Feature-aware emergency decision recommendation with multi-graph convolutional networks

Yao Shi

doi:10.1117/12.3035238

7 August 2024 Feature-aware emergency decision recommendation with multi-graph convolutional networks

Yao Shi

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 1322411 (2024) https://doi.org/10.1117/12.3035238
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

Emergency decision-making for unexpected events at civil transportation airports involves dynamic situations, time constraints and information scarcity. Rapid and effective post-emergency decision implementation remains a critical concern for relevant departments and emergency academia. With the development of graph neural networks, their application in recommendation systems has become a hot research topic. To improve recommendation quality, more and more researchers model data as an information network with two types of nodes: users and items. This approach facilitates more accurate knowledge discovery. However, existing studies often do not fully utilize the comprehensive structural and rich semantic information within the network. Therefore, this paper proposes an emergency recommendation model incorporating multiple types of entity-present learning networks to alleviate the pressure on commanders in responding to accidents. The experimental dataset was obtained on a simulation platform, and the experimental results show that our method has made significant improvements compared to advanced recommendation methods. Further research has demonstrated the effectiveness of the emergency decision-making method approach this paper proposed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yao Shi "Feature-aware emergency decision recommendation with multi-graph convolutional networks", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 1322411 (7 August 2024); https://doi.org/10.1117/12.3035238

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Emergency preparedness

Convolution

Neural networks

Decision making

Matrices

Convolutional neural networks

RELATED CONTENT

Dynamic graph convolution recurrent neural network for traffic flow prediction
Proceedings of SPIE (November 08 2023)

Research on multilingual sentiment analysis based on N Gram and...
Proceedings of SPIE (November 08 2023)

Instance-based adaptive attention algorithm for human-object interaction detection
Proceedings of SPIE (April 28 2023)

Deep learning based traffic flow prediction model on highway research
Proceedings of SPIE (February 20 2024)

Spatial interpolation of surface ozone observations using deep learning
Proceedings of SPIE (September 27 2018)

Deep learning fault diagnosis method for rolling bearings in rolling...
Proceedings of SPIE (October 10 2023)

Research on relationship extraction model for Xizang script named entities...
Proceedings of SPIE (May 06 2024)

Subscribe to Digital Library

Receive Erratum Email Alert

Keywords/Phrases

Search In:

Publication Years