{"id":"https://openalex.org/W4200330997","doi":"https://doi.org/10.1109/icecit54077.2021.9641226","title":"Neural network pruning: An effective way to reduce the initial network for deep learning based human activity recognition","display_name":"Neural network pruning: An effective way to reduce the initial network for deep learning based human activity recognition","publication_year":2021,"publication_date":"2021-09-14","ids":{"openalex":"https://openalex.org/W4200330997","doi":"https://doi.org/10.1109/icecit54077.2021.9641226"},"language":"en","primary_location":{"id":"doi:10.1109/icecit54077.2021.9641226","doi":"https://doi.org/10.1109/icecit54077.2021.9641226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecit54077.2021.9641226","pdf_url":null,"source":{"id":"https://openalex.org/S4363608437","display_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5069693151","display_name":"Mohammad Helal Uddin","orcid":"https://orcid.org/0000-0003-3001-7826","observed_orcids":["https://orcid.org/0000-0003-3001-7826"]},"institutions":[{"id":"https://openalex.org/I161024014","display_name":"Kwangwoon University","ror":"https://ror.org/02e9zc863","country_code":"KR","type":"education","lineage":["https://openalex.org/I161024014"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Mohammad Helal Uddin","raw_affiliation_strings":["Dept. Electronic Engineering, Kwangwoon University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electronic Engineering, Kwangwoon University, Seoul, South Korea","institution_ids":["https://openalex.org/I161024014"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jannat Mir Kanon Ara","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I161024014","display_name":"Kwangwoon University","ror":"https://ror.org/02e9zc863","country_code":"KR","type":"education","lineage":["https://openalex.org/I161024014"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jannat Mir Kanon Ara","raw_affiliation_strings":["Dept. Electronic Engineering, Kwangwoon University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electronic Engineering, Kwangwoon University, Seoul, South Korea","institution_ids":["https://openalex.org/I161024014"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079425599","display_name":"Md Hafizur Rahman","orcid":"https://orcid.org/0000-0002-1981-6582","observed_orcids":["https://orcid.org/0000-0002-1981-6582"]},"institutions":[{"id":"https://openalex.org/I40279923","display_name":"Islamic University","ror":"https://ror.org/04j1w0q97","country_code":"BD","type":"education","lineage":["https://openalex.org/I40279923"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Md Hafizur Rahman","raw_affiliation_strings":["Dept. of Electrical and Electronic Engineering, Islamic University, Kushtia, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical and Electronic Engineering, Islamic University, Kushtia, Bangladesh","institution_ids":["https://openalex.org/I40279923"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101490855","display_name":"Sung\u2010Hyun Yang","orcid":"https://orcid.org/0000-0001-8856-764X","observed_orcids":["https://orcid.org/0000-0001-8856-764X"]},"institutions":[{"id":"https://openalex.org/I161024014","display_name":"Kwangwoon University","ror":"https://ror.org/02e9zc863","country_code":"KR","type":"education","lineage":["https://openalex.org/I161024014"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"S.H. Yang","raw_affiliation_strings":["Dept. Electronic Engineering, Kwangwoon University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electronic Engineering, Kwangwoon University, Seoul, South Korea","institution_ids":["https://openalex.org/I161024014"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5392,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.73106187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9750999808311462,"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":0.9750999808311462,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.01140000019222498,"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":0.0008999999845400453,"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/neural-network-pruning","display_name":"neural network pruning","score":1.0},{"id":"https://openalex.org/keywords/human-activity-recognition","display_name":"human activity recognition","score":0.9700000286102295},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"long short-term memory","score":0.8759999871253967},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.7599999904632568},{"id":"https://openalex.org/keywords/smartphone-accelerometer","display_name":"smartphone accelerometer","score":0.6349999904632568},{"id":"https://openalex.org/keywords/weight-pruning","display_name":"weight pruning","score":0.7089999914169312},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.6029999852180481},{"id":"https://openalex.org/keywords/inference-efficiency","display_name":"inference efficiency","score":0.7480000257492065},{"id":"https://openalex.org/keywords/android-application","display_name":"Android application","score":0.45899999141693115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7632817029953003},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7631498575210571},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.70855712890625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6619064211845398},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6023640632629395},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49112340807914734},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46169814467430115},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.43796342611312866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3240772485733032},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecit54077.2021.9641226","doi":"https://doi.org/10.1109/icecit54077.2021.9641226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecit54077.2021.9641226","pdf_url":null,"source":{"id":"https://openalex.org/S4363608437","display_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W1845051632","https://openalex.org/W2017634428","https://openalex.org/W2114766824","https://openalex.org/W2515385951","https://openalex.org/W2550476060","https://openalex.org/W2792760996","https://openalex.org/W2808217246","https://openalex.org/W2913242149","https://openalex.org/W2963674932","https://openalex.org/W3112927272","https://openalex.org/W4236047370"],"related_works":["https://openalex.org/W4362501139","https://openalex.org/W3017361427","https://openalex.org/W4223644476","https://openalex.org/W3163244872","https://openalex.org/W7212656477","https://openalex.org/W4393405609","https://openalex.org/W4387675809","https://openalex.org/W3005165565","https://openalex.org/W4409431387","https://openalex.org/W4405271408"],"abstract_inverted_index":{"Neural":[0,99],"network":[1,34,100,123,138,147],"pruning":[2,35,101,116],"methods":[3],"can":[4],"decrease":[5],"the":[6,16,23,29,32,71,82,88,96,105,108,118,121,129,133,136,142,146,169,176,190],"parameter":[7],"counts":[8],"of":[9,19,31,87,114,135,184,198],"trained":[10],"neural":[11,33],"networks":[12],"along":[13],"with":[14],"improving":[15],"computational":[17],"performance":[18],"inference":[20],"without":[21],"compromising":[22],"accuracy.":[24],"This":[25],"work":[26],"has":[27,66,77,148],"demonstrated":[28],"effectiveness":[30],"methodology,":[36],"which":[37],"was":[38,52,92,102,110,125,139,178],"applied":[39,103],"to":[40,54,69,104,166,173,193],"deep":[41],"learning-based":[42],"human":[43,57,97],"activity":[44],"recognition.":[45],"A":[46],"long":[47],"short-term":[48],"memory":[49],"(LSTM)":[50],"architecture":[51],"used":[53],"recognize":[55],"daily":[56],"activities":[58],"from":[59,74],"smartphone-based":[60],"accelerometer":[61,72],"data.":[62],"An":[63],"android-based":[64],"application":[65],"been":[67,78,149],"developed":[68],"collect":[70],"data":[73],"smartphones.":[75],"Data":[76],"preprocessed":[79],"before":[80],"training":[81],"LSTM":[83,90],"network.":[84],"The":[85,162],"accuracy":[86,124,134,170,191],"proposed":[89],"model":[91,106,109,164,177],"97.2%":[93,172],"while":[94,145,175],"recognizing":[95],"activities.":[98],"once":[107],"trained.":[111],"After":[112],"70%":[113],"weight":[115],"on":[117],"trained/initial":[119],"network,":[120],"Pruned":[122],"similar":[126],"(97.2%)":[127],"as":[128],"original":[130],"one.":[131],"However,":[132],"pruned":[137,150,179],"better":[140],"than":[141],"initial":[143,163],"model,":[144],"by":[151,180],"25%":[152],"(accuracy":[153,156,160],"97.26%),":[154],"50%":[155,197],"97.29%),":[157],"and":[158],"60%":[159],"97.22%).":[161],"started":[165],"slightly":[167,187],"decline":[168],"(from":[171],"96.72%),":[174],"80%.":[181],"In":[182],"terms":[183],"neural/unit":[185],"pruning,":[186],"declination":[188],"in":[189],"(97.2%":[192],"97.09%)":[194],"occurred":[195],"after":[196],"pruning.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-12-31T00:00:00"}