{"id":"https://openalex.org/W4412877214","doi":"https://doi.org/10.1145/3711896.3736972","title":"Fully Quanvolutional Networks for Time Series Classification","display_name":"Fully Quanvolutional Networks for Time Series Classification","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877214","doi":"https://doi.org/10.1145/3711896.3736972"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3736972","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736972","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736972","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736972","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086618544","display_name":"Nabil Anan Orka","orcid":"https://orcid.org/0000-0001-5251-2137"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Nabil Anan Orka","raw_affiliation_strings":["The University of Queensland, Brisbane, Australia"],"raw_orcid":"https://orcid.org/0000-0001-5251-2137","affiliations":[{"raw_affiliation_string":"The University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064320107","display_name":"Ehtashamul Haque","orcid":"https://orcid.org/0000-0002-9737-5698"},"institutions":[{"id":"https://openalex.org/I5518804","display_name":"BRAC University","ror":"https://ror.org/00sge8677","country_code":"BD","type":"education","lineage":["https://openalex.org/I5518804"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Ehtashamul Haque","raw_affiliation_strings":["BRAC University, Dhaka, Bangladesh"],"raw_orcid":"https://orcid.org/0000-0002-9737-5698","affiliations":[{"raw_affiliation_string":"BRAC University, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I5518804"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101540530","display_name":"Md. Abdul Awal","orcid":"https://orcid.org/0000-0003-3028-4932"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Md. Abdul Awal","raw_affiliation_strings":["The University of Queensland, Brisbane, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3028-4932","affiliations":[{"raw_affiliation_string":"The University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034828056","display_name":"Mohammad Ali Moni","orcid":"https://orcid.org/0000-0003-0756-1006"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mohammad Ali Moni","raw_affiliation_strings":["Charles Sturt University, Orange, Australia and Washington University of Science and Technology, Alexandria, USA"],"raw_orcid":"https://orcid.org/0000-0003-0756-1006","affiliations":[{"raw_affiliation_string":"Charles Sturt University, Orange, Australia and Washington University of Science and Technology, Alexandria, USA","institution_ids":["https://openalex.org/I153230381"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19815086,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2210","last_page":"2221"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9979000091552734,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9861999750137329,"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":0.657593846321106},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.60030597448349},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.487444669008255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35511747002601624},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27484607696533203},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07395264506340027}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657593846321106},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.60030597448349},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.487444669008255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35511747002601624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27484607696533203},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07395264506340027},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3711896.3736972","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736972","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736972","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/4a56cdcb-39cf-47da-986f-a7d2e4c03700","is_oa":true,"landing_page_url":"https://researchoutput.csu.edu.au/en/publications/4a56cdcb-39cf-47da-986f-a7d2e4c03700","pdf_url":null,"source":{"id":"https://openalex.org/S7407055442","display_name":"Charles Sturt University Research Output (CRO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Orka, N A, Haque, E, Awal, M A & Moni, M A 2025, Fully quanvolutional networks for time series classification. in KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2, Association for Computing Machinery, New York, NY, pp. 2210-2221, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025, Toronto, Ontario, Canada, 03/08/25. https://doi.org/10.1145/3711896.3736972","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1145/3711896.3736972","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736972","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736972","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877214.pdf","grobid_xml":"https://content.openalex.org/works/W4412877214.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1529944915","https://openalex.org/W1820615084","https://openalex.org/W1972482761","https://openalex.org/W2025357764","https://openalex.org/W2103956991","https://openalex.org/W2105824687","https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2417863416","https://openalex.org/W2888774813","https://openalex.org/W2896712926","https://openalex.org/W2963446712","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W2988244882","https://openalex.org/W2990961515","https://openalex.org/W3012697779","https://openalex.org/W3042807565","https://openalex.org/W3083891030","https://openalex.org/W3098400458","https://openalex.org/W3192227636","https://openalex.org/W4212855986","https://openalex.org/W4289360400","https://openalex.org/W4372319248","https://openalex.org/W4387079863","https://openalex.org/W4389164264","https://openalex.org/W4390831829","https://openalex.org/W4392305344","https://openalex.org/W4401687189","https://openalex.org/W4405183524","https://openalex.org/W4412879423"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Despite":[0],"the":[1,41,46,97,104,170,184],"advancements":[2],"in":[3,10,36,45,157],"quantum":[4,30,146,189],"convolution":[5],"or":[6],"quanvolution,":[7],"challenges":[8],"persist":[9],"making":[11],"quanvolution":[12,38,44,62,105],"scalable,":[13],"efficient,":[14],"and":[15,73,100,112,128,135,137,147,191,196],"applicable":[16],"to":[17,33,152,168,186],"multi-dimensional":[18],"data.":[19],"Existing":[20],"quanvolutional":[21,86],"networks":[22],"heavily":[23],"rely":[24],"on":[25,125,174],"classical":[26,98],"layers,":[27],"with":[28,82,115],"minimal":[29],"involvement":[31],"due":[32],"inherent":[34],"limitations":[35],"current":[37],"algorithms.":[39],"Moreover,":[40],"application":[42],"of":[43,48,64,78,159,172],"domain":[47],"1D":[49,67],"data":[50],"remains":[51],"largely":[52],"unexplored.":[53],"To":[54,119],"address":[55],"these":[56],"limitations,":[57],"we":[58,122,177],"propose":[59],"a":[60,75,83,116,179],"new":[61],"algorithm-Quanv1D-capable":[63],"processing":[65],"arbitrary-channel":[66],"data,":[68],"handling":[69],"variable":[70],"kernel":[71],"sizes,":[72],"generating":[74],"customizable":[76],"number":[77],"feature":[79],"maps,":[80],"along":[81],"classification":[84],"network-fully":[85],"network":[87],"(FQN)-built":[88],"solely":[89],"using":[90,163],"Quanv1D":[91,93],"layers.":[92],"is":[94],"inspired":[95],"by":[96,107],"Conv1D":[99],"stands":[101],"out":[102],"from":[103],"literature":[106],"being":[108],"fully":[109],"trainable,":[110],"modular,":[111],"freely":[113],"scalable":[114],"self-regularizing":[117],"feature.":[118],"evaluate":[120],"FQN,":[121],"tested":[123],"it":[124],"20":[126],"UEA":[127],"UCR":[129],"time":[130],"series":[131],"datasets,":[132],"both":[133],"univariate":[134],"multivariate,":[136],"benchmarked":[138],"its":[139],"performance":[140],"against":[141],"state-of-the-art":[142],"convolutional":[143],"models":[144,156],"(both":[145],"classical).":[148],"We":[149],"found":[150,192],"FQN":[151,173],"outperform":[153],"all":[154,183],"compared":[155],"terms":[158],"average":[160],"accuracy":[161],"while":[162],"significantly":[164],"fewer":[165],"parameters.":[166],"Additionally,":[167],"assess":[169],"viability":[171],"real":[175],"hardware,":[176],"conducted":[178],"shot-based":[180],"analysis":[181],"across":[182],"datasets":[185],"simulate":[187],"statistical":[188],"noise":[190],"our":[193],"model":[194],"robust":[195],"equally":[197],"efficient.":[198]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}