1,720,961 research outputs found
Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product
No full textFeedforward multilayer machine learning models were developed to predict the shelf life of burfi stored at 30oC. Experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output. Bayesian regularization algorithm was used for training the network. The transfer function for hidden layers was tangent sigmoid, and for the output layer it was purelinear function. The network was trained with 100 epochs, and neurons in each hidden layers varied from 3:3 to 20:20. Excellent agreement was found between the actual and predicted values establishing that feedforward multilayer machine learning models are efficient in predicting the shelf life of burfi
Soft Computing Methodology for Shelf Life Prediction of Processed Cheese
Get PDFFeedforward multilayer models were developed for predicting shelf life of processed cheese stored at 30o C. Input variables were Soluble nitrogen, pH, Standard plate count, Yeast & mould count and Spore count. Sensory score was taken as output parameter for developing feedforward multilayer models. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient performance measures were implemented for testing prediction potential of the soft computing models. The study revealed that soft computing multilayer models can predict shelf life of processed cheese.DOI: http://dx.doi.org/10.11591/ij-ict.v1i1.50
Soft Computing Methodology for Shelf Life Prediction of Processed Cheese
No full textFeedforward multilayer models were developed for predicting shelf life of processed cheese stored at 30o C. Input variables were Soluble nitrogen, pH, Standard plate count, Yeast & mould count and Spore count. Sensory score was taken as output parameter for developing feedforward multilayer models. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient performance measures were implemented for testing prediction potential of the soft computing models. The study revealed that soft computing multilayer models can predict shelf life of processed cheese.DOI: http://dx.doi.org/10.11591/ij-ict.v1i1.50
Cascade modelling for predicting solubility index of roller dried goat whole milk powder
Full text linkThe aim of this work was to investigate the prediction ability of Cascade artificial neural network (ANN) models for solubility index of roller dried goat whole milk powder. The input variables for ANN model were: loose bulk density, packed bulk density, wettability and dispersibility, while solubility index was the output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used as performance measures. Modelling results indicated very good agreement between the actual and the predicted data, thus confirming that ANN could be used to predict solubility index of roller dried goat whole milk powder
Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product
Get PDFFeedforward multilayer machine learning models were developed to predict the shelf life of burfi stored at 30oC. Experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output. Bayesian regularization algorithm was used for training the network. The transfer function for hidden layers was tangent sigmoid, and for the output layer it was purelinear function. The network was trained with 100 epochs, and neurons in each hidden layers varied from 3:3 to 20:20. Excellent agreement was found between the actual and predicted values establishing that feedforward multilayer machine learning models are efficient in predicting the shelf life of burfi
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Estimating Processed Cheese Shelf Life with Artificial Neural Networks
Get PDFCascade multilayer artificial neural network (ANN) models were developed for estimating the shelf life of processed cheese stored at 7-8oC.Mean square error , root mean square error,coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models.The developed model with a combination of 5à16à16à1 showed excellent agreement between the actual and the predicted data , thus confirming that multilayer cascade models are good in estimating the shelf life of processed cheese.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.33
Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models
Full text linkFor predicting the shelf life of processed cheese stored at 7-8 C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese
Evaluation of shelf life of processed cheese by implementing neural computing models
No full textFor predicting the shelf life of processed cheese
stored at 7-8º C, Elman single and multilayer models were
developed and compared. The input variables used for developing
the models were soluble nitrogen, pH; standard plate count, Yeast
& mould count, and spore count, while output variable was
sensory score. Mean Square Error, Root Mean Square Error,
Coefficient of Determination and Nash - Sutcliffo Coefficient
were applied in order to compare the prediction ability of the
developed models. The Elman models got simulated very well and
showed excellent agreement between the experimental data and
the predicted values, suggesting that the Elman models can be
used for predicting the shelf life of processed cheese
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