2019
DOI: 10.1007/s10765-019-2590-5
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Artificial Neural Network for Modeling Thermal Conductivity of Biodiesels with Different Metallic Nanoparticles for Heat Transfer Applications

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Cited by 6 publications
(1 citation statement)
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“…The issue is that most of the NN literature involving fluorescence or even infrared imaging is focused on image enhancement [19] (with a special focus with infrared [20]) or object recognition in thermal images [21], rather than improved temperature accuracy. However, in terms of interpreting thermal data, there has been success in determining Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent thermophysical properties from measurements via genetic algorithms and neural networks (NN) [22,23] and predicting properties of alloys from pure metals in a database [24] or based on nanoparticle content [25].…”
Section: Neural Network Approaches To Temperature Analysismentioning
confidence: 99%
“…The issue is that most of the NN literature involving fluorescence or even infrared imaging is focused on image enhancement [19] (with a special focus with infrared [20]) or object recognition in thermal images [21], rather than improved temperature accuracy. However, in terms of interpreting thermal data, there has been success in determining Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent thermophysical properties from measurements via genetic algorithms and neural networks (NN) [22,23] and predicting properties of alloys from pure metals in a database [24] or based on nanoparticle content [25].…”
Section: Neural Network Approaches To Temperature Analysismentioning
confidence: 99%