{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:34:59Z","timestamp":1774528499675,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pers Ubiquit Comput"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00779-022-01678-w","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T12:03:00Z","timestamp":1646827380000},"page":"909-916","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fog-based framework for diabetes prediction using hybrid ANFIS model\u00a0in cloud environment"],"prefix":"10.1007","volume":"27","author":[{"given":"Dipesh","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nirupama","family":"Mandal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugal","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"1678_CR1","volume-title":"Machine-to-machine to the Internet of Things: introduction to a new age of intelligence, Amsterdam","author":"J H\u00f6ller","year":"2014","unstructured":"H\u00f6ller J, Tsiatsis V, Mulligan C, Karnouskos S, Avesand S, Boyle D (2014) Machine-to-machine to the Internet of Things: introduction to a new age of intelligence, Amsterdam. Elsevier, The Netherlands"},{"issue":"8","key":"1678_CR2","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/MC.2016.245","volume":"49","author":"AV Dastjerdi","year":"2016","unstructured":"Dastjerdi AV, Buyya R (2016) Fog computing: helping the Internet of Things realize its potential. Computer 49(8):112\u2013116","journal-title":"Computer"},{"key":"1678_CR3","doi-asserted-by":"crossref","unstructured":"Shi Y (2015) The fog computing service for healthcare. 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech). IEEE","DOI":"10.1109\/Ubi-HealthTech.2015.7203325"},{"issue":"1","key":"1678_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4103\/2468-8827.184853","volume":"1","author":"G Roglic","year":"2016","unstructured":"Roglic G (2016) \u201cWHO Global report on diabetes\u201d, a summary. Int J Noncommunicable Dis 1(1):3","journal-title":"Int J Noncommunicable Dis"},{"key":"1678_CR5","doi-asserted-by":"crossref","unstructured":"Emon MU, Imran AM, Islam R, Keya MS, Zannat R, Ohidujjaman (2021) \u201cPerformance analysis of chronic kidney disease through machine learning approaches. 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp 713\u2013719","DOI":"10.1109\/ICICT50816.2021.9358491"},{"key":"1678_CR6","doi-asserted-by":"crossref","unstructured":"Kavitha M, Gnaneswar G, Dinesh R, Sai YR, Suraj RS (2021) Heart disease prediction using hybrid machine learning model. 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp 1329\u20131333","DOI":"10.1109\/ICICT50816.2021.9358597"},{"key":"1678_CR7","doi-asserted-by":"crossref","unstructured":"Ramanujam E, Chandrakumar T, Thivyadharsine KT, Varsha D (2020) A multilingual decision support system for early detection of diabetes using machine learning approach: case study for rural Indian people. 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp 17\u201321","DOI":"10.1109\/ICRCICN50933.2020.9296187"},{"key":"1678_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/RBME.2020.2993591","volume":"14","author":"S Lekha","year":"2021","unstructured":"Lekha S, Suchetha M (2021) Recent advancements and future prospects on E-nose sensors technology and machine learning approaches for non-invasive diabetes diagnosis: a review\u201d. IEEE Rev Biomed Eng 14:127\u2013138","journal-title":"IEEE Rev Biomed Eng"},{"key":"1678_CR9","doi-asserted-by":"crossref","unstructured":"Emon MU, Keya MS, Kaiser MS, islam MA, Tanha T, Zulfiker MS (2021) Primary stage of diabetes prediction using machine learning approaches. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)","DOI":"10.1109\/ICAIS50930.2021.9395968"},{"key":"1678_CR10","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.still.2014.11.002","volume":"146","author":"B Kuang","year":"2015","unstructured":"Kuang B, Tekin Y, Mouazen AM (2015) Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content. Soil Tillage Res 146:243\u2013252","journal-title":"Soil Tillage Res"},{"key":"1678_CR11","doi-asserted-by":"publisher","first-page":"105041","DOI":"10.1016\/j.compag.2019.105041","volume":"167","author":"K Khosravi","year":"2019","unstructured":"Khosravi K, Daggupati P, Alami MT, Awadh SM, Ghareb MI, Panahi M, Pham BT, Rezaie F, Qi C, Yaseen ZM (2019) Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: a case study in Iraq. Comput Electron Agric 167:105041","journal-title":"Comput Electron Agric"},{"key":"1678_CR12","doi-asserted-by":"publisher","first-page":"74471","DOI":"10.1109\/ACCESS.2019.2920916","volume":"7","author":"ZM Yaseen","year":"2019","unstructured":"Yaseen ZM, Mohtar WHMW, Ameen AMS, Ebtehaj I, Razali SFM, Bonakdari H, Salih SQ, Al-Ansari N, Shahid S (2019) Implementation of univariate paradigm for stream_ow simulation using hybrid data-driven model: case study in tropical region. IEEE Access 7:74471\u201374481","journal-title":"IEEE Access"},{"key":"1678_CR13","doi-asserted-by":"crossref","unstructured":"Pavithra D, Jayanthi AN (2020) An improved adaptive neuro fuzzy interference system for the detection of autism spectrum disorder. J Ambient Intell Human Comput 1\u201313","DOI":"10.1007\/s12652-020-02332-0"},{"issue":"4","key":"1678_CR14","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s00521-016-2746-1","volume":"30","author":"M Hasanipanah","year":"2018","unstructured":"Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015\u20131024","journal-title":"Neural Comput Appl"},{"key":"1678_CR15","doi-asserted-by":"publisher","first-page":"105279","DOI":"10.1016\/j.compag.2020.105279","volume":"170","author":"P Aghelpour","year":"2020","unstructured":"Aghelpour P, Bahrami-Pichaghchi H, Kisi O (2020) Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Comput Electron Agric 170:105279","journal-title":"Comput Electron Agric"},{"issue":"4","key":"1678_CR16","doi-asserted-by":"publisher","first-page":"780","DOI":"10.3390\/app9040780","volume":"9","author":"K Elbaz","year":"2019","unstructured":"Elbaz K, Shen S-L, Zhou A, Yuan D-J, Xu Y-S (2019) Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm. Appl Sci 9(4):780","journal-title":"Appl Sci"},{"key":"1678_CR17","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1016\/j.jhydrol.2019.06.065","volume":"576","author":"M Dehghani","year":"2019","unstructured":"Dehghani M, Sei A, Riahi-Madvar H (2019) Novel forecasting models for immediate-short-term to long-term in_uent _ow prediction by combining ANFIS and grey wolf optimization. J Hydrol 576:698\u2013725","journal-title":"J Hydrol"},{"key":"1678_CR18","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.jhydrol.2019.05.045","volume":"575","author":"S Maroufpoor","year":"2019","unstructured":"Maroufpoor S, Maroufpoor E, Bozorg-Haddad O, Shiri J, Yaseen ZM (2019) \u201cSoil moisture simulation using hybrid artificial intelligent model: hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J Hydrol 575:544\u2013556","journal-title":"J Hydrol"},{"issue":"2","key":"1678_CR19","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.aej.2013.01.001","volume":"52","author":"DK Ghose","year":"2013","unstructured":"Ghose DK, Panda SS, Swain PC (2013) Prediction and optimization of runoff via ANFIS and GA. Alexandria Eng J 52(2):209\u2013220","journal-title":"Alexandria Eng J"},{"key":"1678_CR20","doi-asserted-by":"crossref","unstructured":"GiaTN, Jiang M, Rahmani A, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare Internet of Things: a case study on ECG feature extraction. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp 356\u2013363","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.51"},{"key":"1678_CR21","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.future.2017.02.014","volume":"78","author":"AM Rahmani","year":"2018","unstructured":"Rahmani AM et al (2018) Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: a fog computing approach. Future Gen Comput Syst 78:641\u2013658","journal-title":"Future Gen Comput Syst"},{"issue":"1","key":"1678_CR22","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s10916-019-1495-y","volume":"44","author":"A Manocha","year":"2020","unstructured":"Manocha A, Singh R, Bhatia M (2020) Cognitive intelligence assisted fog-cloud architecture for generalized anxiety disorder (gad) prediction. J Med Syst 44(1):7","journal-title":"J Med Syst"},{"key":"1678_CR23","doi-asserted-by":"crossref","unstructured":"Nayyar A, Puri V, Nguyen NG (2019) Biosenhealth 1.0: a novel internet of medical things (iomt)-based patient health monitoring system. International Conference on Innovative Computing and Communications. Springer, pp 155\u2013164","DOI":"10.1007\/978-981-13-2324-9_16"},{"key":"1678_CR24","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.future.2019.02.055","volume":"97","author":"PH Vilela","year":"2019","unstructured":"Vilela PH, Rodrigues JJ, Solic P, Saleem K, Furtado V (2019) Performance evaluation of a fog-assisted IoT solution for e-health applications. Futur Gener Comput Syst 97:379\u2013386","journal-title":"Futur Gener Comput Syst"},{"issue":"11","key":"1678_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CC.2017.8233646","volume":"14","author":"S He","year":"2017","unstructured":"He S, Cheng B, Wang H, Huang Y, Chen J (2017) Proactive personalized services through fog-cloud computing in large-scale IoT-based healthcare application. China Commun 14(11):1\u201316","journal-title":"China Commun"},{"key":"1678_CR26","unstructured":"Constant N, Borthakur D, Abtahi M, Dubey H, Mankodiya K (2017) Fog-assisted wiot: a smart fog gateway for end-to-end analytics in wearable Internet of Things. arXiv preprint arXiv:1701.08680"},{"key":"1678_CR27","doi-asserted-by":"crossref","unstructured":"Mukherjee A, De D, Ghosh SK (2020) Fogioht: a weighted majority game theory based energy-efficient delay-sensitive fog network for Internet of Health Things. Internet of Things 100181","DOI":"10.1016\/j.iot.2020.100181"},{"key":"1678_CR28","doi-asserted-by":"crossref","unstructured":"Yannuzzi M, Milito R, Serral-Graci\u00e0 R, Montero D, Nemirovsky M (2014) Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: 2014 IEEE 19th international workshop on computer aided modeling and design of communication links and networks (CAMAD). IEEE, pp 325\u2013329","DOI":"10.1109\/CAMAD.2014.7033259"},{"key":"1678_CR29","doi-asserted-by":"crossref","unstructured":"Negash B, Gia TN, Anzanpour A, Azimi I, Jiang M, Westerlund T, Rahmani AM, Liljeberg P, Tenhunen H (2018) Leveraging fog computing for healthcare IoT. Fog computing in the Internet of Things Springer, pp 145\u2013169","DOI":"10.1007\/978-3-319-57639-8_8"},{"key":"1678_CR30","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume":"4","author":"R Eberhart","year":"1995","unstructured":"Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942\u20131948","journal-title":"Proc IEEE Int Conf Neural Netw"}],"container-title":["Personal and Ubiquitous Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00779-022-01678-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00779-022-01678-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00779-022-01678-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T14:17:11Z","timestamp":1685024231000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00779-022-01678-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1678"],"URL":"https:\/\/doi.org\/10.1007\/s00779-022-01678-w","relation":{},"ISSN":["1617-4909","1617-4917"],"issn-type":[{"value":"1617-4909","type":"print"},{"value":"1617-4917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,9]]},"assertion":[{"value":"3 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}