{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T18:44:13Z","timestamp":1757616253999,"version":"3.44.0"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"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":[[2019,12]]},"DOI":"10.1109\/ieem44572.2019.8978649","type":"proceedings-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T21:27:12Z","timestamp":1580765232000},"page":"1119-1123","source":"Crossref","is-referenced-by-count":2,"title":["Predicting the Remaining Useful Life of Ball Bearing Under Dynamic Loading Using Supervised Learning"],"prefix":"10.1109","author":[{"given":"S.","family":"Singh","sequence":"first","affiliation":[{"name":"Delhi Technological University,Department of Mechanical Engineering,New Delhi,India"}]},{"given":"T.","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Delhi Technological University,Department of Mechanical Engineering,New Delhi,India"}]},{"given":"G.","family":"Kumar","sequence":"additional","affiliation":[{"name":"Delhi Technological University,Department of Mechanical Engineering,New Delhi,India"}]},{"given":"O.M.","family":"Yadav","sequence":"additional","affiliation":[{"name":"North Dakota State University,Department of Industrial and Manufacturing Engineering,Fargo,USA"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC.2011.6022177"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.12.016"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref12"},{"key":"ref13","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"journal-title":"MATLAB and Statistics Toolbox Release","year":"2018","key":"ref14"},{"journal-title":"The Python Packaging Authority (PyPA)","article-title":"Python 3.6.5","year":"2018","key":"ref15"},{"journal-title":"Relief-based feature selection introduction and review","year":"2017","author":"urbanowicz","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1080\/10408340500526766"},{"journal-title":"Fault detection and model-based diagnostics in nonlinear dynamic systems","year":"2010","author":"nakhaeinejad","key":"ref18"},{"journal-title":"Less is more A comprehensive framework for the number of components of ensemble classifiers","year":"2017","author":"bonab","key":"ref19"},{"journal-title":"Statsmodels Econometric and Statistical Modeling with Python","year":"2010","author":"seabold","key":"ref28"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-162097"},{"journal-title":"Filter versus wrapper feature subset selection in large dimensionality micro array A review","year":"0","author":"kumari","key":"ref27"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.01.038"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICELMACH.2014.6960445"},{"key":"ref29","first-page":"2","author":"setup","year":"2000","journal-title":"NASA Bearing Data Description"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/s16060895"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICMA.2007.4304127"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2004.10.013"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2015.10.014"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-005-0481-0"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.06.007"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0118-7"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"journal-title":"Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events Theoretical Background","year":"0","author":"bafandeh","key":"ref21"},{"journal-title":"ST4_Method_Random_Forest Berkeley","year":"2001","author":"breiman","key":"ref24"},{"journal-title":"A Decision Tree Classification Model for University Admission System","year":"2012","author":"mashat","key":"ref23"},{"journal-title":"Confusion Matrix-based Feature Selection","year":"0","author":"visa","key":"ref26"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2569061"}],"event":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","start":{"date-parts":[[2019,12,15]]},"location":"Macao, China","end":{"date-parts":[[2019,12,18]]}},"container-title":["2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8961317\/8978490\/08978649.pdf?arnumber=8978649","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T18:09:09Z","timestamp":1757095749000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8978649\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/ieem44572.2019.8978649","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}