{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T11:49:19Z","timestamp":1749124159415,"version":"3.37.3"},"reference-count":36,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3096513","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T20:34:53Z","timestamp":1626122093000},"page":"104969-104979","source":"Crossref","is-referenced-by-count":3,"title":["A Machine Learning Based Approach for Recommending Unfamiliar Process Activities"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6452-6915","authenticated-orcid":false,"given":"Anastasiia","family":"Pika","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7205-8821","authenticated-orcid":false,"given":"Moe Thandar","family":"Wynn","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3301300"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47175-4_19"},{"key":"ref30","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1080\/17517575.2014.986215"},{"key":"ref35","article-title":"An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs","author":"weinzierl","year":"2020","journal-title":"arXiv 2005 01194"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5465\/ame.1991.4274728"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1108\/09696470610680017"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/09585192.2010.500489"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"friedman","year":"2001","journal-title":"Ann Statist"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26643-1_4"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38709-8_38"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31095-9_44"},{"key":"ref17","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume":"1412","author":"kingma","year":"2015","journal-title":"Proc ICLR"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2330555"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46295-0_25"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1108\/MD-05-2017-0476"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2016.06.012"},{"key":"ref3","first-page":"475","article-title":"Towards a taxonomy of human resource allocation criteria","author":"arias","year":"2017","journal-title":"Proc BPM"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2008.07.002"},{"key":"ref5","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref8","first-page":"462","article-title":"Predictive process monitoring methods: Which one suits me best","author":"di","year":"2018","journal-title":"Proc BPM"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2018.090734"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33143-5"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2017.2772256"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38709-8_30"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICPM.2019.00027"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3041218"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.03.003"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/8085.001.0001"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-49435-3_21"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09481256.pdf?arnumber=9481256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:00Z","timestamp":1639771020000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9481256\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3096513","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}