{"id":"https://openalex.org/W3137686095","doi":"https://doi.org/10.1109/bigdata50022.2020.9378000","title":"Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters","display_name":"Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137686095","doi":"https://doi.org/10.1109/bigdata50022.2020.9378000","mag":"3137686095"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378000","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5023012187","display_name":"Niyati R. Darji","orcid":null},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Niyati R. Darji","raw_affiliation_strings":["Sytems and Computer Engineering Carleton University, Ottawa, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sytems and Computer Engineering Carleton University, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I67031392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004784914","display_name":"Samuel A. Ajila","orcid":"https://orcid.org/0000-0001-8824-1922"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Samuel A. Ajila","raw_affiliation_strings":["Sytems and Computer Engineering Carleton University, Ottawa, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sytems and Computer Engineering Carleton University, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I67031392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67031392"],"apc_list":null,"apc_paid":null,"fwci":0.3411,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66221789,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2472","last_page":"2481"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9983000159263611,"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":0.9783999919891357,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.9357515573501587},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8838807344436646},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7521083354949951},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7148516178131104},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.709980309009552},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6535543203353882},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6055275201797485},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5909237265586853},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.5888785123825073},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5726324319839478},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5648993253707886}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9357515573501587},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8838807344436646},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7521083354949951},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7148516178131104},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.709980309009552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6535543203353882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6055275201797485},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5909237265586853},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5888785123825073},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5726324319839478},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5648993253707886},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378000","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1549998098","https://openalex.org/W1570448133","https://openalex.org/W1981045564","https://openalex.org/W1987522330","https://openalex.org/W1989804777","https://openalex.org/W2002673464","https://openalex.org/W2015200434","https://openalex.org/W2021361613","https://openalex.org/W2103496339","https://openalex.org/W2121269968","https://openalex.org/W2129998663","https://openalex.org/W2279785795","https://openalex.org/W2401738471","https://openalex.org/W2599306383","https://openalex.org/W2608595939","https://openalex.org/W2971503667","https://openalex.org/W3017049137","https://openalex.org/W3214997989","https://openalex.org/W4249743763","https://openalex.org/W4251817184","https://openalex.org/W4320573540","https://openalex.org/W6695404774","https://openalex.org/W6713103359","https://openalex.org/W6736610077"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W4205712847","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4287818966","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3114025147","https://openalex.org/W3192751261"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,18,76,83,88,95,116,153,180],"provide":[3],"context-aware":[4],"services":[5],"such":[6],"as":[7],"health":[8],"monitoring":[9],"and":[10,47,87,124,136,158],"customized":[11],"energy":[12,45],"consumption,":[13],"smart":[14,64],"environment":[15],"designers":[16],"need":[17],"design":[19],"robust":[20],"systems":[21],"for":[22,41,133,138,155,160,182],"recognizing":[23],"the":[24,35,51,78,97,100,128,151,165],"Activities":[25],"of":[26,71,99,104,141,177],"Daily":[27],"living":[28],"(ADL).":[29],"Once":[30],"these":[31],"activities":[32,86],"are":[33,173],"recognized,":[34],"data":[36,171],"collected":[37],"can":[38,53,66],"be":[39,54,67],"used":[40],"prediction.":[42],"For":[43],"example,":[44],"consumption":[46],"other":[48,118],"characteristics":[49],"in":[50,62],"home":[52,65],"predicted.":[55],"This":[56],"is":[57,75],"possible":[58],"if":[59],"human":[60,85],"activity":[61],"a":[63],"forecasted.":[68],"The":[69,102],"aim":[70],"this":[72],"research":[73],"work":[74],"\"find":[77],"best":[79,129],"machine":[80],"learning":[81],"algorithm":[82],"predict":[84],"use":[89],"hyperparameters":[90,147],"tuning":[91],"through":[92,148],"performance":[93],"optimization":[94,149],"improve":[96],"accuracy":[98,130,152],"algorithm\"":[101],"results":[103],"our":[105],"initial":[106],"experiments":[107],"using":[108],"default":[109],"hyper-parameters":[110],"show":[111],"that":[112],"Random":[113,145,167],"Forest,":[114],"compared":[115,179],"four":[117,144],"algorithms":[119],"(MLP,":[120],"SVM,":[121],"Na\u00efve":[122],"Bayes,":[123],"Decision":[125],"Tree),":[126],"has":[127],"at":[131],"65.32%":[132],"all":[134,156],"features":[135,157,162],"62.54%":[137],"reduced":[139,161],"number":[140],"features.":[142],"Tuning":[143],"Forest":[146,168],"increases":[150],"97.9777%":[154],"98.287%":[159],"respectively.":[163],"Using":[164],"optimized":[166],"hyperparameters,":[169],"20,000":[170],"points":[172],"forecasted":[174],"with":[175],"MAE":[176],"0.0098":[178],"0.0445":[181],"Support":[183],"Vector":[184],"Machine":[185],"(SVM).":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
