters of huracrepitan seed oil using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). The experiments were conducted at temperature (60–80 °C), time (4–6 h), and solute/solvent ratio (0.05–0.10) with output parameter as oil yield. Sensitivity analysis shows that temperature and time had the most significant effect on the oil yield. The oil yield estimation performance indicators are: ANN (<i>R</i><sup>2</sup> = 0.999, MSE = 5.63192E-13), ANFIS (<i>R</i><sup>2</sup> = 0.36927, MSE = 0.42331). The results show that ANN gave a better prediction than ANFIS.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

2014 ◽  
Vol 9 (12) ◽  
pp. 1226-1234
Author(s):  
Kadir Temizel ◽  
Mehmet Odabas ◽  
Nurettin Senyer ◽  
Gokhan Kayhan ◽  
Sreekala Bajwa ◽  
...  
Keyword(s):  
High Performance ◽  
Fuzzy Inference ◽  
High Water ◽  
Stress Conditions ◽  
Ann Model ◽  
Inference System ◽  
Leaf Reflectance ◽  
Neuro Fuzzy ◽  
Salinity Levels ◽  
Artificial Neural Network Ann

AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

Investigation of trap efficiency of retention dams

2017 ◽  
Vol 18 (2) ◽  
pp. 450-459 ◽  
Author(s):  
Abbas Parsaie ◽  
Samad Ememgholizadeh ◽  
Amir Hamzeh Haghiabi ◽  
Amir Moradinejad
Keyword(s):  
Fuzzy Inference ◽  
Kernel Functions ◽  
Support Vector ◽  
Inference System ◽  
Trap Efficiency ◽  
Neuro Fuzzy ◽  
Management Plans ◽  
Longitudinal Slope ◽  
The Mean ◽  
Artificial Neural Network Ann

Abstract In this paper, the trap efficiency (TE) of retention dams was investigated using laboratory experiments. To map the relation between TE and involved parameters, artificial intelligence (AI) methods including artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were utilized. Results of experiments indicated that the range of TE varies between 30 and 98%; hence, this structure can be recommended to control sediment transport in watershed management plans. Experimental results showed that by increasing the longitudinal slope of streams, TE decreases. This finding was observed for Vf/Vs parameter, as well. By increasing the mean diameter grain size (D50) and specific gravity of sediments (Gs), TE increases. Results of all applied AI models demonstrated that all of them have suitable performance; however, the minimum data dispersivity was observed in SVM outcomes. It is notable that the best performance of transfer, membership and kernel functions were related to tansig, gaussmf and radial basis function (RBF) for ANN, SVM and ANFIS, respectively.

scholarly journals Specific Wear Rate Modeling of Polytetraflouroethylene Composites Via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools

2021 ◽  
Author(s):  
Musa Alhaji Ibrahim ◽  
Yusuf Şahin ◽  
Auwal Ibrahim ◽  
Auwalu Yusuf Gidado ◽  
Mukhtar Nuhu Yahya
Keyword(s):  
Neural Network ◽  
Wear Rate ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Specific Wear Rate ◽  
Inference System ◽  
Modeling And Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Ptfe Composites

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.

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