scholarly journals The application of artificial neural networks (ANN) for the denaturation of meat proteins – the kinetic analysis method [pdf]

2019 ◽  
Vol 18 (1) ◽  
pp. 87-96
Author(s):  
Agnieszka Strzelczak ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kinetic Analysis ◽  
Analysis Method ◽  
Meat Proteins ◽  
Artificial Neural

scholarly journals A Mediator Role of Perceived Organizational Support in Workplace Deviance Behaviors, Organizational Citizenship and Job Satisfaction Relations

2014 ◽  
Vol 3 (3) ◽  
pp. 18-36
Author(s):  
Kusad Zorlu ◽  
Canan Bastemur
Keyword(s):  
Neural Networks ◽  
Job Satisfaction ◽  
Artificial Neural Networks ◽  
Organizational Citizenship ◽  
Organizational Support ◽  
Workplace Deviance ◽  
Analysis Method ◽  
Mediator Role ◽  
Artificial Neural

The aim of the research is to estimate the effect of workplace deviance behavior on organizational citizenship and job satisfaction and to put forward the mediator role of the organizational support perception in possible relations. The information based on hypothetical and literature are provided in the research principally and then the research part including the questionnaire applied to the employees of Kirsehir Municipality is presented. The validity and reliability tests have been performed successfully and the artificial neural network method has been used as the analysis method. In parallel with the averages and correlation values of the variables in the analysis the Artificial Neural Networks have been modelled by determining the inputs and outputs. In accordance with the findings obtained the workplace deviance behavior has a negative impact on the organizational citizenship and job satisfaction and the organizational support perception can take the mediator role as a relative for eliminating the abovementioned effect. When the artificial neural networks’ being used as the analysis method and the difficulties in measuring the workplace deviance behavior are taken into consideration it can be stated that the findings obtained have at a certain level of originality in terms of management discipline.

CLASSIFYING MYOPATHY AND NEUROPATHY NEUROMUSCULAR DISEASES USING ARTIFICIAL NEURAL NETWORKS

2010 ◽  
Vol 24 (05) ◽  
pp. 791-807 ◽  
Author(s):  
SABRI KOÇER
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Neuromuscular Diseases ◽  
Classification Systems ◽  
Support Vector ◽  
Analysis Method ◽  
Svm Classification ◽  
Acceptable Range ◽  
Artificial Neural

The aim of this study is to classify myopathy and neuropathy neuromuscular diseases using artificial neural networks. Coefficients were obtained from these EMG signals by applying Fast Fourier Transform (FFT), Autoregressive (AR), and Cepstral spectral analysis methods. Each of these coefficients was used as input data for Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM). After these inputs were individually trained in MLP, RBF and SVM classification systems, their classification and test performances were examined. The results revealed that the highest prediction was in SVM classification system, whereas the best analysis method was found to be FFT. The results show that the combination of FFT with SVM topology has provided the area under the ROC curve of 0.953, which is considered within the acceptable range.

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