Emotion Artificial Intelligence as improvement for e-Learning during COVID-19 (eBook, PDF) - K. C., Hari
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Academic Paper from the year 2020 in the subject Computer Sciences - Artificial Intelligence, grade: 10.00, , course: Electronics and Computer, language: English, abstract: In this paper student’s emotions such as excitement, happiness, confusion, sadness, desire and surprise, will be analysed by an Emotion AI. Therefore the neural network model, designed to capture the facial expression, is used. Deep learning is the emerging techniques to process large datasets of images with Kera’s using TensorFlow backend. Convolution Neural Network is an artificial neural network that has specialization…mehr

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Produktbeschreibung
Academic Paper from the year 2020 in the subject Computer Sciences - Artificial Intelligence, grade: 10.00, , course: Electronics and Computer, language: English, abstract: In this paper student’s emotions such as excitement, happiness, confusion, sadness, desire and surprise, will be analysed by an Emotion AI. Therefore the neural network model, designed to capture the facial expression, is used. Deep learning is the emerging techniques to process large datasets of images with Kera’s using TensorFlow backend. Convolution Neural Network is an artificial neural network that has specialization in detection and classification. Convolution neural network has hidden layers called convolution layers. This layer consists of neurons. Facial emotion recognition usually employs a training and testing stage to produce the desirable output. The emotion of the students plays the vital role to determine the student interest in attending classes. Facial expressions are among the most universal forms of body language. The facial expressions are almost similar throughout the world. The facial expression, movement of head, eye, mouth helps to identify the emotions of the students so that the level of interest of student can be predicted form the emotion analysis of students. For example: A smile can be used to indicate happiness. Facial expression reveals the true feelings about a situation. Then, after collecting those information, e-learning quality can be improved and enhanced. The reaction of the students is analyzed during the teaching and learning course. Thus, the mood of students can be predicted easily which help to improve the e - learning environment. The feedback will be provided to teachers to enhance the teaching and learning process in e-learning.
Autorenporträt
Asst. Professor, Pashchimanchal Campus, Institute of Engineering Tribhuvan University