{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:44:35Z","timestamp":1775666675335,"version":"3.50.1"},"reference-count":30,"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":[{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT) Grant","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002560","name":"Korean Government through Ministry of Trade Industry and Energy (MOTIE)","doi-asserted-by":"publisher","award":["P0012724"],"award-info":[{"award-number":["P0012724"]}],"id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002560","name":"Soonchunhyang University Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3103746","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T20:25:56Z","timestamp":1628540756000},"page":"112624-112636","source":"Crossref","is-referenced-by-count":59,"title":["Multi-Disease Classification Model Using Strassen\u2019s Half of Threshold (SHoT) Training Algorithm in Healthcare Sector"],"prefix":"10.1109","volume":"9","author":[{"given":"Manjula Devi","family":"Ramasamy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keerthika","family":"Periasamy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lalitha","family":"Krishnasamy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajesh Kumar","family":"Dhanaraj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyoung","family":"Nam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.04.005"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1993.298622"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(124)","article-title":"Artificial neural networks in hydrology. II: Hydrologic applications","volume":"5","author":"govindaraju","year":"2000","journal-title":"J Hydrol Eng"},{"key":"ref12","first-page":"985","article-title":"Extreme learning machine: A new learning scheme of feedforward neural networks","volume":"2","author":"huang","year":"2004","journal-title":"Proc IEEE Int Joint Conf Neural Netw"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-013-9405-z"},{"key":"ref14","first-page":"1","article-title":"A very fast learning method for neural networks based on sensitivity analysis","volume":"7","author":"castillo","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref15","first-page":"1","article-title":"A new initialization method for neural networks using sensitivity analysis","volume":"2830","author":"guijarro-berdinas","year":"2006","journal-title":"Proc Int Conf Math Stat Modeling"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/GCIS.2009.136"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/5326.897072"},{"key":"ref18","author":"haykin","year":"2010","journal-title":"Neural Networks and Learning Machines"},{"key":"ref19","first-page":"378","article-title":"Back propagation algorithm: The best algorithm among the multi-layer perceptron algorithm","volume":"9","author":"alsmadi","year":"2009","journal-title":"IJCSNS"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICICES.2014.7033836"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1990.137819"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.01.065"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/346949"},{"key":"ref6","author":"levitin","year":"2012","journal-title":"Introduction to the Design & Analysis of Algorithms"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2918276"},{"key":"ref5","first-page":"138","article-title":"EAST: An exponential adaptive skipping training algorithm for multilayer feedforward neural networks","volume":"13","author":"devi","year":"2014","journal-title":"WSEAS Trans Comput"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/72.143378"},{"key":"ref7","first-page":"860","article-title":"Back propagation is sensitive to initial conditions","volume":"4","author":"kolen","year":"1990","journal-title":"Complex Syst"},{"key":"ref2","author":"rajasekaran","year":"2017","journal-title":"Neural Networks Fuzzy Systems and Evolutionary Algorithms Synthesis and Applications"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1993.298625"},{"key":"ref1","author":"faggella","year":"2019","journal-title":"Where Healthcare&#x2019;s Big Data Actually Comes From"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.878121"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23172-8_30"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2045657"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/a10020070"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2002.1031939"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-18046"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2820092"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09509532.pdf?arnumber=9509532","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:26Z","timestamp":1639771046000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9509532\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3103746","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}