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Titlebook: 15th International Congress on Agricultural Mechanization and Energy in Agriculture; ANKAgEng’2023 Eugenio Cavallo,Fernando Auat Cheein,Pur

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https://doi.org/10.1007/978-3-663-04330-0ween marine life, surface evaporation, and power and heat generation, 9-Wind Turbines, 10-Geothermal energy system, 11-District Energy System, 12-Fishery, Agriculture and animal farm, 13-Light industry, 14-Advanced greenhouse system, 15-Irrigation system, 16-Hydrogen mobility and agricultural mechan
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https://doi.org/10.1007/978-3-663-04380-5es remarkable accuracy, attaining a perfect 1.0000 on both validation and test sets. Additionally, this model demonstrates significantly lower loss values throughout the training phases, highlighting its enhanced predictive capabilities. Impressively, convergence is reached with fewer epochs and in
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https://doi.org/10.1007/978-3-663-04458-1ng pre-trained VGG16 and Inception-V3 models, along with a proposed Convolutional Neural Network (CNN) model. The dataset used in the study was divided into three subsets, with 70% allocated for training, 15% for validation, and 15% for testing. Specifically, 1575 images were used for training, and
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2366-2557 f exciting ideas that will open novel research directions andfoster multidisciplinary collaboration among different specialists..978-3-031-51581-1978-3-031-51579-8Series ISSN 2366-2557 Series E-ISSN 2366-2565
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Determination of the Effect of the Technical Parameters Which Affect the Tractor Energy Efficiencyh chapter. .Here one will also find large number of software that supplements this book in the form of MATLAB script (.m files). The name of the files used for the solution of a problem are indicated at the end978-3-030-21082-3978-3-030-21080-9
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Coefficient of Dynamic Wall Friction for Hardwood Fuel Pellets978-3-662-52919-5
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15th International Congress on Agricultural Mechanization and Energy in AgricultureANKAgEng’2023
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