Artificial Neural Network Approach for Revealing Individuality, Group Membership and Age Information in Goat Kid Contact Calls
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چکیده
منابع مشابه
Artificial Neural Network Approach for Revealing Individuality, Group Membership and Age Information in Goat Kid Contact Calls
Machine learning techniques are becoming an important tool for studying animal vocal communication. The goat (Capra hircus) is a very social species, in which vocal communication and recognition are important. We tested the reliability of a Multi-Layer Perceptron (feed-forward Artificial Neural Network, ANN) to automate the process of classification of calls according to individual identity, gr...
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Acoustic communication has great importance in social relationships and especially in individual recognition in the Greater Flamingo Phoenicopterws ruber However, it is constrained by the noisy environment of the colony. I performed an analysis of both the temporal and frequency patterns of contact calls and showed that the use of both is essential for discrimination between individuals. I made...
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Comparison of Artificial Neural Network and Regression Models for Prediction of Body Weight in Raini Cashmere Goat
The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers ...
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Background: Although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. Artificial neural network (ANN) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. Methods: In this study, ANN was applied to simulate groundwater quality ...
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ژورنال
عنوان ژورنال: Acta Acustica united with Acustica
سال: 2014
ISSN: 1610-1928
DOI: 10.3813/aaa.918758