{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:31:30Z","timestamp":1762918290107,"version":"3.37.3"},"reference-count":120,"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":"R\u00e9seau de Transport d\u2019Electricit\u00e9"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3131391","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T23:36:50Z","timestamp":1638315410000},"page":"163391-163411","source":"Crossref","is-referenced-by-count":3,"title":["Proof of Concept: Calibration of an Overhead Line Conductors\u2019 Movements Simulation Model Using Ensemble-Based Machine Learning Model"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2466-9078","authenticated-orcid":false,"given":"Hamdi","family":"Amroun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Ammi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fikri","family":"Hafid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"A survey of systems for predicting stock market movements, combining market indicators and machine learning classifiers","year":"2013","author":"caley","key":"ref39"},{"article-title":"Leveraging big data and machine learning technologies for accurate and scalable genomic analysis","year":"2020","author":"shi","key":"ref38"},{"article-title":"Development of integrated machine learning and data science approaches for the prediction of cancer mutation and autonomous drug discovery of anti-cancer therapeutic agents","year":"2020","author":"agajanian","key":"ref33"},{"article-title":"Machine learning in space weather","year":"2019","author":"chandorkar","key":"ref32"},{"article-title":"Hypothesis-based machine learning for deep-water channel systems","year":"2020","author":"vento","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1089\/088922203762688658"},{"article-title":"A qualitative exploratory study of emergency medicine clinician perspectives on clinical decision support systems (CDSS) rooted in machine learning in England","year":"2020","author":"hayes","key":"ref37"},{"article-title":"Towards explainability in machine learning for Malware detection","year":"2020","author":"bose","key":"ref36"},{"article-title":"Understanding and supporting vocabulary learners via machine learning on behavioral and linguistic data","year":"2020","author":"nam","key":"ref35"},{"article-title":"Detection of SNPS associated with bone loss rate by using machine learning approaches","year":"2020","author":"yaganapu","key":"ref34"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2983924"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.0c05303"},{"key":"ref29","article-title":"A deep active learning system for species identification and counting in camera trap images","author":"norouzzadeh","year":"2019","journal-title":"arXiv 1910 09716"},{"key":"ref20","first-page":"144","article-title":"The ideal teaching machine","author":"yazdani","year":"1986","journal-title":"Computers and Modern Language Studies"},{"key":"ref22","article-title":"Reducing estimation risk in mean-variance portfolios with machine learning","author":"kinn","year":"2018","journal-title":"arXiv 1804 01764"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114037"},{"article-title":"Application of machine learning algorithms in adaptive web-based information systems","year":"1999","author":"smith","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-2845-y"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1002\/fld.4888"},{"key":"ref26","volume":"1241","author":"bhattacharjee","year":"2020","journal-title":"Machine Learning Image Processing Network Security and Data Sciences Second International Conference MIND 2020 Silchar India July30–31 2020 Proceedings Part II"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1063\/1.4927765"},{"key":"ref25","first-page":"1","article-title":"An open experimental database for exploring inorganic materials","volume":"5","author":"zakutayev","year":"2018","journal-title":"Data Science Journal"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2016.05.003"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw4741"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2019.1680576"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1063\/1.5061693"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0146-9"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2101784118"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.2514\/2.2134"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2020.1715844"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s00162-020-00518-y"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.2514\/1.J055595"},{"key":"ref40","article-title":"FinBERT: Financial sentiment analysis with pre-trained language models","author":"araci","year":"2019","journal-title":"arXiv 1908 10063"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3026044"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2756872"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1177\/1094670517738373"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-clinpsy-032816-044949"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.112236"},{"key":"ref49","article-title":"Hidden fluid mechanics: A Navier–Stokes informed deep learning framework for assimilating flow visualization data","author":"raissi","year":"2018","journal-title":"arXiv 1808 04327"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemmater.9b03854"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-46649-z"},{"article-title":"Message passing neural networks for molecular property prediction","year":"2019","author":"swanson","key":"ref46"},{"article-title":"Density functional theory and machine learning methods for dielectric materials discovery","year":"2016","author":"petousis","key":"ref45"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevFluids.4.100501"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-fluid-010719-060214"},{"article-title":"Polarizable multipolar electrostatics driven by Kriging machine learning for a peptide force field: Assessment, improvement and up-scaling","year":"2014","author":"fletcher","key":"ref42"},{"key":"ref41","first-page":"10","article-title":"Approval voting and incentives in crowdsourcing","author":"shah","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"article-title":"Computer vision and machine learning methods for the analysis of brain and cardiac imagery","year":"2010","author":"mohan","key":"ref44"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1128\/AEM.70.10.6157-6165.2004"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.2514\/6.2018-1903"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1115\/GT2017-63497"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/s42241-020-0028-y"},{"key":"ref70","article-title":"Universal differential equations for scientific machine learning","author":"rackauckas","year":"2020","journal-title":"arXiv 2001 04385"},{"key":"ref76","article-title":"A comprehensive physics-informed machine learning framework for predictive turbulence modeling","author":"wang","year":"2017","journal-title":"arXiv 1701 07102"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018171291"},{"key":"ref74","article-title":"Super-resolution reconstruction of turbulent flows with machine learning","author":"fukami","year":"2018","journal-title":"arXiv 1811 11328"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.114025"},{"key":"ref78","first-page":"1","article-title":"Automatic differentiation in machine learning: A survey","volume":"18","author":"baydin","year":"2018","journal-title":"J Mach Learn Res"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2020.690"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2020.948"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.2514\/6.2015-2460"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-conmatphys-031119-050745"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2006.11.016"},{"key":"ref64","article-title":"A review on deep reinforcement learning for fluid mechanics","author":"garnier","year":"2019","journal-title":"arXiv 1908 04127"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevFluids.4.064603"},{"key":"ref66","article-title":"Closed-loop separation control using machine learning","author":"gautier","year":"2014","journal-title":"arXiv 1405 0908"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2015.08.014"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.2514\/6.2014-2219"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2006.08.005"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0193-y"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechflu.2020.09.013"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-7-1247-2014"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.112907"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2015.02.019"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jchemed.8b00692"},{"journal-title":"Rules of Machine Learning Best Practices for ML Engineering","year":"2017","author":"zinkevich","key":"ref107"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1145\/2063176.2063182"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2016.05.062"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2017.2742781"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1016\/S1001-6058(16)60815-6"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.113722"},{"key":"ref104","article-title":"A parallel approach for numerical solution of vortex induced vibrations of very long risers","author":"herfjord","year":"1998","journal-title":"Proc Int OMAE Symp Buenos Aires"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-053018-023744"},{"key":"ref103","first-page":"1","article-title":"Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks","volume":"67","author":"zhu","year":"2020","journal-title":"Comput Mech"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2016.803"},{"key":"ref111","article-title":"Machine learning methods for better water quality prediction","volume":"578","author":"ahmed","year":"2019","journal-title":"J Hydrol"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2020.102832"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/324\/1\/012049"},{"article-title":"Machine learning for fluid mechanics applications","year":"2020","author":"g\u00fclek","key":"ref98"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2020.1773932"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2695801"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-economics-080217-053433"},{"key":"ref10","article-title":"Underspecification presents challenges for credibility in modern machine learning","author":"d\u2019amour","year":"2020","journal-title":"arXiv 2011 03395"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1257\/pandp.20191070"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30131-X"},{"key":"ref13","first-page":"317","volume":"19","author":"abrell","year":"2019","journal-title":"How effective was the UK carbon tax?—A machine learning approach to policy evaluation"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93728-1_33"},{"key":"ref15","article-title":"Machine learning discriminates a movement disorder in a zebrafish model of Parkinson’s disease","volume":"13","author":"hughes","year":"2020","journal-title":"Disease Models & Mechanisms"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3100490"},{"key":"ref16","article-title":"A hierarchy of limitations in machine learning","author":"malik","year":"2020","journal-title":"arXiv 2002 05193"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-soc-073117-041106"},{"key":"ref117","article-title":"Recognition of permeability from images with convolutional neural networks","volume":"63","author":"yin","year":"2018","journal-title":"Bull Amer Phys Soc"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3200904"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2019.1649196"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1115\/1.4043964"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2018.797"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2018.1553742"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003842"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/s00162-020-00528-w"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1080\/12460125.2020.1776927"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2018.04.004"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2020.184"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/s10494-019-00011-5"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1002\/fld.4416"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2021.09.079"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-040617-014208"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2020.1758792"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2010.11.002"},{"key":"ref87","article-title":"Informed machine learning—A taxonomy and survey of integrating knowledge into learning systems","author":"von rueden","year":"2019","journal-title":"arXiv 1903 12394"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2019.00117"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09627993.pdf?arnumber=9627993","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T21:16:22Z","timestamp":1648502182000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9627993\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":120,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3131391","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}