@Article{Vrban2026,
author={Vrban, Branislav
and Vrtalov{\'a}, Vendula
and Kne{\v{z}}evi{\'{c}}, Du{\v{s}}ica
and Savi{\'{c}}, Milo{\v{s}}
and Kurbalija, Vladimir
and Jakoveti{\'{c}}, Du{\v{s}}an
and {\v{C}}erba, {\v{S}}tefan
and L{\"u}ley, Jakub},
title={The application of machine learning techniques to data from solid-state nuclear track detector CR-39},
journal={The European Physical Journal Special Topics},
year={2026},
month={May},
day={09},
abstract={The study presents an application of classification-based machine learning techniques to a dataset comprising the radiation dose measurements with solid-state nuclear track detectors of poly(allyl diglycol carbonate) type. The detectors were irradiated with alpha particles and fast neutrons in various experimental configurations making the final dataset complex and suitable for machine learning methods. The most suitable experiment is chosen as a stepping stone, and the proposed evaluation method is tested. The detectors are analysed with the commercially available TASLImage system and the final performance in dose determination is compared to the machine learning efforts. Moreover, the uncertainty quantification algorithm is applied to better judge the future applicability of the new evaluation method.},
issn={1951-6401},
doi={10.1140/epjs/s11734-026-02349-0},
url={https://doi.org/10.1140/epjs/s11734-026-02349-0}
}

{"_id":"CyEsHFYnGnYQ7sAYk","bibbaseid":"vrban-vrtalov-kneevi-savi-kurbalija-jakoveti-erba-lley-theapplicationofmachinelearningtechniquestodatafromsolidstatenucleartrackdetectorcr39-2026","author_short":["Vrban, B.","Vrtalová, V.","Knežević, D.","Savić, M.","Kurbalija, V.","Jakovetić, D.","Čerba, Š.","Lüley, J."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Vrban"],"firstnames":["Branislav"],"suffixes":[]},{"propositions":[],"lastnames":["Vrtalová"],"firstnames":["Vendula"],"suffixes":[]},{"propositions":[],"lastnames":["Knežević"],"firstnames":["Dušica"],"suffixes":[]},{"propositions":[],"lastnames":["Savić"],"firstnames":["Miloš"],"suffixes":[]},{"propositions":[],"lastnames":["Kurbalija"],"firstnames":["Vladimir"],"suffixes":[]},{"propositions":[],"lastnames":["Jakovetić"],"firstnames":["Dušan"],"suffixes":[]},{"propositions":[],"lastnames":["Čerba"],"firstnames":["Štefan"],"suffixes":[]},{"propositions":[],"lastnames":["Lüley"],"firstnames":["Jakub"],"suffixes":[]}],"title":"The application of machine learning techniques to data from solid-state nuclear track detector CR-39","journal":"The European Physical Journal Special Topics","year":"2026","month":"May","day":"09","abstract":"The study presents an application of classification-based machine learning techniques to a dataset comprising the radiation dose measurements with solid-state nuclear track detectors of poly(allyl diglycol carbonate) type. The detectors were irradiated with alpha particles and fast neutrons in various experimental configurations making the final dataset complex and suitable for machine learning methods. The most suitable experiment is chosen as a stepping stone, and the proposed evaluation method is tested. The detectors are analysed with the commercially available TASLImage system and the final performance in dose determination is compared to the machine learning efforts. Moreover, the uncertainty quantification algorithm is applied to better judge the future applicability of the new evaluation method.","issn":"1951-6401","doi":"10.1140/epjs/s11734-026-02349-0","url":"https://doi.org/10.1140/epjs/s11734-026-02349-0","bibtex":"@Article{Vrban2026,\nauthor={Vrban, Branislav\nand Vrtalov{\\'a}, Vendula\nand Kne{\\v{z}}evi{\\'{c}}, Du{\\v{s}}ica\nand Savi{\\'{c}}, Milo{\\v{s}}\nand Kurbalija, Vladimir\nand Jakoveti{\\'{c}}, Du{\\v{s}}an\nand {\\v{C}}erba, {\\v{S}}tefan\nand L{\\\"u}ley, Jakub},\ntitle={The application of machine learning techniques to data from solid-state nuclear track detector CR-39},\njournal={The European Physical Journal Special Topics},\nyear={2026},\nmonth={May},\nday={09},\nabstract={The study presents an application of classification-based machine learning techniques to a dataset comprising the radiation dose measurements with solid-state nuclear track detectors of poly(allyl diglycol carbonate) type. The detectors were irradiated with alpha particles and fast neutrons in various experimental configurations making the final dataset complex and suitable for machine learning methods. The most suitable experiment is chosen as a stepping stone, and the proposed evaluation method is tested. The detectors are analysed with the commercially available TASLImage system and the final performance in dose determination is compared to the machine learning efforts. Moreover, the uncertainty quantification algorithm is applied to better judge the future applicability of the new evaluation method.},\nissn={1951-6401},\ndoi={10.1140/epjs/s11734-026-02349-0},\nurl={https://doi.org/10.1140/epjs/s11734-026-02349-0}\n}\n","author_short":["Vrban, B.","Vrtalová, V.","Knežević, D.","Savić, M.","Kurbalija, V.","Jakovetić, D.","Čerba, Š.","Lüley, J."],"key":"Vrban2026","id":"Vrban2026","bibbaseid":"vrban-vrtalov-kneevi-savi-kurbalija-jakoveti-erba-lley-theapplicationofmachinelearningtechniquestodatafromsolidstatenucleartrackdetectorcr39-2026","role":"author","urls":{"Paper":"https://doi.org/10.1140/epjs/s11734-026-02349-0"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/f/JhX5bDPAMcYz6bs8j/uploaded.bib","dataSources":["K7G9JM9tE4sj6hDYC"],"keywords":[],"search_terms":["application","machine","learning","techniques","data","solid","state","nuclear","track","detector","vrban","vrtalová","knežević","savić","kurbalija","jakovetić","čerba","lüley"],"title":"The application of machine learning techniques to data from solid-state nuclear track detector CR-39","year":2026}