{"id":"https://openalex.org/W4400406494","doi":"https://doi.org/10.3390/bdcc8070075","title":"Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification","display_name":"Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification","publication_year":2024,"publication_date":"2024-07-08","ids":{"openalex":"https://openalex.org/W4400406494","doi":"https://doi.org/10.3390/bdcc8070075"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc8070075","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc8070075","pdf_url":"https://www.mdpi.com/2504-2289/8/7/75/pdf?version=1720430700","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-2289/8/7/75/pdf?version=1720430700","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010339587","display_name":"Yuri Gordienko","orcid":"https://orcid.org/0000-0003-2682-4668"},"institutions":[{"id":"https://openalex.org/I202483615","display_name":"National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d","ror":"https://ror.org/00syn5v21","country_code":"UA","type":"education","lineage":["https://openalex.org/I202483615"]}],"countries":["UA"],"is_corresponding":true,"raw_author_name":"Yuri Gordienko","raw_affiliation_strings":["Faculty of Informatics and Computer Science, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine","Faculty of Informatics and Computer Science, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", 03056 Kyiv, Ukraine"],"raw_orcid":"https://orcid.org/0000-0003-2682-4668","affiliations":[{"raw_affiliation_string":"Faculty of Informatics and Computer Science, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine","institution_ids":["https://openalex.org/I202483615"]},{"raw_affiliation_string":"Faculty of Informatics and Computer Science, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", 03056 Kyiv, Ukraine","institution_ids":["https://openalex.org/I202483615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012071959","display_name":"Yevhenii Trochun","orcid":"https://orcid.org/0000-0002-2744-6681"},"institutions":[{"id":"https://openalex.org/I202483615","display_name":"National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d","ror":"https://ror.org/00syn5v21","country_code":"UA","type":"education","lineage":["https://openalex.org/I202483615"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Yevhenii Trochun","raw_affiliation_strings":["Faculty of Informatics and Computer Science, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine","Faculty of Informatics and Computer Science, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", 03056 Kyiv, Ukraine"],"raw_orcid":"https://orcid.org/0000-0002-2744-6681","affiliations":[{"raw_affiliation_string":"Faculty of Informatics and Computer Science, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine","institution_ids":["https://openalex.org/I202483615"]},{"raw_affiliation_string":"Faculty of Informatics and Computer Science, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", 03056 Kyiv, Ukraine","institution_ids":["https://openalex.org/I202483615"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055782226","display_name":"Sergii Stirenko","orcid":"https://orcid.org/0000-0002-9395-8685"},"institutions":[{"id":"https://openalex.org/I202483615","display_name":"National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d","ror":"https://ror.org/00syn5v21","country_code":"UA","type":"education","lineage":["https://openalex.org/I202483615"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Sergii Stirenko","raw_affiliation_strings":["Faculty of Informatics and Computer Science, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine","Faculty of Informatics and Computer Science, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", 03056 Kyiv, Ukraine"],"raw_orcid":"https://orcid.org/0000-0002-9395-8685","affiliations":[{"raw_affiliation_string":"Faculty of Informatics and Computer Science, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine","institution_ids":["https://openalex.org/I202483615"]},{"raw_affiliation_string":"Faculty of Informatics and Computer Science, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", 03056 Kyiv, Ukraine","institution_ids":["https://openalex.org/I202483615"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010339587"],"corresponding_institution_ids":["https://openalex.org/I202483615"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":1.6557,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.86043137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"8","issue":"7","first_page":"75","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9988999962806702,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9988999962806702,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9836999773979187,"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/computer-science","display_name":"Computer science","score":0.8405108451843262},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.72072434425354},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6477118134498596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6017279624938965},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5852106809616089},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.555070161819458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5214787721633911},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5210772752761841},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5178120732307434},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4730980694293976},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4457795023918152},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.41505861282348633},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19081011414527893},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10232853889465332},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.09395185112953186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8405108451843262},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.72072434425354},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6477118134498596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6017279624938965},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5852106809616089},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.555070161819458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5214787721633911},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5210772752761841},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5178120732307434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4730980694293976},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4457795023918152},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.41505861282348633},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19081011414527893},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10232853889465332},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.09395185112953186},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/bdcc8070075","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc8070075","pdf_url":"https://www.mdpi.com/2504-2289/8/7/75/pdf?version=1720430700","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:102916b5ef914494bea4a033e31a1336","is_oa":true,"landing_page_url":"https://doaj.org/article/102916b5ef914494bea4a033e31a1336","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":"Big Data and Cognitive Computing, Vol 8, Iss 7, p 75 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/bdcc8070075","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc8070075","pdf_url":"https://www.mdpi.com/2504-2289/8/7/75/pdf?version=1720430700","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6669828665","display_name":null,"funder_award_id":"2022.01/0199","funder_id":"https://openalex.org/F4320318013","funder_display_name":"National Research Foundation of Ukraine"}],"funders":[{"id":"https://openalex.org/F4320318013","display_name":"National Research Foundation of Ukraine","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4400406494.pdf"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W2195333992","https://openalex.org/W2302255633","https://openalex.org/W2559394418","https://openalex.org/W2811269136","https://openalex.org/W2946948417","https://openalex.org/W2989257188","https://openalex.org/W3007475506","https://openalex.org/W3037607219","https://openalex.org/W3137120888","https://openalex.org/W3210187349","https://openalex.org/W4206451978","https://openalex.org/W4255421341","https://openalex.org/W4280596516","https://openalex.org/W4285352055","https://openalex.org/W4312458389","https://openalex.org/W4319965846","https://openalex.org/W4320727554","https://openalex.org/W4361282512","https://openalex.org/W4378365443","https://openalex.org/W4379741860","https://openalex.org/W4386910109","https://openalex.org/W4390614711"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W3138386522","https://openalex.org/W2499279132","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856"],"abstract_inverted_index":{"By":[0,157,257],"utilizing":[1],"hybrid":[2],"quantum\u2013classical":[3],"neural":[4,79,103,217],"networks":[5,104],"(HNNs),":[6],"this":[7,242,272,289],"research":[8,122,243,290],"aims":[9],"to":[10,21,25,41,83,107,189,210,292],"enhance":[11],"the":[12,63,74,78,92,124,141,151,159,166,206,224,245,259,277,286,308,322],"efficiency":[13],"of":[14,65,77,94,126,136,143,153,161,227,249,261,276,280,288,296,310,324],"image":[15,118,254,315],"classification":[16,119,255,316],"tasks.":[17,120,256],"HNNs":[18,127,144,162,209,229,251,262,281,312],"allow":[19],"us":[20],"utilize":[22],"quantum":[23,71],"computing":[24],"solve":[26],"machine":[27,183,297,330],"learning":[28,184,194,298,331],"problems,":[29],"which":[30,81,177],"can":[31,105,187,221,320],"be":[32,84],"highly":[33],"power-efficient":[34],"and":[35,55,97,115,133,203,247,270,306,327],"provide":[36],"significant":[37],"computation":[38],"speedup":[39],"compared":[40],"classical":[42,102,155,216,264],"operations.":[43],"This":[44,60,121,219],"is":[45,58,178],"particularly":[46],"relevant":[47],"in":[48,148,215,241,282],"sustainable":[49,182,294],"applications":[50,295],"where":[51],"reducing":[52],"computational":[53,225],"resources":[54],"energy":[56],"consumption":[57],"crucial.":[59],"study":[61,167,239,273],"explores":[62],"feasibility":[64,238,309],"a":[66],"novel":[67,301],"architecture":[68],"by":[69,139,299],"leveraging":[70],"devices":[72],"as":[73,200],"first":[75],"layer":[76],"network,":[80],"proved":[82],"useful":[85],"for":[86,117,180,208,252,313],"scaling":[87],"HNNs\u2019":[88],"training":[89,228],"process.":[90],"Understanding":[91],"role":[93],"quanvolutional":[95],"operations":[96],"how":[98],"they":[99],"interact":[100],"with":[101,150,196,263],"lead":[106],"optimized":[108],"model":[109,304],"architectures":[110],"that":[111,186],"are":[112],"more":[113,325],"efficient":[114,326],"effective":[116],"investigates":[123],"performance":[125,142,152,260],"across":[128,173],"different":[129,190],"datasets,":[130,165],"including":[131],"CIFAR100":[132],"Satellite":[134],"Images":[135],"Hurricane":[137],"Damage":[138],"evaluating":[140,158],"on":[145,163],"these":[146],"datasets":[147],"comparison":[149],"reference":[154,265],"models.":[156],"scalability":[160],"diverse":[164],"provides":[168,274],"insights":[169,319],"into":[170],"their":[171],"applicability":[172],"various":[174],"real-world":[175,253,314],"scenarios,":[176],"essential":[179],"building":[181],"solutions":[185],"adapt":[188],"environments.":[191],"Leveraging":[192],"transfer":[193],"techniques":[195],"pre-trained":[197],"models":[198,266],"such":[199],"ResNet,":[201,268],"EfficientNet,":[202,269],"VGG16":[204],"demonstrates":[205],"potential":[207,278],"benefit":[211],"from":[212,230],"existing":[213],"knowledge":[214],"networks.":[218],"approach":[220],"significantly":[222],"reduce":[223],"cost":[226],"scratch":[231],"while":[232],"still":[233],"achieving":[234],"competitive":[235],"performance.":[236],"The":[237],"conducted":[240],"assesses":[244],"practicality":[246],"viability":[248],"deploying":[250],"comparing":[258],"like":[267],"VGG-16,":[271],"evidence":[275],"advantages":[279],"certain":[283],"scenarios.":[284],"Overall,":[285],"findings":[287],"contribute":[291],"advancing":[293],"proposing":[300],"techniques,":[302],"optimizing":[303],"architectures,":[305],"demonstrating":[307],"adopting":[311],"problems.":[317],"These":[318],"inform":[321],"development":[323],"environmentally":[328],"friendly":[329],"solutions.":[332]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}