{"id":"https://openalex.org/W2152709263","doi":"https://doi.org/10.1109/ijcnn.2010.5596758","title":"A hierarchical GMDH-based polynomial neural network for handwritten numeral recognition using topological features","display_name":"A hierarchical GMDH-based polynomial neural network for handwritten numeral recognition using topological features","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2152709263","doi":"https://doi.org/10.1109/ijcnn.2010.5596758","mag":"2152709263"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2010.5596758","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2010.5596758","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2010 International Joint Conference on Neural Networks (IJCNN)","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/A5057893924","display_name":"El-Sayed M. El-Alfy","orcid":"https://orcid.org/0000-0001-6279-9776"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"El-Sayed M. El-Alfy","raw_affiliation_strings":["College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5057893924"],"corresponding_institution_ids":["https://openalex.org/I134085113"],"apc_list":null,"apc_paid":null,"fwci":0.9021,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.81496112,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14351","display_name":"Statistical and Computational Modeling","score":0.9979000091552734,"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/T14351","display_name":"Statistical and Computational Modeling","score":0.9979000091552734,"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/T10320","display_name":"Neural Networks and Applications","score":0.9869999885559082,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9696999788284302,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.7415844798088074},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6858319044113159},{"id":"https://openalex.org/keywords/numeral-system","display_name":"Numeral system","score":0.6636233329772949},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6436169743537903},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5968987941741943},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5813532471656799},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.4938283860683441},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.47859689593315125},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47233453392982483},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4479486644268036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4207146465778351},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3957374691963196}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7415844798088074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6858319044113159},{"id":"https://openalex.org/C204160518","wikidata":"https://www.wikidata.org/wiki/Q122653","display_name":"Numeral system","level":2,"score":0.6636233329772949},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6436169743537903},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5968987941741943},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5813532471656799},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.4938283860683441},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.47859689593315125},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47233453392982483},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4479486644268036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4207146465778351},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3957374691963196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2010.5596758","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2010.5596758","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2010 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W82943839","https://openalex.org/W1532325895","https://openalex.org/W1770912791","https://openalex.org/W1971488099","https://openalex.org/W1974320738","https://openalex.org/W2014986661","https://openalex.org/W2015515497","https://openalex.org/W2024986448","https://openalex.org/W2040907763","https://openalex.org/W2054892457","https://openalex.org/W2073858015","https://openalex.org/W2079539333","https://openalex.org/W2081785739","https://openalex.org/W2109883274","https://openalex.org/W2110560073","https://openalex.org/W2113922688","https://openalex.org/W2118598121","https://openalex.org/W2132760371","https://openalex.org/W2142069714","https://openalex.org/W2172000360","https://openalex.org/W2191457041","https://openalex.org/W4213009331","https://openalex.org/W6634339819","https://openalex.org/W6677540384"],"related_works":["https://openalex.org/W2539042112","https://openalex.org/W587872652","https://openalex.org/W4234680390","https://openalex.org/W1964714693","https://openalex.org/W2238382207","https://openalex.org/W4301603763","https://openalex.org/W3148193224","https://openalex.org/W2165592082","https://openalex.org/W2024731242","https://openalex.org/W2136729892"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,43,84,90,116],"multiclass":[3,91],"hierarchical":[4],"abductive":[5],"learning":[6,113],"classifier":[7,56],"and":[8,57,106],"apply":[9],"it":[10],"to":[11,47,73,88],"improve":[12],"the":[13,21,24,52,59,67,71],"recognition":[14],"rate":[15],"of":[16,23,51,61,69,82,86],"handwritten":[17,28,62],"numerals":[18],"while":[19],"reduce":[20],"dimensionality":[22],"feature":[25],"space.":[26],"For":[27],"recognition,":[29],"there":[30],"are":[31],"ten":[32],"classes.":[33],"Using":[34],"9":[35],"binary":[36],"GMDH-based":[37],"neural":[38],"network":[39],"models":[40],"structured":[41],"in":[42,79],"hierarchy":[44],"has":[45,66],"led":[46],"improving":[48,58],"balance":[49],"factor":[50],"dataset":[53,119],"for":[54],"each":[55],"classification":[60,75,92],"numerals.":[63],"It":[64],"also":[65],"advantage":[68],"removing":[70],"need":[72],"resolve":[74],"ties":[76],"that":[77],"exist":[78],"other":[80,110],"forms":[81],"combining":[83],"number":[85],"classifiers":[87,114],"solve":[89],"problem":[93],"whether":[94],"using":[95,115],"one-versus-all":[96],"or":[97],"one-versus-one":[98],"approaches.":[99],"The":[100],"proposed":[101],"approach":[102],"is":[103],"empirically":[104],"evaluated":[105],"compared":[107],"with":[108],"five":[109],"state-of-the-art":[111],"machine":[112],"publicly":[117],"available":[118],"based":[120],"on":[121],"non-Gaussian":[122],"topological":[123],"features.":[124]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}