{"id":"https://openalex.org/W4402352861","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650835","title":"Machine Learning and IoT for Predicting the Productivity of MRI Equipment","display_name":"Machine Learning and IoT for Predicting the Productivity of MRI Equipment","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352861","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650835"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650835","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078294323","display_name":"P\u00e9ricles Miranda","orcid":"https://orcid.org/0000-0002-5767-7544"},"institutions":[{"id":"https://openalex.org/I62921916","display_name":"Universidade Federal Rural de Pernambuco","ror":"https://ror.org/02ksmb993","country_code":"BR","type":"education","lineage":["https://openalex.org/I62921916"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"P\u00e9ricles B.C. Miranda","raw_affiliation_strings":["Universidade Federal Rural de Pernambuco,Departamento de Computa&#x00E7;&#x00E3;o,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal Rural de Pernambuco,Departamento de Computa&#x00E7;&#x00E3;o,Brazil","institution_ids":["https://openalex.org/I62921916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060231291","display_name":"Leonardo de A. Monte","orcid":null},"institutions":[{"id":"https://openalex.org/I62921916","display_name":"Universidade Federal Rural de Pernambuco","ror":"https://ror.org/02ksmb993","country_code":"BR","type":"education","lineage":["https://openalex.org/I62921916"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leonardo Monte","raw_affiliation_strings":["Universidade Federal Rural de Pernambuco,Departamento de Computa&#x00E7;&#x00E3;o,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal Rural de Pernambuco,Departamento de Computa&#x00E7;&#x00E3;o,Brazil","institution_ids":["https://openalex.org/I62921916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074948157","display_name":"Andr\u00e9 Nascimento","orcid":"https://orcid.org/0000-0002-9333-3212"},"institutions":[{"id":"https://openalex.org/I62921916","display_name":"Universidade Federal Rural de Pernambuco","ror":"https://ror.org/02ksmb993","country_code":"BR","type":"education","lineage":["https://openalex.org/I62921916"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Andr\u00e9 C. A. Nascimento","raw_affiliation_strings":["Universidade Federal Rural de Pernambuco,Departamento de Computa&#x00E7;&#x00E3;o,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal Rural de Pernambuco,Departamento de Computa&#x00E7;&#x00E3;o,Brazil","institution_ids":["https://openalex.org/I62921916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090283789","display_name":"M\u00e1rcio P. Basgalupp","orcid":"https://orcid.org/0000-0002-2005-1249"},"institutions":[{"id":"https://openalex.org/I88273585","display_name":"Universidade Federal de S\u00e3o Paulo","ror":"https://ror.org/02k5swt12","country_code":"BR","type":"education","lineage":["https://openalex.org/I88273585"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"M\u00e1rcio P. Basgalupp","raw_affiliation_strings":["Universidade Federal de S&#x00E3;o Paulo,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal de S&#x00E3;o Paulo,Brazil","institution_ids":["https://openalex.org/I88273585"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111319862","display_name":"Jos\u00e9 Leovigildo Coelho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jos\u00e9 Leovigildo Coelho","raw_affiliation_strings":["IONIC Health,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IONIC Health,Brazil","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7676,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66733884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13113","display_name":"Engineering Technology and Methodologies","score":0.8855999708175659,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13113","display_name":"Engineering Technology and Methodologies","score":0.8855999708175659,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10763","display_name":"Digital Transformation in Industry","score":0.8790000081062317,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.8483999967575073,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.7940652966499329},{"id":"https://openalex.org/keywords/productivity","display_name":"Productivity","score":0.7424585223197937},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6764341592788696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5008847713470459},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4214567244052887},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.22027072310447693}],"concepts":[{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.7940652966499329},{"id":"https://openalex.org/C204983608","wikidata":"https://www.wikidata.org/wiki/Q2111958","display_name":"Productivity","level":2,"score":0.7424585223197937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764341592788696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5008847713470459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4214567244052887},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.22027072310447693},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650835","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1831416753","https://openalex.org/W1999393241","https://openalex.org/W2042506099","https://openalex.org/W2045914271","https://openalex.org/W2538578225","https://openalex.org/W2554781611","https://openalex.org/W2588941434","https://openalex.org/W2755296661","https://openalex.org/W2914021120","https://openalex.org/W3092012490","https://openalex.org/W4293868372","https://openalex.org/W4310276739","https://openalex.org/W4320712473","https://openalex.org/W4381249489","https://openalex.org/W4385484612","https://openalex.org/W4388579144","https://openalex.org/W4390875033","https://openalex.org/W4390966943"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,51,130],"rapid":[1],"evolution":[2],"and":[3,17,48,66,71,77,88,159,176],"widespread":[4],"accessibility":[5],"of":[6,32,42,54,68,73,105,132,139,178,201,207,236,248,256],"non-invasive":[7],"medical":[8,24,35,75],"imaging":[9,29],"technologies,":[10,56],"exemplified":[11],"by":[12,91,155],"Magnetic":[13],"Resonance":[14],"Imaging":[15],"(MRI)":[16],"Computerized":[18],"Tomography":[19],"(CT),":[20],"are":[21],"fundamentally":[22],"reshaping":[23],"decisionmaking":[25],"paradigms.":[26],"These":[27],"sophisticated":[28],"modalities,":[30],"capable":[31],"extracting":[33],"high-definition":[34],"images,":[36],"have":[37],"emerged":[38],"as":[39],"integral":[40],"components":[41],"modern":[43],"healthcare,":[44],"facilitating":[45],"precise":[46],"diagnostics":[47],"treatment":[49],"planning.":[50],"escalating":[52],"adoption":[53],"such":[55],"however,":[57],"has":[58],"accentuated":[59],"the":[60,69,78,85,103,133,140,152,156,160,165,174,179,191,199,208,233,246,253],"need":[61],"for":[62],"a":[63,136],"nuanced":[64,228],"understanding":[65],"optimization":[67],"performance":[70,200,242],"productivity":[72],"both":[74],"equipment":[76,123,241],"teams":[79],"operating":[80],"them,":[81],"mainly":[82],"due":[83],"to":[84,101,223,252],"high":[86,145],"costs":[87],"risks":[89],"caused":[90],"their":[92,183],"misuse.":[93],"This":[94,170,227],"work":[95],"proposes":[96],"using":[97],"univariate":[98],"analytical":[99],"models":[100,158],"estimate":[102],"number":[104],"exams":[106],"performed":[107],"per":[108],"day":[109],"with":[110,218],"machine":[111],"learning":[112],"algorithms.":[113],"For":[114],"such,":[115],"different":[116,126],"energy-related":[117,237],"sensors":[118,221,238],"monitoring":[119,250],"25":[120,209],"magnetic":[121],"resonance":[122],"from":[124],"three":[125],"brands":[127],"were":[128],"considered.":[129],"results":[131,215],"research":[134],"reveal":[135],"compelling":[137],"validation":[138],"proposed":[141],"approach.":[142],"A":[143],"notably":[144],"Pearson":[146],"correlation":[147,172],"coefficient":[148],"is":[149],"observed":[150],"between":[151],"predictions":[153],"generated":[154],"evaluated":[157,210],"real":[161],"measurements":[162],"obtained":[163],"through":[164],"Radiology":[166],"Information":[167],"System":[168],"(RIS).":[169],"robust":[171],"emphasizes":[173],"accuracy":[175],"reliability":[177],"estimation":[180],"models,":[181],"validating":[182],"potential":[184],"applicability":[185],"in":[186,239],"real-world":[187],"healthcare":[188],"scenarios.":[189],"Furthermore,":[190],"study":[192],"unveils":[193],"an":[194],"intriguing":[195],"trend":[196],"that":[197],"distinguishes":[198],"electric":[202,219],"current":[203,220],"sensors.":[204],"Thirteen":[205],"out":[206],"MRI":[211],"machines":[212],"demonstrate":[213],"superior":[214],"when":[216],"equipped":[217],"compared":[222],"other":[224],"sensor":[225],"types.":[226],"insight":[229],"not":[230],"only":[231],"substantiates":[232],"critical":[234],"role":[235],"predicting":[240],"but":[243],"also":[244],"underscores":[245],"importance":[247],"tailoring":[249],"strategies":[251],"unique":[254],"characteristics":[255],"each":[257],"machine.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
