Developing a Framework for Leveraging Knowledge Graph & Deep Learning in Mobility Transportation
Facts
- Contact person:
- Reza Khoshkangini
- Financer:
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- STINT
- Responsible at MaU:
- Reza Khoshkangini
- External project members:
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- Ramin Sahba
- Amin Sahba
- Enayat Rajabi
- Collaborators :
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- Texas University at San Antonio in the US and Cape Breton University in Canada
- Time frame:
- 31 July 2023 - 01 August 2025
- Faculty/department:
- Research environment :
- Research subject:
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- Computer Science
- AI and ML
About the project
The project aim is to strengthen collaboration between Malmö University (MAU) in Sweden, Cape Breton University (CBU) in Canada, and the University of Texas at San Antonio (UTSA) in the USA to investigate the challenges and opportunities to develop a knowledge graph-based deep learning framework to identify vehicle faults/breakdowns beforehand and explain the relationship between vehicle usage and failures. Knowledge graphs (KGs) are a well-known semantic approach that represents a network of real-world entities such as events, objectives, and context and demonstrates their affinity between them. The innovative intelligence of knowledge graphs can be applied to heavy vehicles to reduce or eliminate equipment failures, enhance productivity, improve driver safety, and reduce costs.