{"id":"https://openalex.org/W7131393669","doi":"https://doi.org/10.1109/ccwc67433.2026.11393795","title":"Hybrid Sequential Pattern Mining and Machine Learning for Context-Aware Event Prediction in IoT","display_name":"Hybrid Sequential Pattern Mining and Machine Learning for Context-Aware Event Prediction in IoT","publication_year":2026,"publication_date":"2026-01-05","ids":{"openalex":"https://openalex.org/W7131393669","doi":"https://doi.org/10.1109/ccwc67433.2026.11393795"},"language":null,"primary_location":{"id":"doi:10.1109/ccwc67433.2026.11393795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc67433.2026.11393795","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101903642","display_name":"Pooja Goyal","orcid":"https://orcid.org/0000-0001-8578-4008"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pooja Goyal","raw_affiliation_strings":["Computer Science & Engineering, University of North Texas,Denton,Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering, University of North Texas,Denton,Texas","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126840525","display_name":"Bradley Helmholz","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bradley Helmholz","raw_affiliation_strings":["Computer Science & Engineering, University of North Texas,Denton,Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering, University of North Texas,Denton,Texas","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109726464","display_name":"Trinh Canh Khanh Tran","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Trinh Tran","raw_affiliation_strings":["Computer Science & Engineering, University of North Texas,Denton,Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering, University of North Texas,Denton,Texas","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092009118","display_name":"Farhan Rahman Arnob","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farhan Rahman Arnob","raw_affiliation_strings":["Computer Science & Engineering, University of North Texas,Denton,Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering, University of North Texas,Denton,Texas","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077471766","display_name":"Ren\u00e9e Bryce","orcid":"https://orcid.org/0000-0002-7060-8586"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Renee Bryce","raw_affiliation_strings":["Computer Science & Engineering, University of North Texas,Denton,Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering, University of North Texas,Denton,Texas","institution_ids":["https://openalex.org/I123534392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123534392"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14184688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"0917","last_page":"0923"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.12030000239610672,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12127","display_name":"Software System Performance and Reliability","score":0.12030000239610672,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.1096000000834465,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.06710000336170197,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/discriminative-model","display_name":"Discriminative model","score":0.6069999933242798},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5267000198364258},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5253999829292297},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5095999836921692},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4828999936580658},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46399998664855957},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4131999909877777},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.39800000190734863},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3686000108718872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7156000137329102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6829000115394592},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6819000244140625},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6069999933242798},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5095999836921692},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4068000018596649},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.35510000586509705},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.33309999108314514},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc67433.2026.11393795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc67433.2026.11393795","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5494608727","display_name":"REU Site: TaMaLe - Testing and Machine Learning for Context-Driven Systems: Research Experience for Undergraduates","funder_award_id":"2149969","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2050462648","https://openalex.org/W2168196587","https://openalex.org/W2507635762","https://openalex.org/W2990217975","https://openalex.org/W2993853848","https://openalex.org/W3024761859","https://openalex.org/W3172170191","https://openalex.org/W4211186645","https://openalex.org/W4366251042","https://openalex.org/W4406863653","https://openalex.org/W4408047685"],"related_works":[],"abstract_inverted_index":{"Context-aware":[0],"event":[1],"prediction":[2,73],"is":[3],"a":[4,49,126,151],"critical":[5],"challenge":[6],"in":[7,34,82],"Internet":[8],"of":[9,122,138],"Things":[10],"(IoT)":[11],"systems,":[12],"where":[13],"accurate":[14],"forecasting":[15],"enables":[16],"adaptive":[17],"and":[18,65],"personalized":[19],"applications.":[20,157],"Existing":[21],"approaches":[22],"often":[23],"rely":[24],"on":[25,74],"either":[26],"sequential":[27,54,85],"pattern":[28,55,141],"mining":[29],"or":[30],"machine":[31],"learning":[32,68,145],"models":[33,69],"isolation,":[35],"limiting":[36],"their":[37],"ability":[38],"to":[39,70,97],"capture":[40],"complex":[41],"temporal":[42,99],"dependencies.":[43],"In":[44],"this":[45,109],"work,":[46],"we":[47],"introduce":[48],"hybrid":[50],"framework":[51],"that":[52,108],"integrates":[53],"mining-via":[56],"POERMH":[57],"(Partially-Ordered":[58],"Episode":[59],"Rule":[60],"Mining":[61],"with":[62,93,115,143],"Head":[63],"support)":[64],"PrefixSpan-with":[66],"discriminative":[67],"enhance":[71],"context-event":[72],"mobile":[75],"IoT":[76,105,156],"devices.":[77],"Our":[78],"key":[79],"contribution":[80],"lies":[81],"leveraging":[83],"mined":[84],"structures":[86],"as":[87],"feature":[88],"representations":[89],"for":[90,146,154],"classifiers,":[91],"combined":[92],"optimized":[94],"time-interval":[95],"segmentation":[96],"preserve":[98],"dynamics.":[100],"Empirical":[101],"evaluation":[102],"across":[103],"real-world":[104],"datasets":[106],"demonstrates":[107],"integration":[110],"significantly":[111],"outperforms":[112],"standalone":[113],"baselines,":[114],"Support":[116],"Vector":[117],"Machines":[118],"achieving":[119],"an":[120],"F1-score":[121],"67.08":[123],"%,":[124],"representing":[125],"substantial":[127],"improvement":[128],"over":[129],"traditional":[130],"sequence-only":[131],"methods.":[132],"These":[133],"findings":[134],"highlight":[135],"the":[136],"importance":[137],"coupling":[139],"symbolic":[140],"discovery":[142],"statistical":[144],"robust":[147],"context":[148],"modeling,":[149],"offering":[150],"scalable":[152],"approach":[153],"next-generation":[155]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-02-26T00:00:00"}