{"id":"https://openalex.org/W4404564541","doi":"https://doi.org/10.1109/uemcon62879.2024.10754771","title":"Fitting and Filtering Functional Data for Use in Video Data Analysis","display_name":"Fitting and Filtering Functional Data for Use in Video Data Analysis","publication_year":2024,"publication_date":"2024-10-17","ids":{"openalex":"https://openalex.org/W4404564541","doi":"https://doi.org/10.1109/uemcon62879.2024.10754771"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon62879.2024.10754771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon62879.2024.10754771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 15th Annual Ubiquitous Computing, Electronics &amp;amp; Mobile Communication Conference (UEMCON)","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/A5102922418","display_name":"Iain Smith","orcid":"https://orcid.org/0009-0009-1746-9079"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Iain Nicholas Smith","raw_affiliation_strings":["University of Alberta,Department of Computing Science,Edmonton,Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta,Department of Computing Science,Edmonton,Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080670833","display_name":"Mohamad El-Hajj","orcid":"https://orcid.org/0009-0001-4614-0303"},"institutions":[{"id":"https://openalex.org/I924318406","display_name":"MacEwan University","ror":"https://ror.org/003s89n44","country_code":"CA","type":"education","lineage":["https://openalex.org/I924318406"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mohamad El-Hajj","raw_affiliation_strings":["MacEwan University,Department of Computer Science,Edmonton,Canada"],"affiliations":[{"raw_affiliation_string":"MacEwan University,Department of Computer Science,Edmonton,Canada","institution_ids":["https://openalex.org/I924318406"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102922418"],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18864056,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"411","last_page":"418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.2590999901294708,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.2590999901294708,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.22450000047683716,"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/T11439","display_name":"Video Analysis and Summarization","score":0.22040000557899475,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7476166486740112},{"id":"https://openalex.org/keywords/functional-data-analysis","display_name":"Functional data analysis","score":0.5824871063232422},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3705098628997803},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11726406216621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7476166486740112},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.5824871063232422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3705098628997803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11726406216621399}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon62879.2024.10754771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon62879.2024.10754771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 15th Annual Ubiquitous Computing, Electronics &amp;amp; Mobile Communication Conference (UEMCON)","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":14,"referenced_works":["https://openalex.org/W1925427981","https://openalex.org/W2047188683","https://openalex.org/W2148443418","https://openalex.org/W2407202096","https://openalex.org/W2785741560","https://openalex.org/W2963341661","https://openalex.org/W3036625624","https://openalex.org/W3184282408","https://openalex.org/W3207662977","https://openalex.org/W4399650373","https://openalex.org/W6692567619","https://openalex.org/W6746587748","https://openalex.org/W6849110675","https://openalex.org/W6850357939"],"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/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Our":[0,31],"research":[1],"focuses":[2],"on":[3],"advancing":[4],"the":[5,34,46,68,103,124,166],"capabilities":[6],"of":[7,37,48,54,70,126,168],"machine":[8,153],"learning":[9,154],"applications":[10,128,155],"that":[11,51,135,147],"involve":[12],"analyzing":[13],"video":[14,130,157,172],"data.":[15,131,158],"To":[16],"achieve":[17],"this,":[18],"we":[19,73,133],"have":[20],"created":[21],"a":[22,94],"novel":[23],"method":[24],"for":[25,61,98,121,129,164],"integrating":[26],"functional":[27,41,100,169],"data":[28,101,170],"into":[29,102],"video.":[30],"approach":[32],"entails":[33],"direct":[35],"application":[36],"convolutional":[38],"filters":[39,50],"to":[40,66,113,139,151],"data,":[42],"as":[43,45,142],"well":[44],"introduction":[47],"new":[49],"make":[52],"use":[53],"derivatives,":[55,143],"which":[56],"represent":[57],"an":[58],"exciting":[59,162],"avenue":[60],"further":[62],"exploration.":[63],"In":[64],"order":[65],"validate":[67],"effectiveness":[69],"our":[71,87],"approach,":[72],"conducted":[74],"experiments":[75,83],"using":[76],"both":[77],"synthetic":[78],"and":[79],"real-world":[80],"datasets.":[81],"These":[82],"helped":[84],"us":[85],"establish":[86],"method\u2019s":[88],"potential":[89,120],"in":[90,171],"practical":[91],"scenarios.We":[92],"propose":[93],"specific":[95],"parameter":[96,108],"ratio":[97,109],"incorporating":[99],"original":[104],"input":[105],"frames.":[106],"This":[107,159],"has":[110],"been":[111],"shown":[112],"require":[114],"less":[115],"information":[116,146],"while":[117],"offering":[118],"substantial":[119],"exploration":[122],"within":[123],"realm":[125],"machine-learning":[127],"Furthermore,":[132],"found":[134],"additional":[136],"operations":[137],"applicable":[138],"functions,":[140],"such":[141],"yield":[144],"valuable":[145],"can":[148],"be":[149],"harnessed":[150],"enhance":[152],"involving":[156],"opens":[160],"up":[161],"possibilities":[163],"leveraging":[165],"richness":[167],"analysis.":[173]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
