{"id":"https://openalex.org/W2551332719","doi":"https://doi.org/10.1186/s40537-016-0054-3","title":"TreeNet analysis of human stress behavior using socio-mobile data","display_name":"TreeNet analysis of human stress behavior using socio-mobile data","publication_year":2016,"publication_date":"2016-11-16","ids":{"openalex":"https://openalex.org/W2551332719","doi":"https://doi.org/10.1186/s40537-016-0054-3","mag":"2551332719"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-016-0054-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-016-0054-3","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-016-0054-3","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-016-0054-3","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013943928","display_name":"B. Padmaja","orcid":null},"institutions":[{"id":"https://openalex.org/I138535024","display_name":"Aeronautical Development Agency","ror":"https://ror.org/016d6bq56","country_code":"IN","type":"government","lineage":["https://openalex.org/I138535024"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"B. Padmaja","raw_affiliation_strings":["Department of CSE, Institute of Aeronautical Enginnering, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE, Institute of Aeronautical Enginnering, Hyderabad, India","institution_ids":["https://openalex.org/I138535024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103059248","display_name":"V. V. Rama Prasad","orcid":"https://orcid.org/0000-0003-0749-2442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V. V. Rama Prasad","raw_affiliation_strings":["Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032763450","display_name":"K. V. N. Sunitha","orcid":"https://orcid.org/0000-0002-1790-8678"},"institutions":[{"id":"https://openalex.org/I2802287669","display_name":"University College for Women","ror":"https://ror.org/02ny12416","country_code":"IN","type":"education","lineage":["https://openalex.org/I2802287669"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. V. N. Sunitha","raw_affiliation_strings":["Department of CSE, BVRITH College of Engineering for Women, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE, BVRITH College of Engineering for Women, Hyderabad, India","institution_ids":["https://openalex.org/I2802287669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013943928"],"corresponding_institution_ids":["https://openalex.org/I138535024"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":1.5121,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.84409652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"3","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10475","display_name":"Psychological Well-being and Life Satisfaction","score":0.949999988079071,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10168","display_name":"COVID-19 and Mental Health","score":0.9498000144958496,"subfield":{"id":"https://openalex.org/subfields/3203","display_name":"Clinical Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7483757734298706},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.57588130235672},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.4327622950077057},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4161175787448883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2200639247894287}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483757734298706},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.57588130235672},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.4327622950077057},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4161175787448883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2200639247894287},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1186/s40537-016-0054-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-016-0054-3","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-016-0054-3","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1186/s40537-016-0054-3","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-016-0054-3","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-016-0054-3","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2551332719.pdf","grobid_xml":"https://content.openalex.org/works/W2551332719.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W565833860","https://openalex.org/W617737805","https://openalex.org/W641132676","https://openalex.org/W950821216","https://openalex.org/W1490463471","https://openalex.org/W1616740115","https://openalex.org/W1625870857","https://openalex.org/W1969606502","https://openalex.org/W1973727274","https://openalex.org/W1985541164","https://openalex.org/W1996221647","https://openalex.org/W2012909434","https://openalex.org/W2042324951","https://openalex.org/W2044440339","https://openalex.org/W2061901927","https://openalex.org/W2070493638","https://openalex.org/W2080366222","https://openalex.org/W2091413411","https://openalex.org/W2099955253","https://openalex.org/W2102709459","https://openalex.org/W2105036571","https://openalex.org/W2113555622","https://openalex.org/W2116016271","https://openalex.org/W2116488983","https://openalex.org/W2119839723","https://openalex.org/W2122330440","https://openalex.org/W2122338287","https://openalex.org/W2139434784","https://openalex.org/W2163753340","https://openalex.org/W2226068710","https://openalex.org/W2337351554","https://openalex.org/W2415552970","https://openalex.org/W2539690336","https://openalex.org/W2540118684","https://openalex.org/W2907515539","https://openalex.org/W2963285709","https://openalex.org/W3147976114","https://openalex.org/W4249304898","https://openalex.org/W7055517062"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"Human":[0],"behavior":[1],"is":[2,119],"essentially":[3],"social":[4,20,29,33,76,97],"and":[5,22,32,84,117,135,151,171],"humans":[6],"start":[7],"their":[8],"daily":[9],"routines":[10],"by":[11],"interacting":[12],"with":[13,144],"others.":[14],"There":[15],"are":[16],"many":[17],"forms":[18],"of":[19,54,58,65,86,107,164],"interactions":[21],"we":[23,42,71,125,153],"have":[24,88,126,140],"used":[25,127],"mobile":[26,44],"phone":[27,45],"based":[28],"interaction":[30],"features":[31],"surveys":[34],"for":[35,62,132],"finding":[36],"human":[37],"stress":[38,93],"behavior.":[39],"For":[40],"this,":[41],"gathered":[43],"call":[46],"logs":[47],"data":[48],"set":[49],"containing":[50],"111,444":[51],"voice":[52],"calls":[53],"131":[55],"adult":[56],"members":[57],"a":[59,63,96],"living":[60],"community":[61],"period":[64],"more":[66,115,120,158],"than":[67],"5":[68,75],"months.":[69],"And":[70],"identified":[72],"that":[73],"top":[74],"network":[77],"measures":[78],"like":[79],"hierarchy,":[80],"density,":[81],"farness,":[82],"reachability":[83],"eigenvector":[85],"individuals":[87],"profound":[89],"influence":[90],"on":[91],"individuals\u2019":[92],"levels":[94],"in":[95,103],"network.":[98],"If":[99],"an":[100],"ego":[101,113],"lies":[102],"the":[104,112],"shortest":[105],"path":[106],"all":[108],"other":[109],"alters":[110],"then":[111],"receives":[114],"information":[116],"hence":[118],"stressed.":[121],"In":[122],"this":[123],"paper,":[124],"TreeNet":[128,155],"machine":[129],"learning":[130],"algorithm":[131],"its":[133],"speed":[134],"immunity":[136],"to":[137,156,167],"outliers.":[138],"We":[139],"tested":[141],"our":[142],"results":[143],"another":[145],"Random":[146],"Forest":[147],"classifier":[148],"as":[149],"well":[150],"yet,":[152],"found":[154],"be":[157,163],"efficient.":[159],"This":[160],"research":[161],"can":[162],"vital":[165],"importance":[166],"economists,":[168],"professionals,":[169],"analysts,":[170],"policy":[172],"makers.":[173]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}