{"id":"https://openalex.org/W4312127035","doi":"https://doi.org/10.3390/s22249735","title":"A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression","display_name":"A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression","publication_year":2022,"publication_date":"2022-12-12","ids":{"openalex":"https://openalex.org/W4312127035","doi":"https://doi.org/10.3390/s22249735","pmid":"https://pubmed.ncbi.nlm.nih.gov/36560104"},"language":"en","primary_location":{"id":"doi:10.3390/s22249735","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22249735","pdf_url":"https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008079276","display_name":"Jawad-ur-Rehman Chughtai","orcid":"https://orcid.org/0000-0003-0430-4661"},"institutions":[{"id":"https://openalex.org/I134276161","display_name":"Pakistan Institute of Engineering and Applied Sciences","ror":"https://ror.org/04d4mbk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I134276161"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Jawad-ur-Rehman Chughtai","raw_affiliation_strings":["Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan","Digital Disruption Lab, DCIS, PIEAS, Islamabad 44000, Pakistan"],"raw_orcid":"https://orcid.org/0000-0003-0430-4661","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan","institution_ids":["https://openalex.org/I134276161"]},{"raw_affiliation_string":"Digital Disruption Lab, DCIS, PIEAS, Islamabad 44000, Pakistan","institution_ids":["https://openalex.org/I134276161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085263869","display_name":"Irfan Ul Haq","orcid":"https://orcid.org/0000-0002-5142-3965"},"institutions":[{"id":"https://openalex.org/I134276161","display_name":"Pakistan Institute of Engineering and Applied Sciences","ror":"https://ror.org/04d4mbk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I134276161"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Irfan ul Haq","raw_affiliation_strings":["Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan","Digital Disruption Lab, DCIS, PIEAS, Islamabad 44000, Pakistan"],"raw_orcid":"https://orcid.org/0000-0002-5142-3965","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan","institution_ids":["https://openalex.org/I134276161"]},{"raw_affiliation_string":"Digital Disruption Lab, DCIS, PIEAS, Islamabad 44000, Pakistan","institution_ids":["https://openalex.org/I134276161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035335908","display_name":"Saif ul Islam","orcid":"https://orcid.org/0000-0002-9546-4195"},"institutions":[{"id":"https://openalex.org/I197827452","display_name":"Institute of Space Technology","ror":"https://ror.org/01tmmzv45","country_code":"PK","type":"education","lineage":["https://openalex.org/I197827452"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Saif ul Islam","raw_affiliation_strings":["Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan"],"raw_orcid":"https://orcid.org/0000-0002-9546-4195","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan","institution_ids":["https://openalex.org/I197827452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091401182","display_name":"Abdullah Gani","orcid":"https://orcid.org/0000-0002-4388-020X"},"institutions":[{"id":"https://openalex.org/I161371597","display_name":"Universiti of Malaysia Sabah","ror":"https://ror.org/040v70252","country_code":"MY","type":"education","lineage":["https://openalex.org/I161371597"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Abdullah Gani","raw_affiliation_strings":["Faculty of Computing and Informatics, University Malaysia Sabah, Labuan 88400, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-4388-020X","affiliations":[{"raw_affiliation_string":"Faculty of Computing and Informatics, University Malaysia Sabah, Labuan 88400, Malaysia","institution_ids":["https://openalex.org/I161371597"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091401182"],"corresponding_institution_ids":["https://openalex.org/I161371597"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":0.4664,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.57540832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"22","issue":"24","first_page":"9735","last_page":"9735"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9943000078201294,"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/feature","display_name":"Feature (linguistics)","score":0.6975529789924622},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6938261985778809},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6685048341751099},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6534610986709595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.614465594291687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6103141903877258},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6028820276260376},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5487150549888611},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48829078674316406},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.48663005232810974},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4711315333843231},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.445125550031662},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4400709271430969},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4336235523223877},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36146315932273865},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1850036382675171},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1514279544353485}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6975529789924622},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6938261985778809},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6685048341751099},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6534610986709595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614465594291687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6103141903877258},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6028820276260376},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5487150549888611},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48829078674316406},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.48663005232810974},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4711315333843231},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.445125550031662},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4400709271430969},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4336235523223877},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36146315932273865},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1850036382675171},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1514279544353485},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22249735","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22249735","pdf_url":"https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36560104","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36560104","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:f5ce74aaf5e0429588b16d895ea3cf87","is_oa":true,"landing_page_url":"https://doaj.org/article/f5ce74aaf5e0429588b16d895ea3cf87","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 24, p 9735 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/24/9735/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22249735","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Sensors; Volume 22; Issue 24; Pages: 9735","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9781256","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9781256","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22249735","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22249735","pdf_url":"https://www.mdpi.com/1424-8220/22/24/9735/pdf?version=1670840998","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7200000286102295,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312127035.pdf"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W628224726","https://openalex.org/W1977177161","https://openalex.org/W2002033255","https://openalex.org/W2008559906","https://openalex.org/W2064675550","https://openalex.org/W2077963913","https://openalex.org/W2079735306","https://openalex.org/W2144475703","https://openalex.org/W2157331557","https://openalex.org/W2177066871","https://openalex.org/W2344029946","https://openalex.org/W2590835266","https://openalex.org/W2595642159","https://openalex.org/W2762384255","https://openalex.org/W2778869053","https://openalex.org/W2789876780","https://openalex.org/W2792123074","https://openalex.org/W2799109291","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2899962859","https://openalex.org/W2907984235","https://openalex.org/W2915300743","https://openalex.org/W2921319277","https://openalex.org/W2921532413","https://openalex.org/W2932292895","https://openalex.org/W2942121324","https://openalex.org/W2944851425","https://openalex.org/W2945989849","https://openalex.org/W2946563989","https://openalex.org/W2954107114","https://openalex.org/W2962834725","https://openalex.org/W2965832807","https://openalex.org/W2966819461","https://openalex.org/W2969876583","https://openalex.org/W2972581457","https://openalex.org/W2976882027","https://openalex.org/W2981664222","https://openalex.org/W3005353079","https://openalex.org/W3029460886","https://openalex.org/W3033172001","https://openalex.org/W3034022599","https://openalex.org/W3039467114","https://openalex.org/W3041279471","https://openalex.org/W3084828613","https://openalex.org/W3089815870","https://openalex.org/W3097856495","https://openalex.org/W3102272367","https://openalex.org/W3106295757","https://openalex.org/W3117031190","https://openalex.org/W3128242493","https://openalex.org/W3128586590","https://openalex.org/W3190032105","https://openalex.org/W4224991288","https://openalex.org/W6779948358"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W2891633941","https://openalex.org/W3202800081","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W4390971112","https://openalex.org/W3036530763","https://openalex.org/W1514365828","https://openalex.org/W3204228978","https://openalex.org/W2155806188"],"abstract_inverted_index":{"Travel":[0],"time":[1,91,153],"prediction":[2],"is":[3,23,141],"essential":[4],"to":[5,86,107,130,143,148,169],"intelligent":[6],"transportation":[7],"systems":[8],"directly":[9],"affecting":[10],"smart":[11],"cities":[12],"and":[13,41,75,117,186,206],"autonomous":[14],"vehicles.":[15],"Accurately":[16],"predicting":[17],"traffic":[18,108],"based":[19,62],"on":[20,63],"heterogeneous":[21,160],"factors":[22],"highly":[24],"beneficial":[25],"but":[26],"remains":[27],"a":[28,69,76,137],"challenging":[29],"problem.":[30],"The":[31,93,155,192],"literature":[32],"shows":[33],"significant":[34,166],"performance":[35,127,156],"improvements":[36,167],"when":[37],"traditional":[38],"machine":[39],"learning":[40,43,50,60,103],"deep":[42,102,133,198],"models":[44,104],"are":[45,105,128],"combined":[46],"using":[47,162],"an":[48,58],"ensemble":[49,59,161],"approach.":[51],"This":[52],"research":[53],"mainly":[54],"contributes":[55],"by":[56,82],"proposing":[57],"model":[61,123],"hybridized":[64,132,145,197],"feature":[65,115,134,146,199],"spaces":[66,116,147],"obtained":[67,110],"from":[68,111],"bidirectional":[70,77],"long":[71],"short-term":[72],"memory":[73],"module":[74],"gated":[78],"recurrent":[79],"unit,":[80],"followed":[81],"support":[83,138],"vector":[84,139],"regression":[85],"produce":[87,203],"the":[88,114,122,125,144,150,170,176,187,196,210],"final":[89,151],"travel":[90,152],"prediction.":[92,154],"proposed":[94,159],"approach":[95],"consists":[96],"of":[97,121,157,175,189],"three":[98],"stages-initially,":[99],"six":[100],"state-of-the-art":[101],"applied":[106,142],"data":[109,164],"sensors.":[112],"Then":[113],"decision":[118],"scores":[119],"(outputs)":[120],"with":[124],"highest":[126],"fused":[129],"obtain":[131],"spaces.":[135],"Finally,":[136],"regressor":[140],"get":[149],"our":[158],"test":[163],"showed":[165],"compared":[168],"baseline":[171,212],"techniques":[172],"in":[173],"terms":[174],"root":[177],"mean":[178,182],"square":[179],"error":[180,184],"(53.87\u00b13.50),":[181],"absolute":[183],"(12.22\u00b11.35)":[185],"coefficient":[188],"determination":[190],"(0.99784\u00b10.00019).":[191],"results":[193,208],"demonstrated":[194],"that":[195],"space":[200],"concept":[201],"could":[202],"more":[204],"stable":[205],"superior":[207],"than":[209],"other":[211],"techniques.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
