{"id":"https://openalex.org/W2889396243","doi":"https://doi.org/10.1007/978-3-319-99978-4","title":"Artificial Neural Networks in Pattern Recognition","display_name":"Artificial Neural Networks in Pattern Recognition","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2889396243","doi":"https://doi.org/10.1007/978-3-319-99978-4","mag":"2889396243"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-99978-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-99978-4","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book"},"type":"book","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-319-99978-4","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"ANNPR 2018 Siena","orcid":null},"institutions":[{"id":"https://openalex.org/I102064193","display_name":"University of Siena","ror":"https://ror.org/01tevnk56","country_code":"IT","type":"education","lineage":["https://openalex.org/I102064193"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"ANNPR 2018 Siena","raw_affiliation_strings":["University of Siena, Siena, Italy"],"affiliations":[{"raw_affiliation_string":"University of Siena, Siena, Italy","institution_ids":["https://openalex.org/I102064193"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108114633","display_name":"Luca Pancioni","orcid":null},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pancioni, Luca","raw_affiliation_strings":["Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078096723","display_name":"Friedhelm Schwenker","orcid":"https://orcid.org/0000-0001-5118-0812"},"institutions":[{"id":"https://openalex.org/I102064193","display_name":"University of Siena","ror":"https://ror.org/01tevnk56","country_code":"IT","type":"education","lineage":["https://openalex.org/I102064193"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Schwenker, Friedhelm","raw_affiliation_strings":["University of Siena, Siena, Italy"],"affiliations":[{"raw_affiliation_string":"University of Siena, Siena, Italy","institution_ids":["https://openalex.org/I102064193"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053948915","display_name":"Edmondo Trentin","orcid":"https://orcid.org/0000-0003-2197-0703"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trentin, Edmondo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102064193"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":48.1571,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.99677179,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10818","display_name":"Astrophysics and Cosmic Phenomena","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10818","display_name":"Astrophysics and Cosmic Phenomena","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11044","display_name":"Particle Detector Development and Performance","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10048","display_name":"Particle physics theoretical and experimental studies","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.826930046081543},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7468675374984741},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7149830460548401},{"id":"https://openalex.org/keywords/petabyte","display_name":"Petabyte","score":0.6859560012817383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6322590708732605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47749751806259155},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4667627513408661},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46148696541786194},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.26565253734588623},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.12332633137702942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.826930046081543},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7468675374984741},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7149830460548401},{"id":"https://openalex.org/C13600138","wikidata":"https://www.wikidata.org/wiki/Q8799","display_name":"Petabyte","level":3,"score":0.6859560012817383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6322590708732605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47749751806259155},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4667627513408661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46148696541786194},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26565253734588623},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.12332633137702942}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/978-3-319-99978-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-99978-4","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book"},{"id":"pmh:oai:arXiv.org:1810.00592","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.00592","pdf_url":"https://arxiv.org/pdf/1810.00592","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1007/978-3-319-99978-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-99978-4","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book"},"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1663973292","https://openalex.org/W1669203936","https://openalex.org/W1836465849","https://openalex.org/W1986805421","https://openalex.org/W1992634610","https://openalex.org/W2009086942","https://openalex.org/W2021677098","https://openalex.org/W2059743763","https://openalex.org/W2060930136","https://openalex.org/W2073528560","https://openalex.org/W2082377359","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2116159083","https://openalex.org/W2145535735","https://openalex.org/W2160111546","https://openalex.org/W2162931300","https://openalex.org/W2163605009","https://openalex.org/W2271840356","https://openalex.org/W2523246573","https://openalex.org/W2557750643","https://openalex.org/W2576915720","https://openalex.org/W2617552603","https://openalex.org/W2751935654","https://openalex.org/W2754317782","https://openalex.org/W2768783066","https://openalex.org/W2795194658","https://openalex.org/W2870589900","https://openalex.org/W2883859163","https://openalex.org/W2889269551","https://openalex.org/W2889364221","https://openalex.org/W2949117887","https://openalex.org/W2951063581","https://openalex.org/W2951320766","https://openalex.org/W2962686583","https://openalex.org/W2963866993","https://openalex.org/W2964121744","https://openalex.org/W2991618507","https://openalex.org/W3099703557","https://openalex.org/W3100063856","https://openalex.org/W3121470950","https://openalex.org/W3170727651","https://openalex.org/W4235765578"],"related_works":["https://openalex.org/W1538652242","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4380075502"],"abstract_inverted_index":{"The":[0,115,127],"Cherenkov":[1],"Telescope":[2],"Array":[3],"(CTA)":[4],"will":[5,25],"be":[6,75,138],"the":[7,22,31,36,47,91,151,162],"world's":[8],"leading":[9],"ground-based":[10],"gamma-ray":[11,113,142],"observatory":[12],"allowing":[13],"us":[14,148],"to":[15,39,46,74,97,108,140,149],"study":[16],"very":[17],"high":[18,104],"energy":[19],"phenomena":[20],"in":[21,61,77,161],"Universe.":[23],"CTA":[24],"produce":[26,81],"huge":[27],"data":[28,43,82,123,136,159],"sets,":[29],"of":[30,33,93,125,154,157],"order":[32],"petabytes,":[34],"and":[35,80,107,119],"challenge":[37],"is":[38],"find":[40],"better":[41],"alternative":[42],"analysis":[44],"methods":[45,69],"already":[48],"existing":[49],"ones.":[50],"Machine":[51],"learning":[52,56],"algorithms,":[53],"like":[54],"deep":[55],"techniques,":[57],"give":[58],"encouraging":[59],"results":[60,128],"this":[62],"direction.":[63],"In":[64],"particular,":[65],"convolutional":[66,94],"neural":[67,95,131],"network":[68],"on":[70,121],"images":[71,102],"have":[72],"proven":[73],"effective":[76],"pattern":[78],"recognition":[79],"representations":[83],"which":[84],"can":[85,137],"achieve":[86],"satisfactory":[87],"predictions.":[88],"We":[89],"test":[90],"use":[92,153],"networks":[96,116,132,145],"discriminate":[98],"signal":[99],"from":[100,112],"background":[101],"with":[103,134],"rejections":[105],"factors":[106],"provide":[109],"reconstruction":[110],"parameters":[111],"events.":[114],"are":[117],"trained":[118,133],"evaluated":[120],"artificial":[122],"sets":[124],"images.":[126],"show":[129],"that":[130],"simulated":[135],"useful":[139],"extract":[141],"information.":[143],"Such":[144],"would":[146],"help":[147],"make":[150],"best":[152],"large":[155],"quantities":[156],"real":[158],"coming":[160],"next":[163],"decades.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2018-09-07T00:00:00"}