Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments
Abstract Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to mo...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-02-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00558-9 |
| _version_ | 1827326196980908032 |
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| author | Yue Pan Andia Foroughi |
| author_facet | Yue Pan Andia Foroughi |
| author_sort | Yue Pan |
| collection | DOAJ |
| description | Abstract Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact of their learning environment on their interaction and behavior. Different methods have been used to diagnose ASD in the past, each with their own strengths and weaknesses. Research into ASD diagnostics has largely focused on machine learning algorithms and strategies rather than diagnostic methods. This article discusses many diagnostic techniques used in the ASD literature, such as neuroimaging, speech recordings, facial features, and EEG signals. This has led us to conclude that in schools and educational settings, autism can be diagnosed cheaply, quickly, and accurately through face analysis. To facilitate and speed up the processing of facial information among children in educational settings, we applied the AlexNet architecture designed for edge computing. A fast method for detecting autism spectrum disorders from the face can be applied to educational settings using this structure. While we have investigated a variety of methods, the face can provide us with appropriate information about the disorder. In addition, it can produce more interpretive features. In order to help students in schools who are suffering from this disease, key factors must be considered: potential clinical and therapeutic situations, efficiency, predictability, privacy protection, accuracy, cost-effectiveness, and lack of methodological intervention. The diseases are troublesome, so they should be identified and treated. |
| first_indexed | 2024-03-07T14:41:05Z |
| format | Article |
| id | doaj.art-b565f2e21b7b40a0b727a35f8f218e8b |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-07T14:41:05Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-b565f2e21b7b40a0b727a35f8f218e8b2024-03-05T20:22:21ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-02-0113111210.1186/s13677-023-00558-9Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environmentsYue Pan0Andia Foroughi1Chengdu Sport UniversityDepartment of Biomedical Engineering, Central Tehran Branch, Islamic Azad UniversityAbstract Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact of their learning environment on their interaction and behavior. Different methods have been used to diagnose ASD in the past, each with their own strengths and weaknesses. Research into ASD diagnostics has largely focused on machine learning algorithms and strategies rather than diagnostic methods. This article discusses many diagnostic techniques used in the ASD literature, such as neuroimaging, speech recordings, facial features, and EEG signals. This has led us to conclude that in schools and educational settings, autism can be diagnosed cheaply, quickly, and accurately through face analysis. To facilitate and speed up the processing of facial information among children in educational settings, we applied the AlexNet architecture designed for edge computing. A fast method for detecting autism spectrum disorders from the face can be applied to educational settings using this structure. While we have investigated a variety of methods, the face can provide us with appropriate information about the disorder. In addition, it can produce more interpretive features. In order to help students in schools who are suffering from this disease, key factors must be considered: potential clinical and therapeutic situations, efficiency, predictability, privacy protection, accuracy, cost-effectiveness, and lack of methodological intervention. The diseases are troublesome, so they should be identified and treated.https://doi.org/10.1186/s13677-023-00558-9Autism spectrum disorderEducational environmentsEdge computingSpeech signalsEEG recordingsFacial features |
| spellingShingle | Yue Pan Andia Foroughi Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments Journal of Cloud Computing: Advances, Systems and Applications Autism spectrum disorder Educational environments Edge computing Speech signals EEG recordings Facial features |
| title | Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments |
| title_full | Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments |
| title_fullStr | Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments |
| title_full_unstemmed | Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments |
| title_short | Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments |
| title_sort | evaluation of ai tools for healthcare networks at the cloud edge interaction to diagnose autism in educational environments |
| topic | Autism spectrum disorder Educational environments Edge computing Speech signals EEG recordings Facial features |
| url | https://doi.org/10.1186/s13677-023-00558-9 |
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