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National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Weather Estimation Based on Images of Clouds
 Kukaň, Tomáš ; Goldmann, Tomáš (referee) ; Orság, Filip (advisor)
The main purpose of this thesis is a creation of a simple mobile application that would be able to give weather predictions based on a cloud photo through the usage of convolu- tional neural networks. I have analyzed all types of clouds and joined them with weather prediction. Then there are the results of experiments with different neural networks archi- tectures and different datasets. In the end of this thesis I have described the creation of the Android application as well as the problems I had to solve.
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Weather Estimation Based on Images of Clouds
 Kukaň, Tomáš ; Goldmann, Tomáš (referee) ; Orság, Filip (advisor)
The main purpose of this thesis is a creation of a simple mobile application that would be able to give weather predictions based on a cloud photo through the usage of convolu- tional neural networks. I have analyzed all types of clouds and joined them with weather prediction. Then there are the results of experiments with different neural networks archi- tectures and different datasets. In the end of this thesis I have described the creation of the Android application as well as the problems I had to solve.
Detailed record
Vytvoření predikčního modelu předpovědi počasí pomocí neuronové sítě a asociačních pravidel
 Kadlec, Jakub ; Rauch, Jan (advisor) ; Berka, Petr (referee)
This diploma thesis introduces three different methods of creating a neural network binary classifier for the purpose of automated weather prediction with attribute pre-selection using association rules and correlation patters mining by the LISp-Miner system. First part of the thesis consists of collection of theoretical knowledge enabling the creation of such predictive model, whereas the second part describes the creation of the model itself using the CRISP-DM methodology. Final part of the thesis analyses the performance of created classifiers and concludes the proposed methods and their possible benefits over training the network without attribute pre-selection.
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