{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T15:37:31Z","timestamp":1770737851375,"version":"3.49.0"},"reference-count":16,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,3,31]],"date-time":"2018-03-31T00:00:00Z","timestamp":1522454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>In this work, we address a multi-class classification task of oyster vessel behaviors determination by classifying them into four different classes: fishing, traveling, poling (exploring) and docked (anchored). The main purpose of this work is to automate the oyster vessel behaviors determination task using machine learning and to explore different techniques to improve the accuracy of the oyster vessel behavior prediction problem. To employ machine learning technique, two important descriptors: speed and net speed, are calculated from the trajectory data, recorded by a satellite communication system (Vessel Management System, VMS) attached to the vessels fishing on the public oyster grounds of Louisiana. We constructed a support vector machine (SVM) based method which employs Radial Basis Function (RBF) as a kernel to accurately predict the behavior of oyster vessels. Several validation and parameter optimization techniques were used to improve the accuracy of the SVM classifier. A total 93% of the trajectory data from a July 2013 to August 2014 dataset consisting of 612,700 samples for which the ground truth can be obtained using rule-based classifier is used for validation and independent testing of our method. The results show that the proposed SVM based method is able to correctly classify 99.99% of 612,700 samples using the 10-fold cross validation. Furthermore, we achieved a precision of 1.00, recall of 1.00, F1-score of 1.00 and a test accuracy of 99.99%, while performing an independent test using a subset of 93% of the dataset, which consists of 31,418 points.<\/jats:p>","DOI":"10.3390\/make1010004","type":"journal-article","created":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T12:32:20Z","timestamp":1522672340000},"page":"64-74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Machine Learning Approach to Determine Oyster Vessel Behavior"],"prefix":"10.3390","volume":"1","author":[{"given":"Devin Joseph","family":"Frey","sequence":"first","affiliation":[{"name":"Canizaro\/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA"}]},{"given":"Avdesh","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-2194","authenticated-orcid":false,"given":"Md Tamjidul","family":"Hoque","sequence":"additional","affiliation":[{"name":"Canizaro\/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA"}]},{"given":"Mahdi","family":"Abdelguerfi","sequence":"additional","affiliation":[{"name":"Canizaro\/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9666-8715","authenticated-orcid":false,"given":"Thomas","family":"Soniat","sequence":"additional","affiliation":[{"name":"Canizaro\/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Biological Sciences, University of New Orleans, New Orleans, LA 70148, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,31]]},"reference":[{"key":"ref_1","unstructured":"Louisiana Department of Wildlife and Fisheries (LDWF) (2017, December 12). 2014 Oyster Stock Assessment Report, Available online: http:\/\/www.wlf.louisiana.gov\/sites\/default\/files\/pdf\/page\/37756-stock-assessments\/2014oysterstockassessment.pdf."},{"key":"ref_2","unstructured":"Louisiana Department of Wildlife and Fisheries (LDWF) (2012). Louisiana Wildlife and Fisheries Commission Considers Establishing Vessel Monitoring for the Harvesting of Oysters on Public Seed Grounds, LDWF."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Witt, M.J., and Godley, B.J. (2007). A Step Towards Seascape Scale Conservation: Using Vessel Monitoring System (VMS) to Map Fishing Activity. PLoS ONE, 2.","DOI":"10.1371\/journal.pone.0001111"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1093\/icesjms\/fsq137","article-title":"Integrating Vessel Monitoring System (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution","volume":"68","author":"Gerritsen","year":"2010","journal-title":"ICES J. Mar. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1139\/cjfas-2013-0552","article-title":"Deriving high-resolution spatiotemporal fishing effort of large-scale longline fishery from vessel monitoring system (VMS) data and validated by observer data","volume":"71","author":"Chang","year":"2014","journal-title":"Can. J. Fish. Aquat. Sci."},{"key":"ref_6","unstructured":"Gallegos, D.X. (2014). A GIS-Centric Approach for Modeling Vessel Management Behavior System Data to Determine Oyster Vessel Behavior on Public Oyster Grounds in Louisiana. Computer Science, University of New Orleans. Available online: http:\/\/scholarworks.uno.edu\/td\/1918\/."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_8","unstructured":"Bishop, C.M. (2009). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_9","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2000). Pattern Classification, Wiley."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1080\/13658816.2012.692791","article-title":"Transportation mode-based segmentation and classification of movement trajectories","volume":"27","author":"Biljecki","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1111\/tgis.12181","article-title":"Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach","volume":"20","author":"Wan","year":"2016","journal-title":"Trans. GIS"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Palma, A.T., Bogorny, V., Kuijpers, B., and Alvares, L.O. (2008, January 16\u201320). A Clustering-based Approach for Discovering Interesting Places in Trajectories. 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Res."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:59:13Z","timestamp":1760194753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,31]]},"references-count":16,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["make1010004"],"URL":"https:\/\/doi.org\/10.3390\/make1010004","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,31]]}}}