{"id":"https://openalex.org/W4416251584","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228029","title":"FM-EVT:Feedforward Neural Network and Multilayer Perceptron Classifier Enhanced Event Transformer","display_name":"FM-EVT:Feedforward Neural Network and Multilayer Perceptron Classifier Enhanced Event Transformer","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251584","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228029"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5084358635","display_name":"Yudan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yudan Liu","raw_affiliation_strings":["Capital Normal University,Information Engineering College,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Capital Normal University,Information Engineering College,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000578600","display_name":"Li Su","orcid":"https://orcid.org/0000-0002-8828-5965"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Su","raw_affiliation_strings":["Capital Normal University,Information Engineering College,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Capital Normal University,Information Engineering College,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100434026","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0003-2297-0393"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["Capital Normal University,Information Engineering College,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Capital Normal University,Information Engineering College,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084358635"],"corresponding_institution_ids":["https://openalex.org/I96852419"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36539118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5922999978065491},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.573199987411499},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5608000159263611},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4997999966144562},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47600001096725464},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47130000591278076},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.454800009727478}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.718999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6234999895095825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5922999978065491},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.573199987411499},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5608000159263611},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4997999966144562},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.454800009727478},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40149998664855957},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.33169999718666077},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2618530766","https://openalex.org/W2745933219","https://openalex.org/W2922107638","https://openalex.org/W2955504279","https://openalex.org/W2963587345","https://openalex.org/W2979969178","https://openalex.org/W3025773901","https://openalex.org/W3040838455","https://openalex.org/W3102040318","https://openalex.org/W3128431487","https://openalex.org/W3134123147","https://openalex.org/W3163388905","https://openalex.org/W3186059296","https://openalex.org/W4281385234","https://openalex.org/W4281669672","https://openalex.org/W4283809036","https://openalex.org/W4292787316","https://openalex.org/W4312349930","https://openalex.org/W4390872068"],"related_works":[],"abstract_inverted_index":{"Event":[0,234,250],"cameras,":[1],"as":[2],"a":[3,173,227,244],"bio-inspired":[4],"type":[5],"of":[6,34,74,166,207,224,261,266],"dynamic":[7],"visual":[8],"sensors":[9],"(DVS),":[10],"have":[11,28],"gained":[12],"increasing":[13,163],"attentions":[14],"in":[15,42,78,96,205,263],"the":[16,32,79,83,93,97,109,121,135,142,154,164,193,198,216,231,237,249,257,264],"computer":[17],"vision":[18],"field.":[19],"Existing":[20],"methods":[21],"for":[22,157],"processing":[23,57],"event":[24,43,54,68,126,267],"data":[25,56,269],"using":[26],"transformers":[27],"failed":[29],"to":[30,105,133,144,248],"address":[31],"characteristics":[33],"small":[35],"dataset":[36,196,200],"size":[37],"and":[38,63,169,183,197,211,259],"high":[39],"information":[40,110],"density":[41],"datasets.":[44],"To":[45],"overcome":[46],"this":[47,75],"issue,":[48],"we":[49],"propose":[50],"an":[51,222],"improved":[52],"transformer-based":[53],"camera":[55,268],"framework":[58,76],"called":[59],"feedforward":[60],"neural":[61,103],"network":[62],"multilayer":[64,174],"perceptron":[65,175],"classifier":[66,176],"enhanced":[67],"transformer":[69,98],"(FM-EVT).":[70],"The":[71],"core":[72],"innovation":[73],"lies":[77],"following":[80],"aspects:":[81],"Firstly,":[82],"attention":[84],"separation":[85],"head":[86,114],"layer":[87],"replacement":[88],"technique":[89],"is":[90,131,177],"adopted,":[91],"where":[92],"self-attention":[94],"heads":[95],"are":[99],"replaced":[100],"with":[101],"shallow":[102],"networks":[104],"more":[106,118,146],"finely":[107],"analyze":[108],"differences":[111],"that":[112,202],"each":[113],"focuses":[115],"on,":[116],"thereby":[117],"effectively":[119],"capturing":[120],"spatial-temporal":[122],"dependency":[123],"relationships":[124],"among":[125],"data.":[127],"Secondly,":[128],"focal":[129],"loss":[130,139],"introduced":[132],"replace":[134],"traditional":[136],"negative":[137],"log-likelihood":[138],"function,":[140],"enabling":[141],"model":[143,233],"focus":[145],"on":[147,192,215,236],"difficult-to-classify":[148],"samples":[149],"during":[150],"training,":[151],"thus":[152],"enhancing":[153],"prediction":[155],"performance":[156],"minority":[158],"classes.":[159],"In":[160],"addition,":[161],"by":[162],"number":[165],"hidden":[167],"layers":[168],"applying":[170],"regularization":[171],"techniques,":[172],"constructed,":[178],"which":[179],"enhances":[180],"feature":[181],"extraction":[182],"generalization":[184],"capabilities,":[185],"further":[186],"improving":[187],"classification":[188],"accuracy.":[189],"Experimental":[190],"results":[191,254],"DVS128":[194,217],"gesture":[195,218],"SL-Animals-DVS":[199,238],"demonstrate":[201],"FM-EVT":[203,220,262],"excels":[204],"terms":[206],"accuracy,":[208],"parameter":[209],"count,":[210],"computational":[212],"cost.":[213],"Specifically,":[214],"dataset,":[219,239],"achieves":[221],"accuracy":[223,241],"98.23%,":[225],"representing":[226],"2.03%":[228],"improvement":[229],"over":[230],"baseline":[232],"Transformer;":[235],"its":[240],"reaches":[242],"90%,":[243],"1.88%":[245],"increase":[246],"compared":[247],"Transformer":[251],"model.":[252],"These":[253],"fully":[255],"validate":[256],"effectiveness":[258],"superiority":[260],"field":[265],"processing.":[270]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}