An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip
It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and...
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ftmdpi:oai:mdpi.com:/1999-4893/15/5/150/ 2023-08-20T04:07:01+02:00 An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip Janith Kodithuwakku Dilki Dandeniya Arachchi Jay Rajasekera 2022-04-27 application/pdf https://doi.org/10.3390/a15050150 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/a15050150 https://creativecommons.org/licenses/by/4.0/ Algorithms; Volume 15; Issue 5; Pages: 150 artificial intelligence (AI) convolutional neural network (CNN) facial emotion recognition (FER) support vector machine (SVM) Text 2022 ftmdpi https://doi.org/10.3390/a15050150 2023-08-01T04:53:10Z It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and support vector machine (SVM)-based machine learning models to classify the emotional states and the attention level of the participants to a video conversation. This application visualizes their attention and emotion analytics in a meaningful manner. This proposed system provides an artificial intelligence (AI)-powered analytics system with optimized machine learning models to monitor the audience and prepare insightful reports on the basis of participants’ facial features throughout the video conversation. One of the main objectives of this research is to utilize the neural accelerator chip to enhance emotion and attention detection tasks. A custom CNN developed by Gyrfalcon Technology Inc (GTI) named GnetDet was used in this system to run the trained model on their GTI Lightspeeur 2803 neural accelerator chip. Text gyrfalcon MDPI Open Access Publishing Algorithms 15 5 150 |
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MDPI Open Access Publishing |
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language |
English |
topic |
artificial intelligence (AI) convolutional neural network (CNN) facial emotion recognition (FER) support vector machine (SVM) |
spellingShingle |
artificial intelligence (AI) convolutional neural network (CNN) facial emotion recognition (FER) support vector machine (SVM) Janith Kodithuwakku Dilki Dandeniya Arachchi Jay Rajasekera An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip |
topic_facet |
artificial intelligence (AI) convolutional neural network (CNN) facial emotion recognition (FER) support vector machine (SVM) |
description |
It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and support vector machine (SVM)-based machine learning models to classify the emotional states and the attention level of the participants to a video conversation. This application visualizes their attention and emotion analytics in a meaningful manner. This proposed system provides an artificial intelligence (AI)-powered analytics system with optimized machine learning models to monitor the audience and prepare insightful reports on the basis of participants’ facial features throughout the video conversation. One of the main objectives of this research is to utilize the neural accelerator chip to enhance emotion and attention detection tasks. A custom CNN developed by Gyrfalcon Technology Inc (GTI) named GnetDet was used in this system to run the trained model on their GTI Lightspeeur 2803 neural accelerator chip. |
format |
Text |
author |
Janith Kodithuwakku Dilki Dandeniya Arachchi Jay Rajasekera |
author_facet |
Janith Kodithuwakku Dilki Dandeniya Arachchi Jay Rajasekera |
author_sort |
Janith Kodithuwakku |
title |
An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip |
title_short |
An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip |
title_full |
An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip |
title_fullStr |
An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip |
title_full_unstemmed |
An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip |
title_sort |
emotion and attention recognition system to classify the level of engagement to a video conversation by participants in real time using machine learning models and utilizing a neural accelerator chip |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/a15050150 |
genre |
gyrfalcon |
genre_facet |
gyrfalcon |
op_source |
Algorithms; Volume 15; Issue 5; Pages: 150 |
op_relation |
https://dx.doi.org/10.3390/a15050150 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/a15050150 |
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Algorithms |
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15 |
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150 |
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