{"id":"https://openalex.org/W2909621870","doi":"https://doi.org/10.1109/aiccsa.2018.8612875","title":"Deep Learning Experiments with Skewed Data for Defect Prediction in Plastic Injection Molding","display_name":"Deep Learning Experiments with Skewed Data for Defect Prediction in Plastic Injection Molding","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2909621870","doi":"https://doi.org/10.1109/aiccsa.2018.8612875","mag":"2909621870"},"language":"en","primary_location":{"id":"doi:10.1109/aiccsa.2018.8612875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiccsa.2018.8612875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)","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/A5087047409","display_name":"Seongwoo Kim","orcid":"https://orcid.org/0000-0002-8929-3932"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongwoo Kim","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100417272","display_name":"Se Young Kim","orcid":"https://orcid.org/0000-0001-9188-868X"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seyoung Kim","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082994692","display_name":"Kwang Ryel Ryu","orcid":"https://orcid.org/0000-0003-1770-0129"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwang Ryel Ryu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5128,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66794492,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12080","display_name":"Injection Molding Process and Properties","score":0.9980999827384949,"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/T12080","display_name":"Injection Molding Process and Properties","score":0.9980999827384949,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9581999778747559,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.909500002861023,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.6651455163955688},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6352217793464661},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5861460566520691},{"id":"https://openalex.org/keywords/molding","display_name":"Molding (decorative)","score":0.5847269892692566},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5846906304359436},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5587131381034851},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5187489986419678},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5118467211723328},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5050137639045715},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49918103218078613},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4817202687263489},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4236331284046173},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3251492381095886},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10444128513336182},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09123039245605469}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6651455163955688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6352217793464661},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5861460566520691},{"id":"https://openalex.org/C67558686","wikidata":"https://www.wikidata.org/wiki/Q1770806","display_name":"Molding (decorative)","level":2,"score":0.5847269892692566},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5846906304359436},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5587131381034851},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5187489986419678},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5118467211723328},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5050137639045715},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49918103218078613},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4817202687263489},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4236331284046173},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3251492381095886},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10444128513336182},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09123039245605469},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aiccsa.2018.8612875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiccsa.2018.8612875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2136848157"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4387163678","https://openalex.org/W4288108708","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W3173606726"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,75],"investigate":[4],"the":[5,70,100],"possibility":[6],"of":[7,29,94,102],"predicting":[8],"defects":[9,65],"in":[10,45],"plastic":[11],"injection":[12],"molding":[13,25],"by":[14],"learning":[15,57],"predictive":[16],"models":[17],"from":[18,23],"time-series":[19,62],"process":[20],"data":[21],"collected":[22],"a":[24,55,79,85],"machine.":[26],"The":[27],"model":[28,38,50,58],"our":[30,103],"choice":[31],"is":[32,51,72],"an":[33,97],"RNN":[34],"(recursive":[35],"neural":[36],"network)":[37],"using":[39],"LSTM":[40],"(long":[41],"short-term":[42],"memory)":[43],"units":[44],"its":[46],"hidden":[47],"layer.":[48],"This":[49],"well":[52],"known":[53],"as":[54],"deep":[56],"specialized":[59],"for":[60],"processing":[61],"data.":[63],"Since":[64],"are":[66],"rare":[67],"and":[68,96],"thus":[69],"dataset":[71],"highly":[73],"skewed,":[74],"try":[76],"to":[77,99],"achieve":[78],"high":[80,86],"average":[81],"recall":[82],"rather":[83],"than":[84],"classification":[87],"accuracy.":[88],"We":[89],"give":[90],"some":[91],"initial":[92],"results":[93],"experiments":[95],"outlook":[98],"direction":[101],"future":[104],"works.":[105]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
