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Learners mood detection using Convolutional Neural Network (CNN)

机译:使用卷积神经网络(CNN)进行学习者情绪检测

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This research concerns about classroom learners mood detection in learning process which is believed to be an important thing to increase learning process effectiveness. Convolutional Neural Network (CNN), a branch of deep learning architectures and a part of Machine Learning, was used as a method in this research. The experiments were conducted through several stages such as face detection, image improvement and model formation. There are 660 images used as training data and the classification process result showed a good result. The accuracy average result was considered as a good result by using 4 layers of CNN i.e. 2 convolutional layers and 2 subsampling layers. Based on the experiments result, the system needs to be developed further by adding more specific data class and training data.
机译:这项研究关注课堂学习者在学习过程中的情绪检测,这被认为是提高学习过程有效性的重要内容。卷积神经网络(CNN)是深度学习体系结构的一个分支,并且是机器学习的一部分,被用作本研究的一种方法。实验是通过几个阶段进行的,例如面部检测,图像改进和模型形成。有660张图像用作训练数据,分类处理结果显示出良好的效果。通过使用4层CNN,即2个卷积层和2个子采样层,可以将平均精度结果视为一个很好的结果。根据实验结果,需要通过添加更多特定的数据类别和训练数据来进一步开发该系统。

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