{"id":"https://openalex.org/W4292313505","doi":"https://doi.org/10.3390/s22166129","title":"Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets","display_name":"Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets","publication_year":2022,"publication_date":"2022-08-16","ids":{"openalex":"https://openalex.org/W4292313505","doi":"https://doi.org/10.3390/s22166129","pmid":"https://pubmed.ncbi.nlm.nih.gov/36015898"},"language":"en","primary_location":{"id":"doi:10.3390/s22166129","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22166129","pdf_url":"https://www.mdpi.com/1424-8220/22/16/6129/pdf?version=1660661156","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/16/6129/pdf?version=1660661156","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007729428","display_name":"Loris Nanni","orcid":"https://orcid.org/0000-0002-3502-7209"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Loris Nanni","raw_affiliation_strings":["Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062894420","display_name":"Sheryl Brahnam","orcid":"https://orcid.org/0000-0001-7664-6930"},"institutions":[{"id":"https://openalex.org/I119942284","display_name":"Missouri State University","ror":"https://ror.org/01d2sez20","country_code":"US","type":"education","lineage":["https://openalex.org/I119942284"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sheryl Brahnam","raw_affiliation_strings":["Department of Information Technology and Cybersecurity, Missouri State University, 901 S. National Street, Springfield, MO 65804, USA"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology and Cybersecurity, Missouri State University, 901 S. National Street, Springfield, MO 65804, USA","institution_ids":["https://openalex.org/I119942284"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062279764","display_name":"Michelangelo Paci","orcid":"https://orcid.org/0000-0003-0510-1444"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Michelangelo Paci","raw_affiliation_strings":["BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Arvo Ylp\u00f6n katu 34, D 219, FI-33520 Tampere, Finland"],"affiliations":[{"raw_affiliation_string":"BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Arvo Ylp\u00f6n katu 34, D 219, FI-33520 Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046852066","display_name":"Stefano Ghidoni","orcid":"https://orcid.org/0000-0003-3406-8719"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Stefano Ghidoni","raw_affiliation_strings":["Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5062894420"],"corresponding_institution_ids":["https://openalex.org/I119942284"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":4.0434,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.94491412,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"22","issue":"16","first_page":"6129","last_page":"6129"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9980999827384949,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9939000010490417,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8343285918235779},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7981907725334167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7324679493904114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5947489738464355},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5837850570678711},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5552322268486023},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5093158483505249},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5004594326019287},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4483257830142975},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.41702479124069214},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41279804706573486},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41064733266830444},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3856489360332489},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.268757700920105},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10271376371383667}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8343285918235779},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7981907725334167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7324679493904114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5947489738464355},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5837850570678711},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5552322268486023},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5093158483505249},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5004594326019287},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4483257830142975},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.41702479124069214},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41279804706573486},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41064733266830444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3856489360332489},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.268757700920105},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10271376371383667},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D003952","descriptor_name":"Diagnostic Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003952","descriptor_name":"Diagnostic Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003952","descriptor_name":"Diagnostic Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":7,"locations":[{"id":"doi:10.3390/s22166129","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22166129","pdf_url":"https://www.mdpi.com/1424-8220/22/16/6129/pdf?version=1660661156","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36015898","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36015898","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:e67d4b0864e0468b85fae5109d6e9ce2","is_oa":true,"landing_page_url":"https://doaj.org/article/e67d4b0864e0468b85fae5109d6e9ce2","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 16, p 6129 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/16/6129/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22166129","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 16; Pages: 6129","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9415767","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9415767","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:trepo.tuni.fi:10024/223426","is_oa":true,"landing_page_url":"https://trepo.tuni.fi/handle/10024/223426","pdf_url":null,"source":{"id":"https://openalex.org/S7407055260","display_name":"Trepo - Institutional Repository of Tampere University","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:www.research.unipd.it:11577/3454737","is_oa":true,"landing_page_url":"https://hdl.handle.net/11577/3454737","pdf_url":null,"source":{"id":"https://openalex.org/S4377196283","display_name":"Research Padua  Archive (University of Padua)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.3390/s22166129","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22166129","pdf_url":"https://www.mdpi.com/1424-8220/22/16/6129/pdf?version=1660661156","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292313505.pdf","grobid_xml":"https://content.openalex.org/works/W4292313505.grobid-xml"},"referenced_works_count":77,"referenced_works":["https://openalex.org/W150571132","https://openalex.org/W1565746575","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1979010656","https://openalex.org/W2000266578","https://openalex.org/W2014915963","https://openalex.org/W2079579181","https://openalex.org/W2112385576","https://openalex.org/W2145752049","https://openalex.org/W2147377972","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2201305792","https://openalex.org/W2311607323","https://openalex.org/W2435090885","https://openalex.org/W2622826443","https://openalex.org/W2758308612","https://openalex.org/W2759912964","https://openalex.org/W2786627679","https://openalex.org/W2884367402","https://openalex.org/W2886848602","https://openalex.org/W2905441841","https://openalex.org/W2905483812","https://openalex.org/W2951934944","https://openalex.org/W2954932437","https://openalex.org/W2962907613","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2984906359","https://openalex.org/W2996367417","https://openalex.org/W2999711171","https://openalex.org/W3006423879","https://openalex.org/W3006434062","https://openalex.org/W3007943565","https://openalex.org/W3008480154","https://openalex.org/W3010968125","https://openalex.org/W3011037064","https://openalex.org/W3020996329","https://openalex.org/W3033157674","https://openalex.org/W3043683372","https://openalex.org/W3045415942","https://openalex.org/W3083622693","https://openalex.org/W3089733125","https://openalex.org/W3093468414","https://openalex.org/W3106331693","https://openalex.org/W3106717751","https://openalex.org/W3108473457","https://openalex.org/W3113962394","https://openalex.org/W3114589555","https://openalex.org/W3120894151","https://openalex.org/W3124539583","https://openalex.org/W3127426043","https://openalex.org/W3128882947","https://openalex.org/W3135034047","https://openalex.org/W3135876408","https://openalex.org/W3152539471","https://openalex.org/W3156417449","https://openalex.org/W3157749950","https://openalex.org/W3160262995","https://openalex.org/W3164844993","https://openalex.org/W3171056019","https://openalex.org/W3171873561","https://openalex.org/W3182985353","https://openalex.org/W3192169136","https://openalex.org/W3195815524","https://openalex.org/W3196396697","https://openalex.org/W3201913426","https://openalex.org/W3213404038","https://openalex.org/W3215726887","https://openalex.org/W4214728635","https://openalex.org/W6682889407","https://openalex.org/W6698064614","https://openalex.org/W6725739302","https://openalex.org/W6739879593","https://openalex.org/W6921805399"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W2786764570","https://openalex.org/W4309960894","https://openalex.org/W2728804654"],"abstract_inverted_index":{"CNNs":[0,54,110,182],"and":[1,24,37,158,184],"other":[2],"deep":[3],"learners":[4],"are":[5,140],"now":[6],"state-of-the-art":[7],"in":[8,26,43,88,106,200],"medical":[9,19,30,168],"imaging":[10],"research.":[11],"However,":[12],"the":[13,44,65,77,89,103,124,144,198],"small":[14],"sample":[15],"size":[16],"of":[17,46,52,67,79,109,115,126,133,138,202],"many":[18,86],"data":[20,169],"sets":[21,170],"dampens":[22],"performance":[23,125,201],"results":[25],"overfitting.":[27],"In":[28],"some":[29],"areas,":[31],"it":[32],"is":[33,55,100],"simply":[34],"too":[35],"labor-intensive":[36],"expensive":[38],"to":[39,70,101,122],"amass":[40],"images":[41],"numbering":[42],"hundreds":[45],"thousands.":[47],"Building":[48],"Deep":[49],"CNN":[50],"ensembles":[51],"pre-trained":[53],"one":[56],"powerful":[57],"method":[58,74,95,163],"for":[59,143],"overcoming":[60],"this":[61,203],"problem.":[62],"Ensembles":[63],"combine":[64],"outputs":[66],"multiple":[68],"classifiers":[69],"improve":[71],"performance.":[72],"This":[73,119],"relies":[75],"on":[76,85,166],"introduction":[78],"diversity,":[80],"which":[81,139],"can":[82],"be":[83],"introduced":[84],"levels":[87],"classification":[90,173],"workflow.":[91],"A":[92],"recent":[93],"ensembling":[94],"that":[96],"has":[97],"shown":[98],"promise":[99],"vary":[102],"activation":[104,189],"functions":[105],"a":[107,116,130],"set":[108,132],"or":[111],"within":[112],"different":[113],"layers":[114,190],"single":[117],"CNN.":[118],"study":[120],"aims":[121],"examine":[123],"both":[127],"methods":[128],"using":[129],"large":[131],"twenty":[134],"activations":[135],"functions,":[136],"six":[137],"presented":[141],"here":[142],"first":[145],"time:":[146],"2D":[147],"Mexican":[148],"ReLU,":[149],"TanELU,":[150],"MeLU":[151],"+":[152],"GaLU,":[153,157],"Symmetric":[154,156],"MeLU,":[155],"Flexible":[159],"MeLU.":[160],"The":[161,175],"proposed":[162],"was":[164],"tested":[165],"fifteen":[167],"representing":[171],"various":[172],"tasks.":[174],"best":[176],"performing":[177],"ensemble":[178],"combined":[179],"two":[180],"well-known":[181],"(VGG16":[183],"ResNet50)":[185],"whose":[186],"standard":[187],"ReLU":[188],"were":[191],"randomly":[192],"replaced":[193],"with":[194],"another.":[195],"Results":[196],"demonstrate":[197],"superiority":[199],"approach.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-12T07:58:50.170612","created_date":"2025-10-10T00:00:00"}