A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System Data
In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system f...
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| Veröffentlicht in: | 2021 International Conference on Computational Science and Computational Intelligence (CSCI) S. 285 - 289 |
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01.12.2021
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| Abstract | In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways. |
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| AbstractList | In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways. |
| Author | Kwembe, Tor A. Manyfield-Donald, Cheronika Cheng, Jing-Ru C. |
| Author_xml | – sequence: 1 givenname: Cheronika surname: Manyfield-Donald fullname: Manyfield-Donald, Cheronika email: cheronika.n.manyfield@students.jsums.edu organization: Jackson State University,Computational Data-Enabled Science and Engineering,Jackson,MS,USA,39217 – sequence: 2 givenname: Tor A. surname: Kwembe fullname: Kwembe, Tor A. email: tor.a.kwembe@jsums.edu organization: Jackson State University,Department of Mathematics and Statistical Sciences,Jackson,MS,USA,39217 – sequence: 3 givenname: Jing-Ru C. surname: Cheng fullname: Cheng, Jing-Ru C. email: ruth.c.cheng@erdc.dren.mil organization: Engineer Research and Development Center U.S. Army Corps of Engineers,Information Technology Lab.,Vicksburg,MS,USA,39180 |
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| Snippet | In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of... |
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| SubjectTerms | Automatic Identification System Big Data Clustering algorithms Clustering methods Clustering Using Representatives (CURE) Data collection Machine learning Machine learning algorithms Scientific computing Tracking |
| Title | A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System Data |
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