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Integration of a Self-Organizing Map and a Virtual Pheromone for Real Time Abnormal Movement Detection in Marine Traffic

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236462" target="_blank" >RIV/61989100:27240/17:10236462 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989100:27740/17:10236462

  • Result on the web

    <a href="https://www.mii.lt/informatica/htm/INFO1145.htm" target="_blank" >https://www.mii.lt/informatica/htm/INFO1145.htm</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.15388/Informatica.2017.133" target="_blank" >10.15388/Informatica.2017.133</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Integration of a Self-Organizing Map and a Virtual Pheromone for Real Time Abnormal Movement Detection in Marine Traffic

  • Original language description

    In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipeda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LM2015070" target="_blank" >LM2015070: IT4Innovations National Supercomputing Center</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Informatica

  • ISSN

    0868-4952

  • e-ISSN

  • Volume of the periodical

    28

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    LT - LITHUANIA

  • Number of pages

    16

  • Pages from-to

    359-374

  • UT code for WoS article

    000405641900007

  • EID of the result in the Scopus database

    2-s2.0-85031682286