{"id":"https://openalex.org/W4213427644","doi":"https://doi.org/10.1109/wsc52266.2021.9715470","title":"Data Farming Output Analysis Using Explainable AI","display_name":"Data Farming Output Analysis Using Explainable AI","publication_year":2021,"publication_date":"2021-12-12","ids":{"openalex":"https://openalex.org/W4213427644","doi":"https://doi.org/10.1109/wsc52266.2021.9715470"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc52266.2021.9715470","pdf_url":null,"source":{"id":"https://openalex.org/S4363607834","display_name":"2018 Winter Simulation Conference (WSC)","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5019425599","display_name":"Niclas Feldkamp","orcid":"https://orcid.org/0000-0002-7456-5077"},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Niclas Feldkamp","raw_affiliation_strings":["Group for Information Technology in Production and Logistics, Technische Universität Ilmenau,Ilmenau,GERMANY,98693","t Ilmenau, Ilmenau, GERMANY"],"affiliations":[{"raw_affiliation_string":"t Ilmenau, Ilmenau, GERMANY","institution_ids":["https://openalex.org/I119449181"]},{"raw_affiliation_string":"Group for Information Technology in Production and Logistics, Technische Universität Ilmenau,Ilmenau,GERMANY,98693","institution_ids":["https://openalex.org/I119449181"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5019425599"],"corresponding_institution_ids":["https://openalex.org/I119449181"],"apc_list":null,"apc_paid":null,"fwci":0.508,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.756396,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":78,"max":81},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9996,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9996,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9639,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9456,"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/black-box","display_name":"Black box","score":0.43637857}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77998877},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.68040717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5315357},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.49842358},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.43637857},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4313395},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4175651},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3525167},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32132608},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc52266.2021.9715470","pdf_url":null,"source":{"id":"https://openalex.org/S4363607834","display_name":"2018 Winter Simulation Conference (WSC)","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":33,"referenced_works":["https://openalex.org/W1864969757","https://openalex.org/W1984214132","https://openalex.org/W2102636708","https://openalex.org/W2125847307","https://openalex.org/W2148080122","https://openalex.org/W2213612645","https://openalex.org/W2282821441","https://openalex.org/W2745431423","https://openalex.org/W2788403449","https://openalex.org/W2809925683","https://openalex.org/W2891503716","https://openalex.org/W2958089299","https://openalex.org/W2962772482","https://openalex.org/W2962862931","https://openalex.org/W2964108846","https://openalex.org/W2973449430","https://openalex.org/W2981731882","https://openalex.org/W2999615587","https://openalex.org/W3088946003","https://openalex.org/W3093454618","https://openalex.org/W3098112554","https://openalex.org/W3125997628","https://openalex.org/W3126582233","https://openalex.org/W3144972196","https://openalex.org/W3145555567","https://openalex.org/W3145815558","https://openalex.org/W3151903439","https://openalex.org/W3174752098","https://openalex.org/W4230221692","https://openalex.org/W4234636779","https://openalex.org/W4238134598","https://openalex.org/W4300071243","https://openalex.org/W607674676"],"related_works":["https://openalex.org/W4394895745","https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4206777497","https://openalex.org/W4200136508","https://openalex.org/W3090563135","https://openalex.org/W2960264696","https://openalex.org/W2910064364","https://openalex.org/W2497432351","https://openalex.org/W1980614089"],"abstract_inverted_index":{"Data":[0,112],"Farming":[1,113],"combines":[2],"large-scale":[3],"simulation":[4,26],"experiments":[5],"with":[6],"high":[7],"performance":[8],"computing":[9],"and":[10,34,45,97],"sophisticated":[11],"big":[12],"data":[13,27,61],"analysis":[14,19,115],"methods.":[15],"The":[16],"portfolio":[17,110],"of":[18,25,42,53,72,78,80,95,111],"methods":[20,36,55,116],"for":[21,62],"those":[22,54],"large":[23],"amounts":[24],"still":[28],"yields":[29],"potential":[30],"to":[31,107],"further":[32],"development,":[33],"new":[35],"emerge":[37],"frequently.":[38],"Especially":[39],"the":[40,76,109],"application":[41],"machine":[43],"learning":[44],"artificial":[46,88],"intelligence":[47,89],"is":[48],"difficult,":[49],"since":[50],"a":[51,84,93],"lot":[52,94],"are":[56],"very":[57,100],"good":[58],"at":[59,66],"approximating":[60],"prediction,":[63],"but":[64],"less":[65],"actually":[67],"revealing":[68],"their":[69],"underlying":[70],"model":[71],"rules.":[73],"To":[74],"overcome":[75],"lack":[77],"comprehensibility":[79],"such":[81],"black-box":[82],"algorithms,":[83],"discipline":[85],"called":[86],"explainable":[87],"(XAI)":[90],"has":[91,98],"gained":[92],"traction":[96],"become":[99],"popular":[101],"recently.":[102],"This":[103],"paper":[104],"shows":[105],"how":[106],"extend":[108],"output":[114],"using":[117],"XAI.":[118]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4213427644","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-01-09T14:34:11.389194","created_date":"2022-02-25"}