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- W4386337620 abstract "Machine learning is growing every day, with improvements in existing algorithms, thus making them more applicable for real life scenarios. It has made huge impact on everyday life that the majority of mobile applications have some kind of machine algorithm integrated in their structure. Convolutional neural networks are a great tool for image processing, and in this work we developed the model for emotion detection. Every machine learning algorithm requires a dataset in order to be trained and validated. For this specific case, we utilized publicly available FER-2013 dataset that contains seven emotions: angry, disgust, fear, happy, neutral, sad and surprise. Ratio between train and validation images is 80:20, respectively. For model optimization we proposed Adam optimizer, alongside other techniques for preventing overfitting during training and saving the best weights. Model achieved the training accuracy of 71.55%, and the validation accuracy of 61.4%. We utilized NVIDIA GeFroce MX350 GPU to train and validate the model, resulting in much shorter training time, which was cca 20 min." @default.
- W4386337620 created "2023-09-01" @default.
- W4386337620 creator A5092726743 @default.
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- W4386337620 date "2023-01-01" @default.
- W4386337620 modified "2023-09-30" @default.
- W4386337620 title "Emotion Detection Using Convolutional Neural Networks" @default.
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- W4386337620 doi "https://doi.org/10.1007/978-3-031-43056-5_21" @default.
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