{"id":"https://openalex.org/W7081629310","doi":"https://doi.org/10.21227/fdj3-qw88","title":"\"All_Div_BD - Heatstroke Prediction Dataset\"","display_name":"\"All_Div_BD - Heatstroke Prediction Dataset\"","publication_year":2025,"publication_date":"2025-09-13","ids":{"openalex":"https://openalex.org/W7081629310","doi":"https://doi.org/10.21227/fdj3-qw88"},"language":"en","primary_location":{"id":"doi:10.21227/fdj3-qw88","is_oa":true,"landing_page_url":"https://doi.org/10.21227/fdj3-qw88","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/fdj3-qw88","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Md Saidur Rahman Kohinoor","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Md Saidur Rahman Kohinoor","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"FAHAD AHMED RUHAN","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"FAHAD AHMED RUHAN","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Md Istiaque Khalique","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Md Istiaque Khalique","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Tajwar Elahi Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tajwar Elahi Chowdhury","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6991999745368958,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6991999745368958,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.02710000053048134,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.01600000075995922,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wet-bulb-globe-temperature","display_name":"Wet-bulb globe temperature","score":0.6987000107765198},{"id":"https://openalex.org/keywords/heatstroke","display_name":"Heatstroke","score":0.5460000038146973},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.46700000762939453},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.45500001311302185},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4456000030040741},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.37389999628067017},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.3711000084877014},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3346000015735626}],"concepts":[{"id":"https://openalex.org/C75043328","wikidata":"https://www.wikidata.org/wiki/Q910009","display_name":"Wet-bulb globe temperature","level":3,"score":0.6987000107765198},{"id":"https://openalex.org/C2776153327","wikidata":"https://www.wikidata.org/wiki/Q337554","display_name":"Heatstroke","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4837999939918518},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3619999885559082},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27970001101493835},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C2777421447","wikidata":"https://www.wikidata.org/wiki/Q17517","display_name":"Mobile phone","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.25850000977516174},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.25780001282691956},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.2535000145435333},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/fdj3-qw88","is_oa":true,"landing_page_url":"https://doi.org/10.21227/fdj3-qw88","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.21227/fdj3-qw88","is_oa":true,"landing_page_url":"https://doi.org/10.21227/fdj3-qw88","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"\"Heatstroke":[0],"is":[1,25,132],"a":[2,41,51,126,136,169,179,191],"critical":[3],"public":[4],"health":[5],"concern":[6],"intensified":[7],"by":[8,98],"global":[9],"warming,":[10],"especially":[11],"in":[12,134,200],"rural":[13],"areas,":[14],"where":[15],"access":[16],"to":[17,28,46,119,184],"traditional":[18],"Wet":[19],"Bulb":[20],"Globe":[21],"Temperature":[22],"(WBGT)":[23],"devices":[24],"limited":[26],"due":[27],"their":[29],"cost":[30],"and":[31,43,64,77,81,95,104,129,146,154,193],"resource-heavy":[32],"settings.":[33],"To":[34],"address":[35],"the":[36,100,110,130],"concern,":[37],"this":[38],"study":[39],"proposes":[40],"scalable":[42,192],"cost-effective":[44,194],"alternative":[45],"expensive":[47],"equipment.":[48],"It":[49,152],"presents":[50],"mobile":[52,138],"regression":[53,84],"framework":[54,189],"equipped":[55],"with":[56,109,158],"context-aware":[57],"sensing":[58,175],"for":[59,181,196],"real-time":[60,143],"forecasting":[61],"of":[62,71,141,163,172],"WBGT":[63,144],"heatstroke":[65],"risk":[66,198],"assessment.":[67],"Using":[68],"ten":[69],"years":[70],"meteorological":[72],"data":[73],"from":[74],"NASA":[75],"POWER":[76],"OpenWeatherMap,":[78],"we":[79,167],"trained":[80],"evaluated":[82],"four":[83],"models.":[85],"The":[86,121],"random":[87],"forest":[88],"model":[89,131],"outperformed":[90],"linear":[91],"regression,":[92],"k-nearest":[93],"neighbors,":[94],"decision":[96],"trees":[97],"achieving":[99],"highest":[101],"R$^{2}$":[102,106],"($99.997\\\\%$)":[103],"adjusted":[105],"score,":[107],"along":[108],"lowest":[111],"error":[112],"metrics":[113],"(MSE,":[114],"RMSE,":[115],"MAE,":[116],"MAPE":[117],"close":[118],"zero).":[120],"results":[122],"are":[123],"validated":[124],"through":[125],"10-fold":[127],"cross-validation,":[128],"deployed":[133],"HeatMeter,":[135],"lightweight":[137],"application":[139],"capable":[140],"providing":[142],"predictions":[145],"context-sensitive":[147],"personalized":[148],"heat":[149,197],"safety":[150],"recommendations.":[151],"tested":[153],"demonstrated":[155],"high":[156],"responsiveness":[157],"an":[159],"average":[160],"inference":[161],"time":[162],"$0.0067$":[164],"seconds.":[165],"Additionally,":[166],"propose":[168],"novel":[170],"use":[171],"ambient":[173],"light":[174],"on":[176],"smartphones":[177],"as":[178],"proxy":[180],"solar":[182],"radiation":[183],"eliminate":[185],"hardware":[186],"dependency.":[187],"This":[188],"demonstrates":[190],"solution":[195],"assessment":[199],"low-resource":[201],"environments.\"":[202]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}