{"id":"https://openalex.org/W4405936395","doi":"https://doi.org/10.1109/iccais63750.2024.10814375","title":"Design and Optimization of Deep Neural Networks for Multi-Label Classification on Smart Electrical Devices Monitoring System","display_name":"Design and Optimization of Deep Neural Networks for Multi-Label Classification on Smart Electrical Devices Monitoring System","publication_year":2024,"publication_date":"2024-11-26","ids":{"openalex":"https://openalex.org/W4405936395","doi":"https://doi.org/10.1109/iccais63750.2024.10814375"},"language":"en","primary_location":{"id":"doi:10.1109/iccais63750.2024.10814375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais63750.2024.10814375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS)","raw_type":"proceedings-article"},"type":"article","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/A5101536309","display_name":"Nam Hoang Nguyen","orcid":"https://orcid.org/0000-0002-9476-3126"},"institutions":[{"id":"https://openalex.org/I94518387","display_name":"Hanoi University of Science and Technology","ror":"https://ror.org/04nyv3z04","country_code":"VN","type":"education","lineage":["https://openalex.org/I94518387"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Nam Hoang Nguyen","raw_affiliation_strings":["School of Electrical &#x0026; Electronic Engineering, Hanoi University of Science and Technology (HUST),Hanoi,Vietnam"],"affiliations":[{"raw_affiliation_string":"School of Electrical &#x0026; Electronic Engineering, Hanoi University of Science and Technology (HUST),Hanoi,Vietnam","institution_ids":["https://openalex.org/I94518387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101536309"],"corresponding_institution_ids":["https://openalex.org/I94518387"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46736024,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14139","display_name":"E-commerce and Technology Innovations","score":0.7639999985694885,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T14139","display_name":"E-commerce and Technology Innovations","score":0.7639999985694885,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13535","display_name":"Wireless Sensor Networks and IoT","score":0.6664000153541565,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/computer-science","display_name":"Computer science","score":0.6935511827468872},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5545517206192017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4733079671859741},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4661496877670288},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3519118130207062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6935511827468872},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5545517206192017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4733079671859741},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4661496877670288},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3519118130207062}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccais63750.2024.10814375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais63750.2024.10814375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1809873675","https://openalex.org/W2119821739","https://openalex.org/W2146241755","https://openalex.org/W2149224293","https://openalex.org/W2149706766","https://openalex.org/W2490662969","https://openalex.org/W2787894218","https://openalex.org/W2919115771","https://openalex.org/W2946547492","https://openalex.org/W4206902449","https://openalex.org/W4213251304","https://openalex.org/W6677580257"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"This":[0,48],"paper":[1],"presents":[2],"the":[3,114,134,141,146],"design":[4],"and":[5,37,63,88,97,109,143],"optimization":[6,89],"of":[7,25,116,136,145],"deep":[8],"neural":[9,84],"networks":[10],"(DNNs)":[11],"tailored":[12],"for":[13,70],"multi-label":[14,117],"classification":[15,95],"on":[16],"smart":[17,30,147],"electrical":[18,55],"devices":[19,73],"monitoring":[20],"system,":[21],"an":[22,65],"emerging":[23],"category":[24],"intelligent":[26],"measurement":[27],"devices.":[28],"The":[29,100,131],"system":[31,148],"is":[32],"equipped":[33],"with":[34],"advanced":[35],"sensing":[36],"data":[38],"acquisition":[39],"capabilities,":[40],"generating":[41],"complex":[42,49],"datasets":[43],"necessitating":[44],"sophisticated":[45],"analytical":[46],"methods.":[47,130],"dataset":[50],"comprises":[51],"various":[52],"properties":[53],"regarding":[54],"devices,":[56],"which":[57],"can":[58],"be":[59],"used":[60],"to":[61,93,112,128],"train":[62],"evaluate":[64],"artificial":[66],"intelligence":[67],"(AI)":[68],"model":[69],"classifying":[71],"home":[72],"when":[74],"plugged":[75],"in.":[76],"Various":[77],"DNN":[78],"architectures":[79],"are":[80],"explored":[81],"by":[82],"using":[83],"architecture":[85],"search":[86],"(NAS)":[87],"techniques":[90],"like":[91],"pruning":[92],"enhance":[94],"accuracy":[96],"computational":[98],"efficiency.":[99],"article's":[101],"approach":[102],"includes":[103],"leveraging":[104],"transfer":[105],"learning,":[106],"regularization":[107],"methods,":[108],"hyperparameter":[110],"tuning":[111],"address":[113],"challenges":[115],"classification.":[118],"Experimental":[119],"results":[120],"demonstrate":[121],"significant":[122],"improvements":[123],"in":[124,139,149],"prediction":[125],"performance":[126],"compared":[127],"traditional":[129],"findings":[132],"underscore":[133],"potential":[135],"optimized":[137],"DNNs":[138],"advancing":[140],"functionality":[142],"reliability":[144],"diverse":[150],"application":[151],"scenarios.":[152]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
