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

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

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

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

Simulated Neural Network Intelligent Computing Models for Predicting Shelf Life of Soft Cakes

This paper highlights the potential of simulated neural networks for predicting shelf life of soft cakes stored at 30o C. Elman and self organizing simulated neural network models were developed. Moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input parameters and overall acceptability score was output parameter. Neurons in each hidden layers varied from 1 to 30. The network was trained with single as well as double hidden layers with 1500 epochs and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. The shelf life predicted by simulated neural network model was 20.57 days, whereas as actual shelf life was 21 days. From the study, it can be concluded that simulated neural networks are excellent tool in predicting shelf life of soft cakes

Optimal tie-breaking rules

We consider two-player contests with the possibility of ties and study the effect of different tie-breaking rules on effort. For ratio-form and difference-form contests that admit pure-strategy Nash equilibrium, we find that the effort of both players is monotone decreasing in the probability that ties are broken in favor of the stronger player. Thus, the effort-maximizing tie-breaking rule commits to breaking ties in favor of the weaker agent. With symmetric agents, we find that the equilibrium is generally symmetric and independent of the tie-breaking rule. We also study the design of random tie-breaking rules that are ex-ante fair and identify sufficient conditions under which breaking ties before the contest actually leads to greater expected effort than the more commonly observed practice of breaking ties after the contest.Comment: 25 page

A Study of Side Effects & Tolerability of Intravesical ‘BCG’ in Superficial Bladder Cancer.

INTRODUCTION: The Ca Bladder is one of the important tumors of the genitourinary cancers and usually Affects the people all around the globe. Broadly urinary bladder cancers can be divided into Superficial, muscle invasive and metastatic cancers. Of which superficial cancers constitute the Majority of cases. Superficial bladder cancers are the tumors confined to the mucosa and Submucosa (Ta, Tis & T1). This is a heterogeneous disease with variable natural history. At one end of the spectrum are low grade tumors (Ta) with low potential for progression & rarely represent a threat to the patient, while at the other end are the high grade (T1) tumors With high malignant potential with significant progression and death. Although thorough endoscopic tumor resection remains the principal treatment, Intravesical agents have become an important in the subgroup of tumors that are at risk of Progression. Intravesical Bacillus Calmette-Guerin, is effective treatment in preventing Recurrences & delaying progression in superficial bladder cancer. It has been in use since the 1980's, and is the most proven and effective form of immunotherapy. Though therapy with BCG is generally safe, it is not completely, without side effects. This study was done to know the various side effects, outcomes and tolerability of the Therapy. AIMS AND OBJECTIVES: 1. To study the side effects & complications following intravesical instillation of BCG. 2. To study the tolerability following administration of intravesical BCG

Evalutaion of chip breaker using flank wear

Machining is a common and essential part of manufacturing of almost all metal products, and also or other materials like wood and plastic. In the today’s era of automatic machines optimization of machining operations is one of the key requirements. During turning operation, unbroken chips pose a major hindrance during machining and hence appropriate control of the chip shape becomes a very important task for maintaining reliable machining process. The continuous chip generated during turning operation deteriorates the workpiece precision and causes safety hazards for the operator. In particular, effective chip control is necessary for a CNC machine or automatic production system because any failure in chip control can cause the lowering in productivity and the worsening in operation due to frequent stop. Chip control in turning is difficult in the case of mild steel because chips are continuous. Thus the development of a chip breaker for mild steel is an important subject for the automation of turning operations. In this study, the role of different parameters like speed, feed and depth of cut, tool flank wear and chip breaker height and width are studied. In this study chip characteristics were tested for changing tool flank wear values. Response surface methodology was used to analyze the relationship between several explanatory variables and two predecided response variables. The chips obtained were found to have greater thickness at low feed and depth of cut, and gradually decreased as feed and depth of cut increases. The analysis lead to the conclusion that cutting speed and depth of cut are the most significant factors along with their higher order terms and interactions between variables