{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:23:26Z","timestamp":1777584206699,"version":"3.51.4"},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2013,4,1]],"date-time":"2013-04-01T00:00:00Z","timestamp":1364774400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The potential of two Kohonen artificial neural networks I ANNs) - linear vector quantisa - tion (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n's) and gamma rays (\u03b3\u2019s). The effect that la) the energy level, and lb) the relative- of the training and lest sets, have on iden- tification accuracy is also evaluated on the given PSD datasel The two Kohonen ANNs demonstrate compfcmentary discrimination ability on the training and test sets: while the LVQ is consistently mote accurate on classifying the training set. the SOM exhibits higher n\/\u03b3 identification rales when classifying new paltms regardless of the proportion of training and test set patterns at the different energy levels: the average tint: for decision making equals\n                    <jats:sup>\u223c<\/jats:sup>\n                    100 \/e in the cax of the LVQ and\n                    <jats:sup>\u223c<\/jats:sup>\n                    450 \u03bcs in the case of the SOM.\n                  <\/jats:p>","DOI":"10.2478\/jaiscr-2014-0006","type":"journal-article","created":{"date-parts":[[2015,1,16]],"date-time":"2015-01-16T12:01:24Z","timestamp":1421409684000},"page":"77-88","source":"Crossref","is-referenced-by-count":25,"title":["Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks"],"prefix":"10.2478","volume":"3","author":[{"given":"Tatiana","family":"Tambouratzis","sequence":"first","affiliation":[{"name":"Department of Industrial Management & Technology, University of Piraeus, addressStreet107 Deligiorgi St., CityPiraeus 185 34, country-regionplaceGreece"}]},{"given":"Dina","family":"Chernikova","sequence":"additional","affiliation":[{"name":"Division of Nuclear Engineering, Chalmers University of Technology SE-412 96 CityplaceGothenburg, country-regionSweden"}]},{"given":"Imre","family":"Pzsit","sequence":"additional","affiliation":[{"name":"Division of Nuclear Engineering, Chalmers University of Technology SE-412 96 CityplaceGothenburg, country-regionSweden"}]}],"member":"374","published-online":{"date-parts":[[2014,12,30]]},"container-title":["Journal of Artificial Intelligence and Soft Computing Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/content.sciendo.com\/view\/journals\/jaiscr\/3\/2\/article-p77.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/jaiscr-2014-0006","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:53:50Z","timestamp":1777406030000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/jaiscr-2014-0006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,4,1]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2014,12,30]]},"published-print":{"date-parts":[[2013,4,1]]}},"alternative-id":["10.2478\/jaiscr-2014-0006"],"URL":"https:\/\/doi.org\/10.2478\/jaiscr-2014-0006","relation":{},"ISSN":["2083-2567"],"issn-type":[{"value":"2083-2567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,4,1]]}}}