{"id":"https://openalex.org/W4388720290","doi":"https://doi.org/10.1109/gcce59613.2023.10315259","title":"Evaluating Imputation Strategies for Handling Missing Data: A Comparative Study","display_name":"Evaluating Imputation Strategies for Handling Missing Data: A Comparative Study","publication_year":2023,"publication_date":"2023-10-10","ids":{"openalex":"https://openalex.org/W4388720290","doi":"https://doi.org/10.1109/gcce59613.2023.10315259"},"language":"en","primary_location":{"id":"doi:10.1109/gcce59613.2023.10315259","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/gcce59613.2023.10315259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)","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/A5084160464","display_name":"Tunn Cho Lwin","orcid":"https://orcid.org/0009-0009-5650-3014"},"institutions":[{"id":"https://openalex.org/I118574687","display_name":"University of Miyazaki","ror":"https://ror.org/0447kww10","country_code":"JP","type":"education","lineage":["https://openalex.org/I118574687"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tunn Cho Lwin","raw_affiliation_strings":["University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan"],"affiliations":[{"raw_affiliation_string":"University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","institution_ids":["https://openalex.org/I118574687"]},{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan","institution_ids":["https://openalex.org/I118574687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018008705","display_name":"San Chain Tun","orcid":null},"institutions":[{"id":"https://openalex.org/I118574687","display_name":"University of Miyazaki","ror":"https://ror.org/0447kww10","country_code":"JP","type":"education","lineage":["https://openalex.org/I118574687"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"San Chain Tun","raw_affiliation_strings":["University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan"],"affiliations":[{"raw_affiliation_string":"University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","institution_ids":["https://openalex.org/I118574687"]},{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan","institution_ids":["https://openalex.org/I118574687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023940062","display_name":"Pyke Tin","orcid":"https://orcid.org/0000-0002-3623-2984"},"institutions":[{"id":"https://openalex.org/I118574687","display_name":"University of Miyazaki","ror":"https://ror.org/0447kww10","country_code":"JP","type":"education","lineage":["https://openalex.org/I118574687"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Pyke Tin","raw_affiliation_strings":["University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan"],"affiliations":[{"raw_affiliation_string":"University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","institution_ids":["https://openalex.org/I118574687"]},{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan","institution_ids":["https://openalex.org/I118574687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058861287","display_name":"Thi Thi Zin","orcid":null},"institutions":[{"id":"https://openalex.org/I118574687","display_name":"University of Miyazaki","ror":"https://ror.org/0447kww10","country_code":"JP","type":"education","lineage":["https://openalex.org/I118574687"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Thi Thi Zin","raw_affiliation_strings":["University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan"],"affiliations":[{"raw_affiliation_string":"University of Miyazaki,Graduate School of Engineering,Miyazaki,Japan","institution_ids":["https://openalex.org/I118574687"]},{"raw_affiliation_string":"Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan","institution_ids":["https://openalex.org/I118574687"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5084160464"],"corresponding_institution_ids":["https://openalex.org/I118574687"],"apc_list":null,"apc_paid":null,"fwci":0.2007,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4701474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"508","last_page":"509"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9560999870300293,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9560999870300293,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9557999968528748,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9294999837875366,"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/missing-data","display_name":"Missing data","score":0.882972002029419},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7802979946136475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6442834734916687},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6027835607528687},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5791270732879639},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5749269127845764},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5378936529159546},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.48039156198501587},{"id":"https://openalex.org/keywords/spline","display_name":"Spline (mechanical)","score":0.4423012435436249},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3664095103740692},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.36626607179641724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32695186138153076},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16925615072250366},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11232692003250122}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.882972002029419},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7802979946136475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6442834734916687},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6027835607528687},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5791270732879639},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5749269127845764},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5378936529159546},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.48039156198501587},{"id":"https://openalex.org/C10390562","wikidata":"https://www.wikidata.org/wiki/Q581809","display_name":"Spline (mechanical)","level":2,"score":0.4423012435436249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3664095103740692},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36626607179641724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32695186138153076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16925615072250366},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11232692003250122},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce59613.2023.10315259","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/gcce59613.2023.10315259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)","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":2,"referenced_works":["https://openalex.org/W3025950194","https://openalex.org/W4317418876"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Missing":[0],"data":[1,10,43,51,125,130],"is":[2,105,127],"a":[3],"significant":[4],"challenge":[5],"across":[6],"various":[7],"domains":[8],"of":[9,15,19,37,55,72,102],"analysis,":[11],"impacting":[12],"the":[13,35,66,108,118,123],"accuracy":[14],"analysis":[16,131],"and":[17,22,48,95],"interpretation":[18],"underlying":[20],"patterns":[21],"relationships":[23],"within":[24],"datasets.":[25],"This":[26,114],"study":[27,115],"specifically":[28],"focuses":[29],"on":[30],"two":[31],"different":[32],"datasets:":[33],"addressing":[34,49],"absence":[36],"depth":[38],"values":[39],"in":[40,52,58],"cattle":[41],"backbone":[42],"captured":[44],"using":[45,107],"3-D":[46],"cameras":[47],"missing":[50,124],"real-time":[53],"recordings":[54,75],"RR":[56],"intervals":[57],"fetal":[59,79],"health":[60],"rate":[61],"variability":[62],"(FHRV)":[63],"obtained":[64],"from":[65],"sensors":[67],"used":[68],"for":[69,121,129],"internal":[70],"monitoring":[71],"electrocardiogram":[73],"(ECG)":[74],"taken":[76],"prior":[77],"to":[78],"delivery.":[80],"To":[81],"tackle":[82],"these":[83],"gaps,":[84],"popular":[85],"time":[86],"series":[87],"imputation":[88],"techniques,":[89],"including":[90],"linear":[91],"interpolation,":[92,94],"spline":[93],"autoregressive":[96],"models,":[97],"are":[98],"employed.":[99],"The":[100],"performance":[101],"each":[103],"model":[104,120],"evaluated":[106],"root":[109],"mean":[110],"square":[111],"error":[112],"(RMSE).":[113],"ultimately":[116],"selects":[117],"optimal":[119],"handling":[122],"which":[126],"important":[128],"research":[132],"work.":[133]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
