整合智能情绪识别功能优化电子学习平台的效用

Layth Mohammed, Abbas Al-Mashhadani, Isam A. Alobaidi, Alaa Mohammed
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引用次数: 0

摘要

由于数据收集工具的发展,图像处理的教育应用已经出现。教育是人类生活中至关重要的领域,需要高度精确的表现。将图像处理和深度学习与教育相结合,将有助于优化整个系统的性能。现在可以通过研究一组学生的面部图像特征来判断学生的情绪状态。通过提供类似于常规教室环境的设施,减少了教育的时间和成本。这可能会帮助许多由于无法承受的成本而无法获得正规教育设施的人。本文采用神经网络进行情绪自动检测。采用人工神经网络和CNN神经网络两种模型。这些模型使用情感图像数据进行测试。结果表明,人工神经网络和CNN的准确率分别为96.7%和99.2%。
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Optimization The Utility Of E-Learning Platform Through Integrating Smart Emotional Recognition Feature
Educational applications of image processing have emerged due to data collection tools development. Education is vital field in human life where highly accurate performance is required. Integrating image processing and deep learning with the education will help to optimize the performance of entire system. It is possible now to make out the student’s emotional status through study the features from facial images taken for a group of students. That reduces the time and cost of the education by providing a facility similar to the regular classrooms environments.  Which may help plenty of people who are unable to access regular educational facilities due to intolerable cost. In this paper, automatic emotional detection is being performed using neural network. Two models are used namely artificial neural network and CNN neural network. The models are tested using emotional images data. Results are reported 96.7 % and 99.2 % accuracies from bother artificial neural network and CNN respectively.
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