A new service recommendation method for agricultural industries in the fog-based Internet of Things environment using a hybrid meta-heuristic algorithm

dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.contributor.authorTu, Jiaqing
dc.contributor.authorAznoli, Fariba
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorYalcin, Senay
dc.date.accessioned2023-10-19T15:12:12Z
dc.date.available2023-10-19T15:12:12Z
dc.date.issued2022
dc.department-temp[Tu, Jiaqing] Zhejiang Coll Secur Technol, Wenzhou 325000, Peoples R China; [Aznoli, Fariba] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Yalcin, Senay] Nisantasi Univ, Dept Comp Engn, TR-34485 Istanbul, Turkey; [Navimipour, Nima Jafari] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkeyen_US
dc.description.abstractRegardless of public perceptions of the agricultural techniques, the fact is that the current agriculture industry is more data-driven, accurate, and intelligent than before. Therefore, novel technologies such as fog computing and the Internet of Things (IoT) can provide a flexible and real-time platform to meet the data-driven requirements of the current agricultural decision-makers. Smart agriculture is a new idea since IoT sensors and fog platforms can provide information about agriculture and then operate on them based on user feedback. Also, with the rapid growth of IoT, the importance of recommender systems is increased in this domain. Therefore, the main goals of this study are to improve the accuracy of agricultural service recommendations and decrease the Mean Absolute Error (MAE) in the IoT-based fog systems using collaborative filtering and artificial Artificial Bee Colony (ABC). However, many of the current methods suffer from low accuracy of recommendations of the agricultural services. The present article suggested a collaborative filtering-based approach based on the ABC and genetic operators to design an effective recommender scheme in fog-based IoT systems. The results showed that the accuracy and MAE are optimized compared to some state-of-the-art methods. They also revealed that compared to other methods, the proposed method improves ranking score by 5.8 %, precision by 5%, recall by 5.7%, intra similarity by 13%, and hamming distance by 4.8%.en_US
dc.identifier.citation2
dc.identifier.doi10.1016/j.cie.2022.108605en_US
dc.identifier.issn0360-8352
dc.identifier.issn1879-0550
dc.identifier.scopus2-s2.0-85137265395en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.cie.2022.108605
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5376
dc.identifier.volume172en_US
dc.identifier.wosWOS:000855739800001en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Industrial Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOptimizationEn_Us
dc.subjectSmart farmingen_US
dc.subjectAgricultureen_US
dc.subjectInternet of Thingsen_US
dc.subjectDesignEn_Us
dc.subjectFogen_US
dc.subjectRecommender systemen_US
dc.subjectOptimization
dc.subjectArtificial bee colonyen_US
dc.subjectDesign
dc.subjectGenetic algorithmen_US
dc.titleA new service recommendation method for agricultural industries in the fog-based Internet of Things environment using a hybrid meta-heuristic algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication

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