{"id":"https://openalex.org/W3019553070","doi":"https://doi.org/10.1109/smartnets48225.2019.9069766","title":"Wireless Sensor Network and Deep Learning For Prediction Greenhouse Environments","display_name":"Wireless Sensor Network and Deep Learning For Prediction Greenhouse Environments","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3019553070","doi":"https://doi.org/10.1109/smartnets48225.2019.9069766","mag":"3019553070"},"language":"en","primary_location":{"id":"doi:10.1109/smartnets48225.2019.9069766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartnets48225.2019.9069766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Smart Applications, Communications and Networking (SmartNets)","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/A5111090765","display_name":"Asmaa Ali","orcid":null},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Asmaa Ali","raw_affiliation_strings":["School of Computing, Queen's University, Kingston, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computing, Queen's University, Kingston, Ontario, Canada","institution_ids":["https://openalex.org/I204722609"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021196543","display_name":"Hossam S. Hassanein","orcid":"https://orcid.org/0000-0003-0260-8979"},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hossam S. Hassanein","raw_affiliation_strings":["School of Computing, Queen's University, Kingston, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computing, Queen's University, Kingston, Ontario, Canada","institution_ids":["https://openalex.org/I204722609"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111090765"],"corresponding_institution_ids":["https://openalex.org/I204722609"],"apc_list":null,"apc_paid":null,"fwci":2.3147,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89690618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12093","display_name":"Greenhouse Technology and Climate Control","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12093","display_name":"Greenhouse Technology and Climate Control","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/greenhouse","display_name":"Greenhouse","score":0.8667898178100586},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.8024665117263794},{"id":"https://openalex.org/keywords/atmosphere","display_name":"Atmosphere (unit)","score":0.6392094492912292},{"id":"https://openalex.org/keywords/dew-point","display_name":"Dew point","score":0.6081473231315613},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.5899662375450134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5791219472885132},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5530322194099426},{"id":"https://openalex.org/keywords/atmospheric-model","display_name":"Atmospheric model","score":0.5318816304206848},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5144784450531006},{"id":"https://openalex.org/keywords/greenhouse-gas","display_name":"Greenhouse gas","score":0.49834227561950684},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4488072395324707},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.445015549659729},{"id":"https://openalex.org/keywords/humidity","display_name":"Humidity","score":0.4292861521244049},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.39293473958969116},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23050150275230408},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1783762276172638},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13199394941329956},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09656140208244324}],"concepts":[{"id":"https://openalex.org/C32198211","wikidata":"https://www.wikidata.org/wiki/Q165044","display_name":"Greenhouse","level":2,"score":0.8667898178100586},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.8024665117263794},{"id":"https://openalex.org/C65440619","wikidata":"https://www.wikidata.org/wiki/Q177974","display_name":"Atmosphere (unit)","level":2,"score":0.6392094492912292},{"id":"https://openalex.org/C82210777","wikidata":"https://www.wikidata.org/wiki/Q178828","display_name":"Dew point","level":2,"score":0.6081473231315613},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.5899662375450134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5791219472885132},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5530322194099426},{"id":"https://openalex.org/C118365302","wikidata":"https://www.wikidata.org/wiki/Q4817115","display_name":"Atmospheric model","level":2,"score":0.5318816304206848},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5144784450531006},{"id":"https://openalex.org/C47737302","wikidata":"https://www.wikidata.org/wiki/Q167336","display_name":"Greenhouse gas","level":2,"score":0.49834227561950684},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4488072395324707},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.445015549659729},{"id":"https://openalex.org/C151420433","wikidata":"https://www.wikidata.org/wiki/Q180600","display_name":"Humidity","level":2,"score":0.4292861521244049},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39293473958969116},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23050150275230408},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1783762276172638},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13199394941329956},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09656140208244324},{"id":"https://openalex.org/C144027150","wikidata":"https://www.wikidata.org/wiki/Q48803","display_name":"Horticulture","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smartnets48225.2019.9069766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartnets48225.2019.9069766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Smart Applications, Communications and Networking (SmartNets)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6700000166893005,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1843644805","https://openalex.org/W1911672148","https://openalex.org/W1991092346","https://openalex.org/W1995203644","https://openalex.org/W2042125329","https://openalex.org/W2056132333","https://openalex.org/W2154047296","https://openalex.org/W2576808707","https://openalex.org/W2726943725","https://openalex.org/W2979650406"],"related_works":["https://openalex.org/W2372412189","https://openalex.org/W52542889","https://openalex.org/W2377092188","https://openalex.org/W2896828128","https://openalex.org/W2335022269","https://openalex.org/W405573773","https://openalex.org/W2902918828","https://openalex.org/W2348292332","https://openalex.org/W906406970","https://openalex.org/W2061827967"],"abstract_inverted_index":{"Greenhouses":[0],"are":[1,81,89,92,101,113],"anti-seasonal.":[2],"Particularly":[3],"in":[4,67],"regions":[5],"with":[6,109],"adverse":[7],"climate":[8],"conditions.":[9],"Controlling,":[10],"monitoring":[11],"and":[12,38,42,78,91,103,131,171],"predicting":[13,61],"a":[14,33,58,93,114],"greenhouse":[15,28,53,141],"is":[16,45],"important":[17],"to":[18,48,83,134,155],"allow":[19],"optimal":[20],"growth":[21],"condition":[22],"for":[23,29,60,64,99,142],"crops.":[24],"However,":[25],"testing":[26],"the":[27,52,96,120,123,136,140,149,157,160],"real":[30],"atmosphere":[31,63,87],"requires":[32],"lot":[34],"of":[35,122,139,159],"time,":[36],"effort":[37],"money.":[39],"The":[40,116],"modeling":[41],"simulation":[43],"approach":[44],"best":[46],"suited":[47],"predict":[49,135],"thereby":[50],"improve":[51],"environment.":[54],"This":[55],"paper":[56],"presents":[57],"model":[59,124,166],"environmental":[62,137],"producing":[65],"tomatoes":[66],"greenhouse.":[68],"Several":[69],"factor":[70],"such":[71],"as:":[72],"air":[73],"temperature,":[74],"humidity,":[75],"barometric":[76],"pressure,":[77],"dew":[79],"point":[80],"needed":[82],"be":[84],"monitered.":[85],"Since":[86],"pattern":[88],"complex":[90],"nonlinear":[94],"system,":[95],"customary":[97],"methods":[98],"prediction":[100],"inefficient":[102],"ineffective.":[104],"Recurrent":[105],"neural":[106],"network":[107],"(RNN)":[108],"long":[110],"short-term":[111],"memory":[112],"solution.":[115],"proposed":[117,161],"RNN":[118],"evaluate":[119,156],"performance":[121,158],"by":[125],"using":[126],"different":[127],"neurons,":[128],"hidden":[129],"layers":[130],"transfer":[132],"functions":[133],"parameters":[138],"an":[143],"entire":[144],"year":[145],"ahead.":[146],"By":[147],"utilizing":[148],"Root":[150],"Mean":[151],"square":[152],"error":[153],"(RMSE)":[154],"model,":[162],"results":[163],"show":[164],"our":[165],"has":[167],"very":[168],"low":[169],"RMSE":[170],"time.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}