Cascade Modelling for Predicting Solubility Index of Roller Dried Goat Whole Milk Powder

The 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

E-Learning: Future of Education

This paper highlights the significance of E-learning in modern education and discusses its technical aspect, market, pros and cons, comparison with instructor led training and possibility of weather E-learning will replace the old classroom teaching. Presently the concept of E-learning is becoming very popular as the numbers of internet savvy users are increasing. E-learning gives the advantage of 24x7 and 365 days a year round access as compared to Instructor-Led Training, which is one time class that must be scheduled. E-learning is cost effective as course content once developed could be easily used and modified for teaching and training. E-learning also provides students freedom from carrying heavy school bags and stop cutting of trees for the sake of paper, pencil and rubber. E-learning is the future of education as it is interactive, interesting and entertaining way of learning, and will soon replace the paper books in the form of touch screen tablets

Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models

For 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

Soft Computing Methodology for Shelf Life Prediction of Processed Cheese

Feedforward 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

Application of simulated neural networks as Non-Linear Modular Modeling Method for predicting shelf life of processed cheese / Sumit Goyal and Gyanendra Kumar Goyal

This paper presents the capability of simulated neural network (SNN) models for predicting the shelf life of processed cheese stored at ambient temperature 30o C. Processed cheese is a dairy product generally made from medium ripened Cheddar cheese. Elman and Linear Layer(Train) SNN models were developed. Body & texture, aroma & flavour, moisture, free fatty acids were used as input variables and sensory score as the output. Neurons in each hidden layers varied from 1 to 40. The network was trained with single as well as double hidden layers up to 100 epochs, and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient performance measures were used for testing prediction potential of the developed models. Results showed a 4201 topology was able to predict the shelf life of processed cheese exceedingly well with R2 as 0.99992157. The corresponding RMSE for this topology was 0.003615359. From this study it is concluded that SNN models are excellent tool for predicting the shelf life of processed cheese

Artificial Neural Expert Computing Models for Determining Shelf Life of Processed Cheese

Time-delay single and multi layer models were developed for predicting shelf life of processed cheese stored at 30oC. Processed cheese is very nutritious dairy product, rich in milk proteins and milk fat. For developing computational neuroscience models,experimental data relating to body & texture, aroma & flavour, moisture, free fatty acids were taken as input variables, while sensory score as output variable. Mean Square Error, Root Mean Square Error, Coefficient of determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction performance of the developed computational models. The results of the study established excellent correlation between experimental data and the predicted values, with a high determination coefficient. From the study it was concluded that artificial neural expert time-delay models are good for predicting the shelf life of processed cheese.DOI:http://dx.doi.org/10.11591/ijece.v2i3.35

Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product

Feedforward 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

Estimating Processed Cheese Shelf Life with Artificial Neural Networks

Cascade 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