{"id":"https://openalex.org/W2158325497","doi":"https://doi.org/10.1080/08839510802649408","title":"MODELING OF THIN FILM PROCESS DATA USING A GENETIC ALGORITHM-OPTIMIZED INITIAL WEIGHT OF BACKPROPAGATION NEURAL NETWORK","display_name":"MODELING OF THIN FILM PROCESS DATA USING A GENETIC ALGORITHM-OPTIMIZED INITIAL WEIGHT OF BACKPROPAGATION NEURAL NETWORK","publication_year":2009,"publication_date":"2009-02-04","ids":{"openalex":"https://openalex.org/W2158325497","doi":"https://doi.org/10.1080/08839510802649408","mag":"2158325497"},"language":"en","primary_location":{"id":"doi:10.1080/08839510802649408","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839510802649408","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5088194281","display_name":"Byungwhan Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Byungwhan Kim","raw_affiliation_strings":["Department of Electronic Engineering, Sejong University , Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Sejong University , Seoul, Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038240848","display_name":"Hwajune Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hwajune Lee","raw_affiliation_strings":["Department of Electronic Engineering, Sejong University , Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Sejong University , Seoul, Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100370751","display_name":"Donghwan Kim","orcid":"https://orcid.org/0000-0003-1411-8635"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Donghwan Kim","raw_affiliation_strings":["School of Mechanical Design & Automation Engineering, Seoul National University of Technology , Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Design & Automation Engineering, Seoul National University of Technology , Seoul, Korea","institution_ids":["https://openalex.org/I118373667"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088194281"],"corresponding_institution_ids":["https://openalex.org/I28777354"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":1.1323,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.81810567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"23","issue":"2","first_page":"168","last_page":"178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9923999905586243,"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"}},"topics":[{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9923999905586243,"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/T10781","display_name":"Plasma Diagnostics and Applications","score":0.9908999800682068,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9639000296592712,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.8163959383964539},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.814955472946167},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7602285146713257},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.7004677653312683},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6574651598930359},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4705864489078522},{"id":"https://openalex.org/keywords/factorial-experiment","display_name":"Factorial experiment","score":0.46747609972953796},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4493199586868286},{"id":"https://openalex.org/keywords/weight-distribution","display_name":"Weight distribution","score":0.43805238604545593},{"id":"https://openalex.org/keywords/biological-system","display_name":"Biological system","score":0.3910388946533203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38490766286849976},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3201926350593567},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23689532279968262},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06705722212791443}],"concepts":[{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.8163959383964539},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.814955472946167},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7602285146713257},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.7004677653312683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6574651598930359},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4705864489078522},{"id":"https://openalex.org/C169222746","wikidata":"https://www.wikidata.org/wiki/Q4116558","display_name":"Factorial experiment","level":2,"score":0.46747609972953796},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4493199586868286},{"id":"https://openalex.org/C25432639","wikidata":"https://www.wikidata.org/wiki/Q3711790","display_name":"Weight distribution","level":2,"score":0.43805238604545593},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.3910388946533203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38490766286849976},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3201926350593567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23689532279968262},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06705722212791443},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/08839510802649408","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839510802649408","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","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":20,"referenced_works":["https://openalex.org/W1497414364","https://openalex.org/W1963865236","https://openalex.org/W2020716741","https://openalex.org/W2022518571","https://openalex.org/W2025453252","https://openalex.org/W2029949252","https://openalex.org/W2048838419","https://openalex.org/W2067359195","https://openalex.org/W2074528568","https://openalex.org/W2099154409","https://openalex.org/W2099579348","https://openalex.org/W2101827854","https://openalex.org/W2130425902","https://openalex.org/W2152824077","https://openalex.org/W2904250082","https://openalex.org/W3011460294","https://openalex.org/W3023540311","https://openalex.org/W3207342693","https://openalex.org/W4232569293","https://openalex.org/W4300402905"],"related_works":["https://openalex.org/W2347477706","https://openalex.org/W4299389082","https://openalex.org/W1943663474","https://openalex.org/W2306830238","https://openalex.org/W2110294633","https://openalex.org/W2014162767","https://openalex.org/W2115729582","https://openalex.org/W2046083801","https://openalex.org/W2809283963","https://openalex.org/W2367260997"],"abstract_inverted_index":{"Artificial":[0],"neural":[1,6],"network,":[2],"particularly":[3],"the":[4,32,37,49,72,91,166,175,178],"backpropagation":[5],"network":[7],"(BPNN),":[8],"has":[9],"been":[10],"used":[11],"to":[12,47,94,138],"construct":[13],"a":[14,22,45,79,126],"prediction":[15,54,148],"model":[16,95],"of":[17,74,114,133,149],"plasma":[18],"processes.":[19],"In":[20,42],"building":[21],"BPNN":[23,53,189],"model,":[24],"many":[25],"training":[26],"factors":[27],"are":[28],"typically":[29],"involved":[30],"and":[31,90,105,118],"most":[33],"difficult":[34],"factor":[35],"is":[36,56,181],"initial":[38],"weight":[39],"distribution":[40],"(IWD).":[41],"this":[43],"study,":[44],"technique":[46,180],"optimize":[48],"IWD":[50,186],"effect":[51,113,187],"on":[52,188],"performance":[55],"presented.":[57],"This":[58],"was":[59,87,121,158],"accomplished":[60],"by":[61,124],"using":[62],"genetic":[63],"algorithm":[64],"(GA).":[65],"The":[66,84,112,131,143,156,171],"experimental":[67],"data":[68],"were":[69,136],"collected":[70],"from":[71],"etching":[73],"silica":[75,97,106],"thin":[76],"films":[77],"in":[78,183],"CHF3-CF4":[80],"inductively":[81],"coupled":[82],"plasma.":[83],"etch":[85,92,98,101,154,168],"process":[86],"statistically":[88],"characterized":[89],"responses":[93],"include":[96],"rate,":[99,102],"Al":[100,103,167],"selectivity,":[104],"profile":[107],"angle":[108],"denoted":[109],"as":[110],"anisotropy.":[111],"GA":[115],"parameters":[116],"(mutation":[117],"crossover":[119],"probabilities)":[120],"also":[122],"evaluated":[123],"conducting":[125],"42":[127],"full":[128],"factorial":[129],"experiment.":[130],"performances":[132],"GA-BPNN":[134,150],"models":[135,151],"compared":[137],"those":[139],"for":[140,152,163],"conventional":[141],"models.":[142],"comparison":[144],"revealed":[145],"an":[146,185],"improved":[147],"all":[153,164],"responses.":[155],"improvement":[157],"even":[159],"more":[160],"than":[161],"15%":[162],"but":[165],"rate":[169],"data.":[170],"proven":[172],"improvements":[173],"support":[174],"finding":[176],"that":[177],"presented":[179],"effective":[182],"optimizing":[184],"modeling.":[190]},"counts_by_year":[{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}