{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:17:48Z","timestamp":1774628268891,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T00:00:00Z","timestamp":1603670400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,10,26]]},"abstract":"<jats:p>In the paper, the flight time deviation of Lithuania airports has been analyzed. The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights. The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines. To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used. To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity\/recall, precision, specificity, F-measure, and accuracy. All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure\/landing time. The departure flights and arrival flights have been investigated separately. To balance the dataset, the SMOTE technique is used. The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.<\/jats:p>","DOI":"10.1155\/2020\/8878681","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T21:05:07Z","timestamp":1603832707000},"page":"1-10","source":"Crossref","is-referenced-by-count":18,"title":["Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0503-4157","authenticated-orcid":true,"given":"Pavel","family":"Stefanovi\u010d","sequence":"first","affiliation":[{"name":"Faculty of Fundamental Science, Vilnius Gediminas Technical University, Saul\u0117tekio al. 11, LT-10223 Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9884-7327","authenticated-orcid":true,"given":"Rokas","family":"\u0160trimaitis","sequence":"additional","affiliation":[{"name":"Faculty of Fundamental Science, Vilnius Gediminas Technical University, Saul\u0117tekio al. 11, LT-10223 Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0570-1741","authenticated-orcid":true,"given":"Olga","family":"Kurasova","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08663 Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2957874"},{"key":"2"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/3525912"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/DASC.2016.7777956"},{"key":"5"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.11118\/actaun201765051799"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/87.251882"},{"key":"8"},{"key":"9","first-page":"249","article-title":"Supervised machine learning: a review of classification techniques","volume":"31","author":"S. 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