{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T23:38:55Z","timestamp":1773013135176,"version":"3.50.1"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Cambridge University Nehru Trust Scholarship"},{"DOI":"10.13039\/100006785","name":"Google Europe Scholarship","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funds for Women Graduates"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3005457","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T21:49:10Z","timestamp":1593467350000},"page":"119654-119667","source":"Crossref","is-referenced-by-count":23,"title":["Gallium Nitride Power Electronic Devices Modeling Using Machine Learning"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4713-4829","authenticated-orcid":false,"given":"Nikita","family":"Hari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7300-506X","authenticated-orcid":false,"given":"Mominul","family":"Ahsan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1282-7796","authenticated-orcid":false,"given":"Sridhar","family":"Ramasamy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3212-2750","authenticated-orcid":false,"given":"Padmanaban","family":"Sanjeevikumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1484-8224","authenticated-orcid":false,"given":"Alhussein","family":"Albarbar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8311-7412","authenticated-orcid":false,"given":"Frede","family":"Blaabjerg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref31","first-page":"1","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/5.58323"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/45.329294"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2006.872926"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2016.2555948"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/mmce.20660"},{"key":"ref13","author":"zhang","year":"2000","journal-title":"Neural Networks for RF and Microwave Design"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JXCDC.2016.2636161"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2006.888683"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ECCE.2018.8557565"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2007.906994"},{"key":"ref18","author":"nielsen","year":"2015","journal-title":"Neural Networks and Deep Learning"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/AERO.2018.8396488"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/34.824819"},{"key":"ref4","article-title":"Gallium nitride power electronics using machine learning","author":"hari","year":"2019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1147\/rd.24.0336"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2014.2373373"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2008.925335"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1970.222832"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IWIPP.2015.7295986"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1049\/el.2016.2947"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IWIPP.2015.7295985"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2014.2368274"},{"key":"ref1","first-page":"1","article-title":"GaN transistors—Giving new life to Moore’s Law","author":"lidow","year":"2015","journal-title":"Proc IEEE 27th Int Symp Power Semiconductor Devices IC’s"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/22.739227"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JRPROC.1953.274273"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1962.1057751"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1147\/rd.21.0002"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/5254.895861"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JRPROC.1961.287775"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1147\/rd.33.0282"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/5.824006"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09127426.pdf?arnumber=9127426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:52:59Z","timestamp":1639770779000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9127426\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3005457","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}