{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:46:55Z","timestamp":1767084415734,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MESCTI","award":["011\/D-UL\/PDCT-M003\/2022","10.54499\/UIDB\/50022\/2020","10.54499\/UIDP\/50022\/2020","LA\/P\/0079\/2020"],"award-info":[{"award-number":["011\/D-UL\/PDCT-M003\/2022","10.54499\/UIDB\/50022\/2020","10.54499\/UIDP\/50022\/2020","LA\/P\/0079\/2020"]}]},{"name":"Laborat\u00f3rio Associado em Energia, Transportes e Aeroespacial (LAETA) Base Funding","award":["011\/D-UL\/PDCT-M003\/2022","10.54499\/UIDB\/50022\/2020","10.54499\/UIDP\/50022\/2020","LA\/P\/0079\/2020"],"award-info":[{"award-number":["011\/D-UL\/PDCT-M003\/2022","10.54499\/UIDB\/50022\/2020","10.54499\/UIDP\/50022\/2020","LA\/P\/0079\/2020"]}]},{"name":"LAETA Programmatic Funding","award":["011\/D-UL\/PDCT-M003\/2022","10.54499\/UIDB\/50022\/2020","10.54499\/UIDP\/50022\/2020","LA\/P\/0079\/2020"],"award-info":[{"award-number":["011\/D-UL\/PDCT-M003\/2022","10.54499\/UIDB\/50022\/2020","10.54499\/UIDP\/50022\/2020","LA\/P\/0079\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Deep learning (DL) and machine learning (ML) models have been successfully applied across multiple domains, but generic architectures often underperform without domain-specific adaptation. This study presents A-BERT, a BERT-based model fine-tuned on a dataset of aviation and aircraft-related academic publications, enabling accurate classification into 14 thematic categories. The temporal evolution of publication counts in each category was then modeled using ARIMA to forecast future research trends in the aviation sector. As a proof of concept, A-BERT outperformed the baseline BERT in several key metrics, offering a reliable approach for large-scale, domain-specific literature classification. Forecast validation through walk-forward testing across multiple time windows yielded Root Mean Square Error (RMSE) values below 2% for all categories, confirming high predictive reliability within this controlled setting. While the framework demonstrates the potential of combining domain-specific text classification with validated time series forecasting, its extension to operational aviation datasets will require further adaptation and external validation.<\/jats:p>","DOI":"10.3390\/app15179403","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T10:29:18Z","timestamp":1756376958000},"page":"9403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence and Aviation: A Deep Learning Strategy for Improved Data Classification and Management"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1429-4414","authenticated-orcid":false,"given":"Fl\u00e1vio L.","family":"L\u00e1zaro","sequence":"first","affiliation":[{"name":"Institute of Mechanical Engineering (IDMEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Faculdade de Engenharia, Universidade Agostinho Neto, Av. 21 de Janeiro, Luanda 1756, Angola"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7169-2660","authenticated-orcid":false,"given":"Lu\u00eds F. F. M.","family":"Santos","sequence":"additional","affiliation":[{"name":"ISEC Lisboa, Alameda das Linhas de Torres, 179, 1750-142 Lisboa, Portugal"},{"name":"Aeronautics and Astronautics Research Center (AEROG), Universidade da Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6200-358 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9388-4308","authenticated-orcid":false,"given":"Duarte","family":"Val\u00e9rio","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Engineering (IDMEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1081-2729","authenticated-orcid":false,"given":"Rui","family":"Melicio","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Engineering (IDMEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Aeronautics and Astronautics Research Center (AEROG), Universidade da Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6200-358 Covilh\u00e3, Portugal"},{"name":"Synopsis Planet, Advance Engineering Unipessoal LDA, Faculdade de Ci\u00eancias, Universidade de Lisboa, Campo Grande 16, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1109\/TEM.2021.3089438","article-title":"Applying systems modeling language in an aviation maintenance system","volume":"69","author":"Fatine","year":"2022","journal-title":"IEEE Trans. Eng. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Madeira, T., Melicio, R., Val\u00e9rio, D., and Santos, L. (2021). Machine learning and natural language processing for prediction of human factors in aviation incident reports. 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