Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...

The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimizationproblems and some types of ANNs such as Hopfield network and Boltzm...

Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combina...

Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...

This study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ANN) in broiler chicken. Artificial neural networks (ANNs) are powerful tools for modeling systems in a wide range of applications. The ANN model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...

In artificial intelligence (AI), new advances make it possible that artificial neural networks (ANNs) learn to solve complex problems in a reasonable amount of time (LeCun et al., 2015). To the computational neuroscientist, ANNs are theoretical vehicles that aid in the understanding of neural information processing (van Gerven). These networks can take the form of the rate-based models that are...

Artificial neural networks (ANNs), sometimes referred to as connectionist networks, are computational models based loosely on the neural architecture of the brain. Over the past twenty years, ANNs have proven to be a fruitful framework for modeling many aspects of cognition, including perception, attention, learning and memory, language, and executive control. A particular type of ANN, called a...

Here, we examine the capability of artificial neural networks (ANNs) in sensitivity analysis of the parameters of efficiency analysis model, namely data envelopment analysis (DEA). We are mainly interested to observe the required change of a group of parameters when another group goes under a managerial change, maintaining the score of the efficiency. In other words, this methodology provides a...

Definition of Artificial Neural Networks (ANNs) is made by computer scientists, artificial intelligence experts and mathematicians in various dimensions. Many of the definitions explain ANN by referring to graphics instead of giving well explained mathematical definitions; therefore, misleading weighted graphs (as in minimum cost flow problem networks) fit the definition of ANN. This study aims...

The artificial neural networks (ANNs) are well suitable to solve a variety class of problems in a knowledge discovery field (e.g., in natural language processing) because the trained networks are more accurate at classifying the examples that represent a problem domain. However, the neural networks that consist of large number of weighted connections (called also links) and activation units oft...