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In contrast, artificial neural networks (ANNs) are computationally expensive to train (off-line), but they provide the fastest result under testing conditions (on-line) while remaining reasonably accurate. When access to data is limited, support vector machines (SVMs) can perform well even with small training sample sizes, while other algorithms show considerable reduction in accuracy if data is scarce, hence, setting a lower limit on the size of required training data. We also show by tracking and modeling the long-term drifts of the detector performance over a one year time-frame, it is possible to dramatically improve the predictive accuracy without any re-calibration. Our research shows for the first time that if the ML algorithm is chosen specific to the use-case, low-cost solution-processed cyber-nanomaterial detectors can be practically implemented under diverse operational requirements, despite their inherent variabilities.<\/jats:p>","DOI":"10.1088\/2632-2153\/ab8967","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T22:21:08Z","timestamp":1586989268000},"page":"025007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Development of use-specific high-performance cyber-nanomaterial optical detectors by effective choice of machine learning algorithms"],"prefix":"10.1088","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-6395","authenticated-orcid":false,"given":"Davoud","family":"Hejazi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2717-5789","authenticated-orcid":false,"given":"Shuangjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3766-2310","authenticated-orcid":false,"given":"Amirreza","family":"Farnoosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2216-9988","authenticated-orcid":false,"given":"Sarah","family":"Ostadabbas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-7082","authenticated-orcid":false,"given":"Swastik","family":"Kar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"key":"mlstab8967bib1","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1146\/annurev.bioeng.5.011303.120723","article-title":"Engineered nanomaterials for biophotonics applications: improving sensing, imaging and therapeutics","volume":"5","author":"West","year":"2003","journal-title":"Ann. 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