AeroNeuro-GlobalNet
In the pursuit of enhanced transport safety, the integration of advanced Low Earth Orbit (LEO) satellite constellations with next-generation 5G and 6G networks presents a transformative solution for real-time monitoring of emotional states in high-risk transport operators, including truck drivers an...
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2025
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| Online Access: | http://hdl.handle.net/11675/14470 https: |
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| _version_ | 1870679719967457282 |
|---|---|
| author | Bostani, Ali |
| author2 | Albousabih, Batool Kalloush, Fahad |
| author2_role | author author |
| author_facet | Bostani, Ali Albousabih, Batool Kalloush, Fahad |
| author_role | author |
| dc.creator.none.fl_str_mv | Bostani, Ali Albousabih, Batool Kalloush, Fahad |
| dc.date.none.fl_str_mv | 2025-01-14 2026-06-03T10:09:30Z 2026-06-03T10:09:30Z |
| dc.identifier.none.fl_str_mv | 10.1109/GCAIOT63427.2024.10833530 9798331529413 http://hdl.handle.net/11675/14470 https: www.scopus.com/pages/publications/85217274878 |
| dc.relation.none.fl_str_mv | Electrical and Computer Engineering |
| dc.title.none.fl_str_mv | AeroNeuro-GlobalNet |
| dc.type.none.fl_str_mv | Conference Paper info:eu-repo/semantics/publishedVersion |
| description | In the pursuit of enhanced transport safety, the integration of advanced Low Earth Orbit (LEO) satellite constellations with next-generation 5G and 6G networks presents a transformative solution for real-time monitoring of emotional states in high-risk transport operators, including truck drivers and airplane pilots. This study introduces AeroNeuro-GlobalNet, a pioneering approach that leverages deep learning architectures, specifically a multi-head attention based long short-term memory (MHA-LSTM) model, to process and analyze EEG signals for emotion detection. The system aims to identify critical emotional states that could compromise safety, such as stress or fatigue, thus providing a novel form of preventive safety measure in the transportation sector. Utilizing the ubiquitous coverage and high-speed capabilities of integrated LEO satellite and terrestrial networks, AeroNeuro-GlobalNet ensures consistent, global monitoring capabilities, crucial for applications where traditional communication systems falter, such as remote air routes and cross-country trucking paths. This paper outlines the development and implementation of the emotion detection system, addresses the challenges of real-time data processing and privacy concerns, and discusses the system's integration with existing transport communication infrastructures. By facilitating continuous monitoring and immediate response capabilities, AeroNeuro-GlobalNet aims to prevent potential accidents and enhance the overall safety of transport operations, reflecting a significant step forward in the application of AI and satellite technology in critical real-world applications. The proposed framework not only enhances transport safety but also sets a foundation for future research in the integration of biometric monitoring technologies with global network infrastructures, offering a scalable solution to a wide array of safety-critical applications in various sectors. |
| id | AUKR_f884597f3e23b5e77edf2d8839b04d9c |
| identifier_str_mv | 10.1109/GCAIOT63427.2024.10833530 9798331529413 www.scopus.com/pages/publications/85217274878 |
| network_acronym_str | AUKR |
| network_name_str | AU Kuwait Rep |
| oai_identifier_str | oai:dspace.auk.edu.kw:11675/14470 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | AeroNeuro-GlobalNetBostani, AliAlbousabih, BatoolKalloush, FahadIn the pursuit of enhanced transport safety, the integration of advanced Low Earth Orbit (LEO) satellite constellations with next-generation 5G and 6G networks presents a transformative solution for real-time monitoring of emotional states in high-risk transport operators, including truck drivers and airplane pilots. This study introduces AeroNeuro-GlobalNet, a pioneering approach that leverages deep learning architectures, specifically a multi-head attention based long short-term memory (MHA-LSTM) model, to process and analyze EEG signals for emotion detection. The system aims to identify critical emotional states that could compromise safety, such as stress or fatigue, thus providing a novel form of preventive safety measure in the transportation sector. Utilizing the ubiquitous coverage and high-speed capabilities of integrated LEO satellite and terrestrial networks, AeroNeuro-GlobalNet ensures consistent, global monitoring capabilities, crucial for applications where traditional communication systems falter, such as remote air routes and cross-country trucking paths. This paper outlines the development and implementation of the emotion detection system, addresses the challenges of real-time data processing and privacy concerns, and discusses the system's integration with existing transport communication infrastructures. By facilitating continuous monitoring and immediate response capabilities, AeroNeuro-GlobalNet aims to prevent potential accidents and enhance the overall safety of transport operations, reflecting a significant step forward in the application of AI and satellite technology in critical real-world applications. The proposed framework not only enhances transport safety but also sets a foundation for future research in the integration of biometric monitoring technologies with global network infrastructures, offering a scalable solution to a wide array of safety-critical applications in various sectors.2026-06-03T10:09:30Z2026-06-03T10:09:30Z2025-01-14Conference Paperinfo:eu-repo/semantics/publishedVersion10.1109/GCAIOT63427.2024.108335309798331529413http://hdl.handle.net/11675/14470https:www.scopus.com/pages/publications/85217274878Electrical and Computer Engineeringoai:dspace.auk.edu.kw:11675/144702026-06-11T11:56:52Z |
| spellingShingle | AeroNeuro-GlobalNet Bostani, Ali |
| status_str | publishedVersion |
| title | AeroNeuro-GlobalNet |
| title_full | AeroNeuro-GlobalNet |
| title_fullStr | AeroNeuro-GlobalNet |
| title_full_unstemmed | AeroNeuro-GlobalNet |
| title_short | AeroNeuro-GlobalNet |
| title_sort | AeroNeuro-GlobalNet |
| url | http://hdl.handle.net/11675/14470 https: |