{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:18:59Z","timestamp":1776277139433,"version":"3.50.1"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["957406 (TERMINET)"],"award-info":[{"award-number":["957406 (TERMINET)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3059589","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T04:22:45Z","timestamp":1613535765000},"page":"30441-30451","source":"Crossref","is-referenced-by-count":57,"title":["Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3557-9211","authenticated-orcid":false,"given":"Sotirios P.","family":"Sotiroudis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6042-0355","authenticated-orcid":false,"given":"Panagiotis","family":"Sarigiannidis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5981-5683","authenticated-orcid":false,"given":"Sotirios K.","family":"Goudos","sequence":"additional","affiliation":[]},{"given":"Katherine","family":"Siakavara","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1309"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00638"},{"key":"ref10","first-page":"3795","article-title":"Optimal artificial neural network design for propagation path-loss prediction using adaptive evolutionary algorithms","author":"sotiroudis","year":"2013","journal-title":"Proc of the European Conf on Antennas and Propag (EuCAP)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LAWP.2013.2251994"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1080\/23311916.2018.1444345"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2947009"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10287-010-0121-8"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/APWC.2013.6624896"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2529\/PIERS070220023434"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"ref18","article-title":"Deep learning-based signal strength prediction using geographical images and expert knowledge","author":"thrane","year":"2020","journal-title":"arXiv 2008 07747"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.23919\/FUSION45008.2020.9190246"},{"key":"ref28","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MOCAST.2019.8741751"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/telecom1020009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8489326"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2931072"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.11113\/elektrika.v18n1.140"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2020.04.008"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2964103"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aeue.2015.06.014"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403287"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738887"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909586"},{"key":"ref21","year":"2017","journal-title":"OpenStreetMap contributors Planet dump"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref23","first-page":"431","article-title":"Understanding variable importances in forests of randomized trees","volume":"1","author":"louppe","year":"2013","journal-title":"Proc 26th Int Conf Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2453991"},{"key":"ref25","year":"2011","journal-title":"EDX Wireless Microcell\/Indoor Module Reference Manual Version 7"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09354618.pdf?arnumber=9354618","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:51Z","timestamp":1639771011000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9354618\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3059589","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}