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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.03358 |
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| _version_ | 1866929661501505536 |
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| author | Kiersztyn, Adam Czerwiński, Dariusz Oniszczuk-Jastrzabek, Aneta Czermański, Ernest Rzepka, Agnieszka |
| author_facet | Kiersztyn, Adam Czerwiński, Dariusz Oniszczuk-Jastrzabek, Aneta Czermański, Ernest Rzepka, Agnieszka |
| contents | Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents er-ror detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data integrity vs. inference accuracy in large AIS datasets Kiersztyn, Adam Czerwiński, Dariusz Oniszczuk-Jastrzabek, Aneta Czermański, Ernest Rzepka, Agnieszka Cryptography and Security Machine Learning Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents er-ror detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea. |
| title | Data integrity vs. inference accuracy in large AIS datasets |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2501.03358 |