This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

These days cloud computing is booming like no other technology. Every organization whether it’s small, mid-sized or big, wants to adapt this cutting edge technology for its business. As cloud technology becomes immensely popular among these businesses, the question arises: Which cloud model to consider for your business? There are four types of cloud models available in the market: Public, Private, Hybrid and Community. This review paper answers the question, which model would be most beneficial for your business. All the four models are defined, discussed and compared with the benefits and pitfalls, thus giving you a clear idea, which model to adopt for your organization.

Elman artificial neural network single and multilayer computerized models were developed for predicting the shelf life of burfi stored at 30ºC. The experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were taken as input variables, and overall acceptability score as output variable for developing the models. Bayesian regularization algorithm was applied as training algorithm for neural network. Transfer function for hidden layers was tangent sigmoid; while for output layer it was pure linear function. Elman model with a combination of 5→10→1 and 5→7→7→1 performed exceedingly well for predicting the shelf life of burfi.

Soft computing cascade multilayer models were developed for predicting the shelf life of burfi stored at 30oC. The 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 variable. The modelling results showed excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.993499439) and low RMSE (0.006500561), indicating that the developed model was able to analyze nonlinear multivariate data with very good performance, and can be used for predicting the shelf life of burfi.

This paper highlights the significance of Time-Delay ANN models for predicting shelf life of processed cheese stored at 7-8oC. Bayesian regularization algorithm was selected as training function. Number of neurons in single and multiple hidden layers varied from 1 to 20. The network was trained with up to 100 epochs. Mean square error, root mean square error, coefficient of determination and nash - Sutcliffe coefficient were used for calculating the prediction capability of the developed models. Time-Delay ANN models with multilayer are quite efficient in predicting the shelf life of processed cheese stored at 7-8^oC.

This paper presents the latency and potential of central nervous system based system intelligent computer engineering system for detecting shelf life of soft mouth melting milk cakes stored at 10o C. Soft mouth melting milk cakes are exquisite sweetmeat cuisine made out of heat and acid thickened solidified sweetened milk. In today’s highly competitive market consumers look for good quality food products. Shelf life is a good and accurate indicator to the food quality and safety. To achieve good quality of food products, detection of shelf life is important. Central nervous system based intelligent computing model was developed which detected 19.82 days shelf life, as against 21 days experimental shelf life.