{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:57:22Z","timestamp":1781179042025,"version":"3.54.1"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:p>Fungal contamination on space vessels can impose great risk to the health of astronauts and\nthe durability of the vessels. The manual microscopic classification is both time-consuming\nand non-feasible in space missions, and thus it is essential to automate the process. The\nstudy involves the model development of three deep learning models, namely the baseline\nCNN, EfficientNetB0, and MobileNetV2, for the classification of five fungal species using\na secondary DeFungi microscopic image database. After image augmentation and common\npreprocessing, each model was tested in terms of precision, recall, F1-score, and ROC-AUC.\nFindings indicate that the MobileNetV2 model performed better than the other models, which\nregistered 67% test set accuracy and excellent performance across most classes. It is compact\nand lightweight and can make onboard fungal monitoring easy in resource-limited settings.\nIn this study, the research is used to indicate how to achieve autonomous bio-surveillance\nadvancement through the use of deep learning in long-duration space missions.<\/jats:p>","DOI":"10.54364\/aaiml.2026.63308","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:09:36Z","timestamp":1781176176000},"page":"01-20","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Models for Microscopic Fungal Identification in Spacecraft Applications"],"prefix":"10.54364","volume":"06","author":[{"given":"Wenzhen","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujian","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeanette","family":"Jones","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijiang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"32807","published-online":{"date-parts":[[2026]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/653363308.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:09:36Z","timestamp":1781176176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/653363308.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2026]]},"published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2026.63308","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}