Artificial Neural Network-Based Predictive Model for Bacterial Growth in a Simulated Medium of Modified-Atmosphere-Packed Cooked Meat Products

2001 ◽  
Vol 49 (4) ◽  
pp. 1799-1804 ◽  
Author(s):  
Wengao Lou ◽  
Shuyro Nakai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Bacterial Growth ◽  
Meat Products ◽  
Modified Atmosphere ◽  
Artificial Neural ◽  
Cooked Meat ◽  
Cooked Meat Products

Growth of Listeria monocytogenes in modified atmosphere packed cooked meat products: a predictive model

2001 ◽  
Vol 18 (1) ◽  
pp. 53-66 ◽  
Author(s):  
F. Devlieghere ◽  
A.H. Geeraerd ◽  
K.J. Versyck ◽  
B. Vandewaetere ◽  
J. Van Impe ◽  
...  
Keyword(s):  
Listeria Monocytogenes ◽  
Predictive Model ◽  
Meat Products ◽  
Modified Atmosphere ◽  
Cooked Meat ◽  
Cooked Meat Products

scholarly journals The effect of Kurtosis on the accuracy of artificial neural network predictive model

2018 ◽  
Vol 204 ◽  
pp. 02018
Author(s):  
Aisyah Larasati ◽  
Anik Dwiastutik ◽  
Darin Ramadhanti ◽  
Aal Mahardika
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

A new predictive model for the minimum strength requirement of steel moment frames using artificial neural network

2019 ◽  
Vol 116 ◽  
pp. 69-81 ◽  
Author(s):  
Amir Ahmad Hedayat ◽  
Ehsan Ahmadi Afzadi ◽  
Hossein Kalantaripour ◽  
Esmaeil Morshedi ◽  
Amin Iranpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Moment Frames ◽  
Steel Moment Frames ◽  
Artificial Neural

scholarly journals A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 4001
Author(s):  
Sung-Sik Park ◽  
Peter D. Ogunjinmi ◽  
Seung-Wook Woo ◽  
Dong-Eun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Model Equation ◽  
Soil Layer ◽  
Unit Weight ◽  
Ann Model ◽  
Matrix Formulation ◽  
Input Parameters ◽  
Artificial Neural

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

An alternative freshness index method for modified atmosphere packaged abalone using an artificial neural network

LWT ◽  
2009 ◽  
Vol 42 (1) ◽  
pp. 343-349 ◽  
Author(s):  
Ubonrat Siripatrawan ◽  
Romanee Sanguandeekul ◽  
Vichaya Narakaew
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Modified Atmosphere ◽  
Index Method ◽  
Artificial Neural

scholarly journals Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chen-Chih Chung ◽  
Lung Chan ◽  
Oluwaseun Adebayo Bamodu ◽  
Chien-Tai Hong ◽  
Hung-Wen Chiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intracerebral Hemorrhage ◽  
Predictive Model ◽  
Sensitivity And Specificity ◽  
Characteristic Curve ◽  
Intravenous Tissue Plasminogen Activator ◽  
Symptomatic Intracerebral Hemorrhage ◽  
Ann Models ◽  
Artificial Neural

AbstractDespite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.

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