{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T16:57:33Z","timestamp":1730221053860,"version":"3.28.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,6]]},"DOI":"10.1109\/eurocon56442.2023.10199072","type":"proceedings-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T17:49:46Z","timestamp":1691430586000},"page":"585-589","source":"Crossref","is-referenced-by-count":0,"title":["A Distributed Ensemble of Diverse Deep Learning Models for Predicting COVID-19 Cases"],"prefix":"10.1109","author":[{"given":"Mohammad Mehedi","family":"Hassan","sequence":"first","affiliation":[{"name":"King Saud University,Information Systems Department CCIS,Riadh,Saudi Arabia,11543"}]},{"given":"Mabrook S.","family":"AlRakhami","sequence":"additional","affiliation":[{"name":"King Saud University,Information Systems Department CCIS,Riadh,Saudi Arabia,11543"}]},{"given":"Ahmed Zohier","family":"Elhendi","sequence":"additional","affiliation":[{"name":"King Saud University,Science Technology and Innovation Dept.,Riyadh,Saudi Arabia,11543"}]},{"given":"Salman A.","family":"AlQahtani","sequence":"additional","affiliation":[{"name":"King Saud University,Computer Engineering Department CCIS,Riadh,Saudi Arabia,11543"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"crossref","first-page":"105560","DOI":"10.1016\/j.compbiomed.2022.105560","article-title":"A data-driven hybrid ensemble ai model for covid-19 infection forecast using multiple neural networks and reinforced learning","volume":"146","author":"jin","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07757-5"},{"journal-title":"Robust stock price prediction using machine learning and deep learning models","year":"2020","author":"mehtab","key":"ref23"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00734-2"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/math11051216"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09838-1"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-0151-5_6"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-55320-6"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-06218-3"},{"key":"ref21","article-title":"A survey of convolutional neural networks: analysis, applications, and prospects","author":"li","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107109"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/app13031816"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2021.100287"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109761"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.4018\/IJEOE.2020100106","article-title":"Forecasting and technical comparison of inflation in turkey with box-jenkins (arima) models and the artificial neural network","volume":"9","author":"oz","year":"2020","journal-title":"International Journal of Energy Optimization and Engineering (IJEOE)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-04222-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.4038\/sljssh.v3i1.88"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104482"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19020738"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110058"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.010691"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMBMC.2021.3099367"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05626-8"}],"event":{"name":"IEEE EUROCON 2023 - 20th International Conference on Smart Technologies","start":{"date-parts":[[2023,7,6]]},"location":"Torino, Italy","end":{"date-parts":[[2023,7,8]]}},"container-title":["IEEE EUROCON 2023 - 20th International Conference on Smart Technologies"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10198869\/10198877\/10199072.pdf?arnumber=10199072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T17:42:10Z","timestamp":1693244530000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10199072\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,6]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/eurocon56442.2023.10199072","relation":{},"subject":[],"published":{"date-parts":[[2023,7,6]]}}}