{"id":"https://openalex.org/W4411887269","doi":"https://doi.org/10.54364/aaiml.2025.52215","title":"Development of an Intelligent Fault Diagnosis Tool for iPhone Motherboards: Power Consumption Analysis Using Deep Learning","display_name":"Development of an Intelligent Fault Diagnosis Tool for iPhone Motherboards: Power Consumption Analysis Using Deep Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4411887269","doi":"https://doi.org/10.54364/aaiml.2025.52215"},"language":"en","primary_location":{"id":"doi:10.54364/aaiml.2025.52215","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.52215","pdf_url":"https://doi.org/10.54364/aaiml.2025.52215","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.54364/aaiml.2025.52215","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118757296","display_name":"P.D.K. Madhubhashana","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"P.D.K. Madhubhashana","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078808474","display_name":"Hirunima Jayasekara","orcid":"https://orcid.org/0000-0001-8235-5486"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H.D.N.V. Jayasekara","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081980322","display_name":"Gavindya Jayawardena","orcid":"https://orcid.org/0000-0002-9523-3346"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G.D.G.N. Jayawardena","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118371622","display_name":"B.N.S. Lankasena","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B.N.S. Lankasena","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024078250","display_name":"Buddhakoralalage Leelanga Dananjaya Seneviratne","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B.M. Seneviratne","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5118757296"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25732595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"05","issue":"02","first_page":"3784","last_page":"3808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14484","display_name":"Technology and Data Analysis","score":0.710099995136261,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14484","display_name":"Technology and Data Analysis","score":0.710099995136261,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13924","display_name":"Internet of Things and Social Network Interactions","score":0.666700005531311,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14156","display_name":"Engineering Applied Research","score":0.6574000120162964,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/fault","display_name":"Fault (geology)","score":0.6457054615020752},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.6026657819747925},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4921981394290924},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4836229383945465},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.4748953878879547},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4082304835319519},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3339814245700836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3322499096393585},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.07183083891868591},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07002976536750793}],"concepts":[{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6457054615020752},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.6026657819747925},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4921981394290924},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4836229383945465},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.4748953878879547},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4082304835319519},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3339814245700836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3322499096393585},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.07183083891868591},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07002976536750793},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.54364/aaiml.2025.52215","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.52215","pdf_url":"https://doi.org/10.54364/aaiml.2025.52215","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.54364/aaiml.2025.52215","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.52215","pdf_url":"https://doi.org/10.54364/aaiml.2025.52215","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411887269.pdf","grobid_xml":"https://content.openalex.org/works/W4411887269.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W1523146868","https://openalex.org/W2888015246","https://openalex.org/W2908550013","https://openalex.org/W2945759188","https://openalex.org/W2980686936","https://openalex.org/W3095793088","https://openalex.org/W3145480061","https://openalex.org/W3173071369","https://openalex.org/W4235390506","https://openalex.org/W4246055320","https://openalex.org/W4401308380","https://openalex.org/W4404022185"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W2804383999","https://openalex.org/W2802049774"],"abstract_inverted_index":{"This":[0,119],"study":[1,149],"presents":[2],"an":[3,33,151],"intelligent":[4,137,157,184],"microcontroller-based":[5],"diagnostic":[6,120],"tool":[7,41],"and":[8,16,25,36,53,59,96,112,130,136,161,172,182,191],"application":[9,121],"designed":[10],"to":[11,82],"enhance":[12],"fault":[13,57,117,177],"detection":[14],"accuracy":[15,110],"efficiency":[17],"in":[18,64,159,179],"iPhone":[19,76],"motherboards,":[20],"utilizing":[21],"power":[22,43,85,140,145,189],"consumption":[23,146],"data":[24],"deep":[26,88],"learning":[27,89],"(DL)":[28],"for":[29,55,115,134,156,169],"real-time":[30],"diagnostics.":[31,141],"Integrating":[32],"RP2040":[34],"microcontroller":[35],"INA226":[37],"current":[38],"sensor,":[39],"the":[40,105,108],"captures":[42],"patterns":[44],"during":[45],"boot-up,":[46],"a":[47,70,124,128,167],"method":[48],"applicable":[49],"across":[50],"embedded":[51,160],"systems":[52,138],"robotics":[54,135],"predictive":[56],"analysis":[58],"maintenance.":[60],"The":[61],"tool,":[62],"deployed":[63],"phone":[65],"repair":[66],"centers,":[67],"has":[68],"generated":[69],"comprehensive":[71],"dataset":[72],"of":[73],"over":[74],"1,600":[75],"6s":[77],"devices":[78,185],"with":[79,104,123,153],"faults":[80],"linked":[81],"12":[83],"distinct":[84],"rails.":[86],"Various":[87],"models,":[90],"including":[91],"Convolutional":[92],"Neural":[93],"Networks":[94],"(CNNs)":[95],"Long":[97],"Short-Term":[98],"Memory":[99],"(LSTM)":[100],"networks,":[101],"were":[102],"evaluated,":[103],"LSTM":[106],"achieving":[107],"highest":[109],"(99%)":[111],"F1-score":[113],"(0.997)":[114],"precise":[116,188],"classification.":[118],"communicates":[122],"central":[125],"server,":[126],"enabling":[127],"scalable":[129],"automated":[131],"framework":[132],"suitable":[133],"requiring":[139],"By":[142],"introducing":[143],"DL-based":[144],"analysis,":[147],"this":[148],"pioneers":[150],"approach":[152],"broad":[154],"implications":[155],"maintenance":[158],"robotic":[162,180],"systems.":[163],"Our":[164],"findings":[165],"offer":[166],"foundation":[168],"faster,":[170],"automated,":[171],"reliable":[173],"diagnostics,":[174],"potentially":[175],"advancing":[176],"management":[178],"applications":[181],"other":[183],"reliant":[186],"on":[187],"monitoring":[190],"control.":[192]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}