Detecting fraud via statistical anomalies
Urban planners and researchers are increasingly integrating mobility data in designing smarter and sustainable cities. It is therefore crucial to identify any anomalies in the dataset to prevent poor planning or statistical interferences. Such mobility data could come from public sources or data b...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175633 |