{"id":"https://openalex.org/W2898738021","doi":"https://doi.org/10.3233/jifs-171979","title":"Quantitative analysis of LIBS spectra using hybrid chemometric models through fusion of extreme learning machines and support vector regression","display_name":"Quantitative analysis of LIBS spectra using hybrid chemometric models through fusion of extreme learning machines and support vector regression","publication_year":2018,"publication_date":"2018-10-29","ids":{"openalex":"https://openalex.org/W2898738021","doi":"https://doi.org/10.3233/jifs-171979","mag":"2898738021"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-171979","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-171979","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-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/A5038097688","display_name":"Taoreed O. Owolabi","orcid":"https://orcid.org/0000-0002-6666-1755"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Taoreed O. Owolabi","raw_affiliation_strings":["Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum &amp; Minerals, Dhahran, Saudi Arabia","Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum &amp; Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]},{"raw_affiliation_string":"Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041189347","display_name":"M.A. Gondal","orcid":null},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"Mohammed A. Gondal","raw_affiliation_strings":["Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum &amp; Minerals, Dhahran, Saudi Arabia","Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum &amp; Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]},{"raw_affiliation_string":"Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041189347"],"corresponding_institution_ids":["https://openalex.org/I134085113"],"apc_list":null,"apc_paid":null,"fwci":2.9139,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.90260471,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"35","issue":"6","first_page":"6277","last_page":"6286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11854","display_name":"Laser-induced spectroscopy and plasma","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T11854","display_name":"Laser-induced spectroscopy and plasma","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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":0.9771999716758728,"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"}},{"id":"https://openalex.org/T10180","display_name":"Analytical chemistry methods development","score":0.972599983215332,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.8279619812965393},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7372845411300659},{"id":"https://openalex.org/keywords/laser-induced-breakdown-spectroscopy","display_name":"Laser-induced breakdown spectroscopy","score":0.708770751953125},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6280900239944458},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5608469843864441},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5218837857246399},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4993886947631836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4335108995437622},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.43294280767440796},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3770318329334259},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3325827717781067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2919192910194397},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2509099245071411},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2433604598045349},{"id":"https://openalex.org/keywords/laser","display_name":"Laser","score":0.1667608618736267},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09248876571655273},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06942665576934814},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.06892809271812439}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.8279619812965393},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7372845411300659},{"id":"https://openalex.org/C50497907","wikidata":"https://www.wikidata.org/wiki/Q2654990","display_name":"Laser-induced breakdown spectroscopy","level":3,"score":0.708770751953125},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6280900239944458},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5608469843864441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5218837857246399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4993886947631836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4335108995437622},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.43294280767440796},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3770318329334259},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3325827717781067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2919192910194397},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2509099245071411},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2433604598045349},{"id":"https://openalex.org/C520434653","wikidata":"https://www.wikidata.org/wiki/Q38867","display_name":"Laser","level":2,"score":0.1667608618736267},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09248876571655273},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06942665576934814},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.06892809271812439},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-171979","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-171979","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W784798348","https://openalex.org/W1795792415","https://openalex.org/W1847241526","https://openalex.org/W1964357740","https://openalex.org/W1982648210","https://openalex.org/W1982736658","https://openalex.org/W1995595573","https://openalex.org/W2003169788","https://openalex.org/W2004257838","https://openalex.org/W2004365213","https://openalex.org/W2004559529","https://openalex.org/W2008710965","https://openalex.org/W2026006180","https://openalex.org/W2026818511","https://openalex.org/W2028759731","https://openalex.org/W2029372760","https://openalex.org/W2040604977","https://openalex.org/W2046224359","https://openalex.org/W2046714498","https://openalex.org/W2048612196","https://openalex.org/W2049713766","https://openalex.org/W2055817716","https://openalex.org/W2057176608","https://openalex.org/W2058501695","https://openalex.org/W2059468946","https://openalex.org/W2061151786","https://openalex.org/W2062065599","https://openalex.org/W2062174987","https://openalex.org/W2067373532","https://openalex.org/W2068367623","https://openalex.org/W2070101089","https://openalex.org/W2072955302","https://openalex.org/W2077532639","https://openalex.org/W2087833865","https://openalex.org/W2092504664","https://openalex.org/W2095495267","https://openalex.org/W2106582661","https://openalex.org/W2111072639","https://openalex.org/W2117905123","https://openalex.org/W2122040390","https://openalex.org/W2127548927","https://openalex.org/W2132355204","https://openalex.org/W2156909104","https://openalex.org/W2181985983","https://openalex.org/W2259799082","https://openalex.org/W2336998947","https://openalex.org/W2346757497","https://openalex.org/W2406298076","https://openalex.org/W2423310949","https://openalex.org/W2442689499","https://openalex.org/W2465791242","https://openalex.org/W2466636344","https://openalex.org/W2477632872","https://openalex.org/W2501843550","https://openalex.org/W2520827189","https://openalex.org/W2534097401","https://openalex.org/W2537529341","https://openalex.org/W2600119064","https://openalex.org/W2615862980","https://openalex.org/W2621411457","https://openalex.org/W2731219726","https://openalex.org/W2735287976","https://openalex.org/W2735999701","https://openalex.org/W2753945947","https://openalex.org/W2787698007","https://openalex.org/W2792458396","https://openalex.org/W2800501027","https://openalex.org/W6957695041"],"related_works":["https://openalex.org/W2345184372","https://openalex.org/W3013515612","https://openalex.org/W2136184105","https://openalex.org/W3185179407","https://openalex.org/W2187500075","https://openalex.org/W2041399278","https://openalex.org/W2160451891","https://openalex.org/W2336974148","https://openalex.org/W2056016498","https://openalex.org/W2277768259"],"abstract_inverted_index":{"Laser":[0],"induced":[1,79],"breakdown":[2],"spectroscopy":[3],"(LIBS)":[4],"is":[5,52,62,98,157],"an":[6],"excellent":[7],"technique":[8],"for":[9,54,90,132,231],"analysis":[10,134,234],"of":[11,24,42,112,117,122,135,138,145,152,161,168,175,189,198,213,221,228,242],"solid":[12],"and":[13,47,108,130,207,216,237],"liquid":[14],"samples.":[15,142],"However":[16],"there":[17],"are":[18,125,179],"inherent":[19,69],"problems":[20],"with":[21,31,210],"concentration":[22],"determination":[23],"elements":[25],"present":[26],"in":[27,127,235],"the":[28,55,77,94,110,150,166,176,187,222,243],"test":[29],"sample":[30],"better":[32,203],"accuracy.":[33],"In":[34],"order":[35],"to":[36,71,159],"address":[37],"this":[38,128],"challenge,":[39],"hybrid":[40,224],"fusion":[41],"extreme":[43],"learning":[44,59,106],"machine":[45,60],"(ELM)":[46,61],"support":[48],"vector":[49],"regression":[50],"(SVR)":[51],"proposed":[53,177,223],"first":[56],"time.":[57],"Extreme":[58],"a":[63,99,196],"non-linear":[64,74,100],"chemo-metric":[65,101],"method":[66],"which":[67,86,148,164],"has":[68],"capacity":[70],"approximate":[72],"any":[73],"relation":[75],"describing":[76],"laser":[78],"plasma.":[80],"However,":[81],"ELM":[82,153,206],"surfers":[83],"from":[84],"over-fitting":[85,113],"affects":[87],"its":[88,118,155,171],"accuracy":[89,220],"spectroscopic":[91],"regression.":[92],"On":[93,186],"other":[95],"hand,":[96],"SVR":[97,169],"tool":[102],"based":[103],"on":[104],"statistical":[105],"theory":[107],"overcomes":[109],"problem":[111],"by":[114],"proper":[115],"tuning":[116],"hyper-parameters.":[119],"The":[120,143,173,219],"merits":[121],"both":[123],"chemo-metrics":[124],"harnessed":[126],"work":[129],"implemented":[131],"quantitative":[133,233],"LIBS":[136,236],"spectra":[137],"seven":[139],"standard":[140],"bronze":[141],"performance":[144,211],"ELM-SVR":[146,201],"model":[147,163,199,209],"uses":[149],"output":[151,167],"as":[154,170,195],"input":[156],"compared":[158],"that":[160],"SVR-ELM":[162,208],"takes":[165],"input.":[172],"hyper-parameters":[174],"models":[178,225],"optimized":[180],"using":[181],"gravitational":[182],"search":[183],"algorithm":[184],"(GSA).":[185],"bases":[188],"root":[190],"mean":[191],"square":[192],"error":[193],"(RMSE)":[194],"measure":[197],"performance,":[200],"performs":[202],"than":[204],"SVR,":[205],"improvement":[212],"95.76%,":[214],"89.33%":[215],"52.71%,":[217],"respectively.":[218],"would":[226],"be":[227],"immense":[229],"significance":[230],"quick":[232],"eventually":[238],"promotes":[239],"wide":[240],"applicability":[241],"technique.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
