{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T10:17:22Z","timestamp":1781777842270,"version":"3.54.5"},"reference-count":191,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense (DoD) High Performance Computing Modernization Program, through the U.S. Army Engineering Research and Development Center","doi-asserted-by":"publisher","award":["W912HZ21C0014"],"award-info":[{"award-number":["W912HZ21C0014"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3391130","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T17:32:46Z","timestamp":1713461566000},"page":"57574-57602","source":"Crossref","is-referenced-by-count":91,"title":["Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3875-4137","authenticated-orcid":false,"given":"Logan","family":"Cummins","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Sommers","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5230-8723","authenticated-orcid":false,"given":"Somayeh Bakhtiari","family":"Ramezani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9151-8347","authenticated-orcid":false,"given":"Sudip","family":"Mittal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3287-5855","authenticated-orcid":false,"given":"Joseph","family":"Jabour","sequence":"additional","affiliation":[{"name":"U.S. Army Engineer Research and Development Center (ERDC), Vicksburg, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3858-0381","authenticated-orcid":false,"given":"Maria","family":"Seale","sequence":"additional","affiliation":[{"name":"U.S. Army Engineer Research and Development Center (ERDC), Vicksburg, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2779-0076","authenticated-orcid":false,"given":"Shahram","family":"Rahimi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3267960"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2022.09.017"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/app10124182"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/su11164371"},{"key":"ref5","volume-title":"What is the Fourth Industrial Revolution? | Industrial Analytics Platform","author":"Lavopa","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI50451.2021.9659965"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534639"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10050593"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3583558"},{"key":"ref10","article-title":"Towards human-centered explainable AI: A survey of user studies for model explanations","author":"Rong","year":"2022","journal-title":"arXiv:2210.11584"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-022-01843-7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1493"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-8403"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581001"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3216617"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/s21238020"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101805"},{"key":"ref18","article-title":"Explainable artificial intelligence (XAI) on TimeSeries data: A survey","author":"Rojat","year":"2021","journal-title":"arXiv:2104.00950"},{"key":"ref19","article-title":"Explainability is in the mind of the beholder: Establishing the foundations of explainable artificial intelligence","author":"Sokol","year":"2021","journal-title":"arXiv:2112.14466"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2021.102684"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/app12199423"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA45728.2021.9613467"},{"key":"ref23","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lundberg"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref26","volume-title":"Interpretable Machine Learning","author":"Molnar","year":"2020"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI54926.2021.00108"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110276"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI50451.2021.9659838"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/793161"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n160"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1002\/cl2.1230"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1177\/1475921720929939"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.05.079"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1115\/GT2022-80777"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICAC55051.2022.9911162"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.3390\/info14050256"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10354-7"},{"key":"ref39","first-page":"1","article-title":"PRONOSTIA: An experimental platform for bearings accelerated degradation tests","volume-title":"Proc. IEEE Int. Conf. Prognostics Health Manag.","author":"Nectoux"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2005.03.007"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109955"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108105"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3200428"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/s22239037"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2020.3048950"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3243929"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3136144"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120860"},{"issue":"4","key":"ref49","first-page":"1","article-title":"An end-to-end bearing fault diagnosis and severity assessment with interpretable deep learning","volume":"18","author":"Ben Abid","year":"2022","journal-title":"J. Electr. Syst."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3390\/mi13091471"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ48607.2020.9177537"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2007.07.004"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109013"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.05.003"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3390\/en13020389"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/MDM52706.2021.00017"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106031"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112595"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-23633-4_29"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3282664"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.36001\/phmconf.2019.v11i1.804"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330908"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66770-2_7"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA55696.2022.00085"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-23633-4_25"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.4271\/2021-01-0247"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.104073"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-022-01207-y"},{"key":"ref69","volume-title":"Turbofan Engine Degradation Simulation Data Set","volume":"18","author":"Saxena","year":"2008"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3277620"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2859922"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.3390\/s22010291"},{"key":"ref73","first-page":"83","article-title":"Learning how to monitor: Pairing monitoring and learning for online system verification","volume-title":"Proc. OVERLAY","author":"Brunello"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108353"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA55696.2022.00234"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08760-8_40"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.3390\/s23041892"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101781"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.3390\/a15060178"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-12670-3_12"},{"key":"ref81","first-page":"1","article-title":"Using artificial intelligence tools to detect problems in induction motors","volume-title":"Proc. 1st Int. Conf. Soft Comput. Intell. Syst.","volume":"1","author":"Brito"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.01.360"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/AI4I49448.2020.00023"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/AI4I51902.2021.00029"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA53316.2021.9564164"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.3390\/machines11030322"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2958787"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109232"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.36001\/ijphm.2023.v14i1.3431"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2022.103781"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.015"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.04.013"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.02.001"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2837621"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101778"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA53316.2021.9564228"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-23618-1_25"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA54385.2022.10032357"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.3390\/s21155200"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2021.10.080"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3144177"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3213009"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2004.02.004"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2020.101850"},{"key":"ref105","volume-title":"Hard Drive Failure Rates: A Look At Drive Reliability","author":"Klein","year":"2021"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100042"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-1994-5"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3127576"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.109046"},{"key":"ref110","volume-title":"Wind Farm Measurements","author":"Hansen","year":"2021"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2021.100065"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.3390\/s21041512"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488481"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/CCTA49430.2022.9966158"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/CCTA48906.2021.9658806"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-57321-8_8"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataService52369.2021.00007"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109147"},{"key":"ref119","volume-title":"SECOM","author":"McCann","year":"2008"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.3390\/app13053088"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1080\/01969722.2019.1705550"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219871"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.3390\/app11094280"},{"key":"ref124","volume-title":"Uci Machine Learning Repository","author":"Dua","year":"2019"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/RAMS51473.2023.10088240"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ45933.2021.9494540"},{"key":"ref127","article-title":"Automated fault tree learning from continuous-valued sensor data: A case study on domestic heaters","author":"Verkuil","year":"2022","journal-title":"arXiv:2203.07374"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/CCTA49430.2022.9966138"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108313"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-18697-4_4"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.3390\/app11167376"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.3390\/s22010226"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.11"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2021.107381"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2022.103339"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2984976"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/IEEM50564.2021.9673044"},{"key":"ref138","author":"Howard","year":"2018","journal-title":"VSB Power Line Fault Detection"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW53433.2021.00137"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/ICPES53652.2021.9683812"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3279808"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.109068"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2022.09.032"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.3390\/su14148664"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116208"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467161"},{"key":"ref147","volume-title":"Water Pipe (WCORP-002)","year":"2023"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.3850\/978-981-14-8593-0_3729-cd"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.688969"},{"key":"ref150","volume-title":"Interpreting Random Forests","author":"Saabas","year":"2014"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref153","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"70","author":"Sundararajan"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOS1145"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119115"},{"key":"ref156","article-title":"SmoothGrad: Removing noise by adding noise","author":"Smilkov","year":"2017","journal-title":"arXiv:1706.03825"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3063289"},{"key":"ref158","volume-title":"Teamhg-memex\/eli5: A Library for Debugging\/inspecting Machine Learning Classifiers and Explaining Their Predictions","year":"2023"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105730"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00839-y"},{"key":"ref162","volume-title":"AI4I 2020 Predictive Maintenance Dataset","year":"2020"},{"key":"ref163","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013","journal-title":"arXiv:1312.6034"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1963.10500855"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5633-9"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-06117-0"},{"key":"ref167","first-page":"1","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Ke"},{"key":"ref168","volume-title":"Launch Control Safety Study","author":"Watson","year":"1961"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/SBRN.2000.889734"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/2.53"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/SLT.2018.8639585"},{"key":"ref172","article-title":"Graph attention networks","author":"Veli\u010d kovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30206-3_12"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-51862-0.50001-0"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1080\/14786440009463897"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.2307\/2331554"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177699147"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177013604"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1002\/9780470172247"},{"key":"ref182","article-title":"InterpretML: A unified framework for machine learning interpretability","author":"Nori","year":"2019","journal-title":"arXiv:1909.09223"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-38756-7_4"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.2307\/1403797"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20319-0_30"},{"key":"ref189","article-title":"Analyzing XAI metrics: Summary of the literature review","author":"Sisk","year":"2022","journal-title":"techrxiv.21262041.v1"},{"key":"ref190","article-title":"Assessing XAI: Unveiling evaluation metrics for local explanation, taxonomies, key concepts, and practical applications","author":"Kadir","year":"2023","journal-title":"EngrXiv preprint"},{"key":"ref191","article-title":"Metrics for explainable AI: Challenges and prospects","author":"Hoffman","year":"2018","journal-title":"arXiv:1812.04608"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/10380310\/10504833-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10504833.pdf?arnumber=10504833","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T19:11:45Z","timestamp":1714763505000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10504833\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":191,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3391130","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}