{"id":"https://openalex.org/W2561184891","doi":"https://doi.org/10.1109/dicta.2016.7797053","title":"Impact of Automatic Feature Extraction in Deep Learning Architecture","display_name":"Impact of Automatic Feature Extraction in Deep Learning Architecture","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2561184891","doi":"https://doi.org/10.1109/dicta.2016.7797053","mag":"2561184891"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2016.7797053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2016.7797053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Impact_of_automatic_feature_extraction_in_deep_learning_architecture/13440848","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025281245","display_name":"Fatma Shaheen","orcid":null},"institutions":[{"id":"https://openalex.org/I74899385","display_name":"Central Queensland University","ror":"https://ror.org/023q4bk22","country_code":"AU","type":"education","lineage":["https://openalex.org/I74899385"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fatma Shaheen","raw_affiliation_strings":["Centre for Intelligent Systems, Central Queensland University, Brisbane, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Intelligent Systems, Central Queensland University, Brisbane, Australia","institution_ids":["https://openalex.org/I74899385"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026528338","display_name":"Brijesh Verma","orcid":"https://orcid.org/0000-0002-4618-0479"},"institutions":[{"id":"https://openalex.org/I74899385","display_name":"Central Queensland University","ror":"https://ror.org/023q4bk22","country_code":"AU","type":"education","lineage":["https://openalex.org/I74899385"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Brijesh Verma","raw_affiliation_strings":["Centre for Intelligent Systems, Central Queensland University, Brisbane, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Intelligent Systems, Central Queensland University, Brisbane, Australia","institution_ids":["https://openalex.org/I74899385"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014752949","display_name":"Md Asafuddoula","orcid":"https://orcid.org/0000-0003-4102-6349"},"institutions":[{"id":"https://openalex.org/I74899385","display_name":"Central Queensland University","ror":"https://ror.org/023q4bk22","country_code":"AU","type":"education","lineage":["https://openalex.org/I74899385"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Md. Asafuddoula","raw_affiliation_strings":["Centre for Intelligent Systems, Central Queensland University, Brisbane, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Intelligent Systems, Central Queensland University, Brisbane, Australia","institution_ids":["https://openalex.org/I74899385"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74899385"],"apc_list":null,"apc_paid":null,"fwci":2.1541,"has_fulltext":false,"cited_by_count":145,"citation_normalized_percentile":{"value":0.91980221,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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":0.9968000054359436,"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/feature-extraction","display_name":"Feature extraction","score":0.8452831506729126},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8358561396598816},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.809208869934082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7860842943191528},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7403514981269836},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7145595550537109},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6501029133796692},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4893459677696228},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4886956512928009},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.47775784134864807},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4479217827320099},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4389357268810272},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4364390969276428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38449588418006897}],"concepts":[{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.8452831506729126},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8358561396598816},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.809208869934082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7860842943191528},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7403514981269836},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7145595550537109},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6501029133796692},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4893459677696228},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4886956512928009},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.47775784134864807},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4479217827320099},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4389357268810272},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4364390969276428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38449588418006897},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/dicta.2016.7797053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2016.7797053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},{"id":"pmh:oai:acquire.cqu.edu.au:cqu:13834","is_oa":false,"landing_page_url":"http://hdl.cqu.edu.au/10018/1211456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400635","display_name":"Acquire (CQUniversity)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I74899385","host_organization_name":"Central Queensland University","host_organization_lineage":["https://openalex.org/I74899385"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Shaheen, F, Verma, B, Asafuddoula, M, (2016), Impact of automatic feature extraction in deep learning architecture, 29-02 December 2016, International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016) 2016, Gold Coast, Australia, IEEE, Piscataway, NJ., p. 638-645, http://dx.doi.org/10.1109/DICTA.2016.7797053, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7794373","raw_type":"conference"},{"id":"pmh:oai:figshare.com:article/13440848","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Impact_of_automatic_feature_extraction_in_deep_learning_architecture/13440848","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"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":"","raw_type":"Conference contribution"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/13440848","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Impact_of_automatic_feature_extraction_in_deep_learning_architecture/13440848","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"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":"","raw_type":"Conference contribution"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1484210532","https://openalex.org/W1576935677","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1947481528","https://openalex.org/W1982479097","https://openalex.org/W1988452762","https://openalex.org/W1999156278","https://openalex.org/W2076063813","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2163605009","https://openalex.org/W2267038056","https://openalex.org/W2286859775","https://openalex.org/W2294556236","https://openalex.org/W2302255633","https://openalex.org/W6628927728","https://openalex.org/W6637373629","https://openalex.org/W6645810757","https://openalex.org/W6684191040","https://openalex.org/W6693581965","https://openalex.org/W6698183232"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2136232598"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"the":[3,143],"impact":[4],"of":[5,122,179],"automatic":[6,65,113],"feature":[7,66,76,107,114,138,153,163],"extraction":[8,67,77,108,115,139,154],"used":[9],"in":[10,68,155,202],"a":[11,25,43,101,120,127,161],"deep":[12],"learning":[13],"architecture":[14],"such":[15],"as":[16,96,196,198],"Convolutional":[17],"Neural":[18],"Network":[19],"(CNN).":[20],"Recently":[21],"CNN":[22,47,69,111,156,199],"has":[23],"become":[24],"very":[26],"popular":[27],"tool":[28],"for":[29],"image":[30,168],"classification":[31],"which":[32,62,84],"can":[33,48,194],"automatically":[34],"extract":[35],"features,":[36],"learn":[37],"and":[38,79,125,136,166,181],"classify":[39],"them.":[40],"It":[41],"is":[42,58,70,81,109,116,149],"common":[44],"belief":[45],"that":[46,64,86,188],"always":[49],"perform":[50,195],"better":[51,72,159,201],"than":[52,73,160],"other":[53,74,87],"well-known":[54],"classifiers.":[55],"However,":[56],"there":[57,80],"no":[59,82],"systematic":[60,102],"study":[61,83,103],"shows":[63,85],"any":[71,158],"simple":[75,88,128,162],"techniques,":[78],"neural":[89],"network":[90],"architectures":[91],"cannot":[92],"achieve":[93],"same":[94,144],"accuracy":[95],"CNN.":[97],"In":[98],"this":[99],"paper,":[100],"to":[104,150],"investigate":[105],"CNN's":[106],"presented.":[110],"with":[112,133,164,169,191],"firstly":[117],"evaluated":[118,141],"on":[119,142],"number":[121,178],"benchmark":[123,145],"datasets":[124],"then":[126],"traditional":[129,189],"Multi-Layer":[130],"Perceptron":[131],"(MLP)":[132],"full":[134,167],"image,":[135],"manual":[137],"are":[140],"datasets.":[146],"The":[147,184],"purpose":[148],"see":[151],"whether":[152],"performs":[157],"MLP":[165,190],"MLP.":[170],"Many":[171],"experiments":[172],"were":[173],"systematically":[174],"conducted":[175],"by":[176],"varying":[177],"epochs":[180],"hidden":[182],"neurons.":[183],"experimental":[185],"results":[186],"revealed":[187],"suitable":[192],"parameters":[193],"good":[197],"or":[200],"certain":[203],"cases.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}