{"id":"https://openalex.org/W2802042319","doi":"https://doi.org/10.3390/s18051490","title":"Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images","display_name":"Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images","publication_year":2018,"publication_date":"2018-05-09","ids":{"openalex":"https://openalex.org/W2802042319","doi":"https://doi.org/10.3390/s18051490","mag":"2802042319","pmid":"https://pubmed.ncbi.nlm.nih.gov/29747439"},"language":"en","primary_location":{"id":"doi:10.3390/s18051490","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18051490","pdf_url":"https://www.mdpi.com/1424-8220/18/5/1490/pdf?version=1525867007","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/18/5/1490/pdf?version=1525867007","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059595536","display_name":"Atmane Khellal","orcid":"https://orcid.org/0000-0002-5455-6528"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Atmane Khellal","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing 100081, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing 100081, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072523018","display_name":"Hongbin Ma","orcid":"https://orcid.org/0000-0002-5734-3157"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongbin Ma","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing 100081, China","State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing 100081, China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110367662","display_name":"Qing Fei","orcid":"https://orcid.org/0000-0003-1276-8511"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Fei","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing 100081, China","State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing 100081, China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5059595536","https://openalex.org/A5072523018"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":5.7858,"has_fulltext":true,"cited_by_count":74,"citation_normalized_percentile":{"value":0.96654406,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"18","issue":"5","first_page":"1490","last_page":"1490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12676","display_name":"Machine Learning and ELM","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10062","display_name":"MicroRNA in disease regulation","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9689000248908997,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/overfitting","display_name":"Overfitting","score":0.8127994537353516},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.807961106300354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7949634790420532},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7527477741241455},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.578406810760498},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5634309649467468},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5493143200874329},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.547127366065979},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5449184775352478},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5388391613960266},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.43640270829200745},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.36116254329681396}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8127994537353516},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.807961106300354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7949634790420532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7527477741241455},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.578406810760498},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5634309649467468},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5493143200874329},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.547127366065979},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5449184775352478},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5388391613960266},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43640270829200745},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36116254329681396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/s18051490","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18051490","pdf_url":"https://www.mdpi.com/1424-8220/18/5/1490/pdf?version=1525867007","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:29747439","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29747439","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:706cbda3022243ddabb45b5e1c5a2b5d","is_oa":true,"landing_page_url":"https://doaj.org/article/706cbda3022243ddabb45b5e1c5a2b5d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 18, Iss 5, p 1490 (2018)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:4940733","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5982679","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s18051490","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18051490","pdf_url":"https://www.mdpi.com/1424-8220/18/5/1490/pdf?version=1525867007","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1809358198","display_name":null,"funder_award_id":"61473038","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5146614486","display_name":null,"funder_award_id":"91648117","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2802042319.pdf","grobid_xml":"https://content.openalex.org/works/W2802042319.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1598330724","https://openalex.org/W1936798282","https://openalex.org/W1963882359","https://openalex.org/W2069315453","https://openalex.org/W2077305340","https://openalex.org/W2099579348","https://openalex.org/W2111072639","https://openalex.org/W2117539524","https://openalex.org/W2118585731","https://openalex.org/W2134603844","https://openalex.org/W2135293965","https://openalex.org/W2141695047","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2243422958","https://openalex.org/W2544366167","https://openalex.org/W2552496680","https://openalex.org/W2613296094","https://openalex.org/W2793141496","https://openalex.org/W2994602700","https://openalex.org/W6677656871"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4298369531","https://openalex.org/W3155135229","https://openalex.org/W3040691452","https://openalex.org/W2592311268","https://openalex.org/W2144004426"],"abstract_inverted_index":{"The":[0,101,124],"success":[1],"of":[2,29,35,47,135,149,172],"Deep":[3],"Learning":[4,79],"models,":[5],"notably":[6],"convolutional":[7,173],"neural":[8,174],"networks":[9],"(CNNs),":[10],"makes":[11],"them":[12],"the":[13,27,33,45,51,132,140,144,157,167],"favorable":[14],"solution":[15],"for":[16,97,113,122,177],"object":[17],"recognition":[18,99],"systems":[19],"in":[20,32,147],"both":[21],"visible":[22],"and":[23,61,87,91,117,152],"infrared":[24],"domains.":[25],"However,":[26],"lack":[28],"training":[30,153,171],"data":[31],"case":[34],"maritime":[36,136],"ships":[37],"research":[38],"leads":[39],"to":[40,44,55,82,110,162],"poor":[41],"performance":[42,151],"due":[43],"problem":[46],"overfitting.":[48],"In":[49],"addition,":[50],"back-propagation":[52,169],"algorithm":[53,109],"used":[54],"train":[56,111],"CNN":[57,85,112],"is":[58,95,131,160],"very":[59],"slow":[60],"requires":[62],"tuning":[63],"many":[64],"hyperparameters.":[65],"To":[66],"overcome":[67],"these":[68],"weaknesses,":[69],"we":[70],"introduce":[71],"a":[72,89],"new":[73],"approach":[74,103,142],"fully":[75],"based":[76,107,120,170],"on":[77,127],"Extreme":[78],"Machine":[80],"(ELM)":[81],"learn":[83],"useful":[84],"features":[86,115,179],"perform":[88],"fast":[90],"accurate":[92],"classification,":[93],"which":[94,130],"suitable":[96],"infrared-based":[98],"systems.":[100],"proposed":[102,141,158],"combines":[104],"an":[105,118],"ELM":[106,119],"learning":[108],"discriminative":[114],"extraction":[116],"ensemble":[121],"classification.":[123],"experimental":[125],"results":[126],"VAIS":[128],"dataset,":[129],"largest":[133],"dataset":[134],"ships,":[137],"confirm":[138],"that":[139],"outperforms":[143],"state-of-the-art":[145],"models":[146],"term":[148],"generalization":[150],"speed.":[154],"For":[155],"instance,":[156],"model":[159],"up":[161],"950":[163],"times":[164],"faster":[165],"than":[166],"traditional":[168],"networks,":[175],"primarily":[176],"low-level":[178],"extraction.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}