{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:32:22Z","timestamp":1754155942648,"version":"3.41.2"},"reference-count":41,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["LHT"],"published-print":{"date-parts":[[2022,11,22]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Epidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.<\/jats:p><\/jats:sec>","DOI":"10.1108\/lht-02-2021-0063","type":"journal-article","created":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T04:56:12Z","timestamp":1624942572000},"page":"1159-1178","source":"Crossref","is-referenced-by-count":4,"title":["A deep neural network based context-aware smart epidemic surveillance in smart cities"],"prefix":"10.1108","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7810-799X","authenticated-orcid":false,"given":"Harsuminder Kaur","family":"Gill","sequence":"first","affiliation":[]},{"given":"Vivek Kumar","family":"Sehgal","sequence":"additional","affiliation":[]},{"given":"Anil Kumar","family":"Verma","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"key2022112909400355900_ref001","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3038416","article-title":"Deep learning for heterogeneous human activity recognition in complex IoT applications","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"key":"key2022112909400355900_ref002","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106941","article-title":"Evaluation framework for smart disaster response systems in uncertainty environment","volume":"145","year":"2020","journal-title":"Mechanical Systems and Signal Processing"},{"key":"key2022112909400355900_ref003","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106647","article-title":"FSS-2019-nCov: a deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection","volume":"212","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"key2022112909400355900_ref004","doi-asserted-by":"publisher","first-page":"120431","DOI":"10.1016\/j.techfore.2020.120431","article-title":"An intelligent framework using disruptive technologies for COVID-19 analysis","volume":"163","year":"2021","journal-title":"Technological Forecasting and Social Change"},{"issue":"1","key":"key2022112909400355900_ref005","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1389-3","article-title":"A dynamic neural network model for predicting risk of Zika in real time","volume":"17","year":"2019","journal-title":"BMC Medicine"},{"issue":"11\u201312","key":"key2022112909400355900_ref006","doi-asserted-by":"publisher","first-page":"2971","DOI":"10.1016\/j.mcm.2013.03.007","article-title":"Estimation of parameters based on artificial neural networks and threshold of HIV\/AIDS epidemic system in Cuba","volume":"57","year":"2013","journal-title":"Mathematical and Computer Modelling"},{"issue":"2","key":"key2022112909400355900_ref007","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.chaos.2005.01.064","article-title":"Prediction of SARS epidemic by BP neural networks with online prediction strategy","volume":"26","year":"2005","journal-title":"Chaos, Solitons and Fractals"},{"key":"key2022112909400355900_ref008","doi-asserted-by":"crossref","unstructured":"Caterini, A.L. and Chang, D.E. (2018), \u201cRecurrent neural networks\u201d, in SpringerBriefs in Computer Science, pp. 59-79, doi: 10.1007\/978-3-319-75304-1_5.","DOI":"10.1007\/978-3-319-75304-1_5"},{"key":"key2022112909400355900_ref009","doi-asserted-by":"publisher","first-page":"120559","DOI":"10.1016\/j.techfore.2020.120559","article-title":"An ethical framework for big data and smart cities","volume":"165","year":"2021","journal-title":"Technological Forecasting and Social Change"},{"key":"key2022112909400355900_ref010","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/icoei.2019.8862581","article-title":"Prediction of dengue using recurrent neural network","year":"2019"},{"key":"key2022112909400355900_ref011","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/07391102.2021.1875049","article-title":"ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images","year":"2021","journal-title":"Journal of Biomolecular Structure and Dynamics"},{"key":"key2022112909400355900_ref012","unstructured":"European Commision (2015), \u201cSmart cities\/European commission, digital agenda for Europe\u201d, available at: https:\/\/ec.europa.eu\/digital-agenda\/en\/smart-cities (accessed 13 February 2021)."},{"article-title":"A fuzzy neuro approach to identify Diarrhea epidemic in Bangladesh","year":"2014","key":"key2022112909400355900_ref013","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEV.2014.6850731"},{"issue":"15\u201316","key":"key2022112909400355900_ref014","doi-asserted-by":"publisher","first-page":"10733","DOI":"10.1007\/s11042-020-08649-4","article-title":"A context sensitive security framework for enterprise multimedia placement in fog computing environment","volume":"79","year":"2020","journal-title":"Multimedia Tools and Applications"},{"issue":"4","key":"key2022112909400355900_ref015","doi-asserted-by":"publisher","first-page":"35","DOI":"10.14257\/ijsh.2015.9.4.04","article-title":"Design of chemical industrial park integrated information management platform based on cloud computing and IOT (The Internet of Things) technologies","volume":"9","year":"2015","journal-title":"International Journal of Smart Home"},{"issue":"8","key":"key2022112909400355900_ref016","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","year":"1997","journal-title":"Neural Computation"},{"article-title":"Predicting the outbreak of the hand-foot-mouth diseases in China using recurrent neural network","year":"2019","key":"key2022112909400355900_ref017","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2019.8904736"},{"issue":"1","key":"key2022112909400355900_ref018","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1080\/03091902.2020.1853839","article-title":"Machine learning in primary care: potential to improve public health","volume":"45","year":"2020","journal-title":"Journal of Medical Engineering and Technology"},{"key":"key2022112909400355900_ref019","unstructured":"Kulkarni, T.D., Whitney, W.F., Kohli, P. and Tenenbaum, J.B. (2015), \u201cDeep convolutional inverse graphics network\u201d, in Advances in Neural Information Processing Systems, pp. 2539-2547."},{"issue":"11","key":"key2022112909400355900_ref020","doi-asserted-by":"publisher","first-page":"2690","DOI":"10.1080\/21645515.2020.1734397","article-title":"Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks","volume":"16","year":"2020","journal-title":"Human Vaccines and Immunotherapeutics"},{"key":"key2022112909400355900_ref021","doi-asserted-by":"publisher","first-page":"3638","DOI":"10.1109\/CCDC.2009.5192879","article-title":"Wavelet neural network predication for epidemic outbreak","year":"2009"},{"key":"key2022112909400355900_ref022","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-21803-4_73","article-title":"A study on graph-structured recurrent neural networks and sparsification with application to epidemic forecasting","volume-title":"Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing","year":"2020"},{"key":"key2022112909400355900_ref023","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.epidem.2014.07.003","article-title":"Eight challenges for network epidemic models","volume":"10","year":"2015","journal-title":"Epidemics"},{"issue":"3","key":"key2022112909400355900_ref024","doi-asserted-by":"publisher","DOI":"10.1145\/3057266","article-title":"Fog computing for sustainable smart cities: a survey","volume":"50","year":"2017","journal-title":"ACM Computing Surveys"},{"key":"key2022112909400355900_ref025","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/IranianCEE.2014.6999855","article-title":"Prediction of seasonal influenza epidemics in Tehran using artificial neural networks","year":"2014"},{"issue":"2","key":"key2022112909400355900_ref026","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/s11227-017-2163-y","article-title":"TDRM: tensor-based data representation and mining for healthcare data in cloud computing environments","volume":"74","year":"2018","journal-title":"Journal of Supercomputing"},{"issue":"2","key":"key2022112909400355900_ref027","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1080\/17517575.2017.1287429","article-title":"An effective framework for finding similar cases of dengue from audio and text data using domain thesaurus and case base reasoning","volume":"12","year":"2018","journal-title":"Enterprise Information Systems"},{"key":"key2022112909400355900_ref028","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/ICCMC.2017.8282721","article-title":"An artificial neural network approach for classification of vector-borne diseases","year":"2018"},{"key":"key2022112909400355900_ref029","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3055804","article-title":"Trustworthy and intelligent COVID-19 diagnostic IoMT through XR and deep learning-based clinic data access","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"key2022112909400355900_ref030","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.01.038","article-title":"Shared data-aware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using Aneka","volume":"106","year":"2020","journal-title":"Future Generation Computer Systems"},{"key":"key2022112909400355900_ref031","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110214","article-title":"Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: case studies in Russia, Peru and Iran","volume":"140","year":"2020","journal-title":"Chaos, Solitons and Fractals"},{"key":"key2022112909400355900_ref032","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3201\/eid1301.060779","article-title":"Epidemics after natural disasters","volume-title":"Emerging Infectious Diseases","year":"2007"},{"issue":"2","key":"key2022112909400355900_ref033","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MIC.2017.36","article-title":"Fog orchestration for internet of things services","volume":"21","year":"2017","journal-title":"IEEE Internet Computing"},{"key":"key2022112909400355900_ref034","unstructured":"World Health Organization (2020), \u201cWHO EMRO | Infectious diseases | Health topics\u201d, available at: http:\/\/www.emro.who.int\/health-topics\/infectious-diseases\/index.html (accessed 13 February 2021)."},{"key":"key2022112909400355900_ref035","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1109\/ICMLC.2010.5580851","article-title":"A neural netwok based approach to detect influenza epidemics using search engine query data","year":"2010"},{"issue":"2","key":"key2022112909400355900_ref036","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1109\/TII.2014.2306382","article-title":"Ubiquitous data accessing method in IoT-based information system for emergency medical services","volume":"10","year":"2014","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"5","key":"key2022112909400355900_ref037","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1177\/1420326X20910408","article-title":"The 2019-nCoV epidemic control strategies and future challenges of building healthy smart cities","volume":"29","year":"2020","journal-title":"Indoor and Built Environment"},{"key":"key2022112909400355900_ref038","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1109\/ACCESS.2017.2771798","article-title":"Influenza activity surveillance based on multiple regression model and artificial neural network","volume":"6","year":"2017","journal-title":"IEEE Access"},{"issue":"4","key":"key2022112909400355900_ref039","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1080\/19475705.2013.853325","article-title":"A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm","volume":"6","year":"2015","journal-title":"Geomatics, Natural Hazards and Risk"},{"key":"key2022112909400355900_ref040","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3131-8","article-title":"Attention-based recurrent neural network for influenza epidemic prediction","volume":"20","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"key2022112909400355900_ref041","doi-asserted-by":"crossref","unstructured":"Zika Virus Epidemic | Kaggle (2016), available at: https:\/\/www.kaggle.com\/cdc\/zika-virus-epidemic\/code (accessed 14 February 2021).","DOI":"10.1080\/14787210.2016.1245614"}],"container-title":["Library Hi Tech"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/LHT-02-2021-0063\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/LHT-02-2021-0063\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:14:05Z","timestamp":1753395245000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/lht\/article\/40\/5\/1159-1178\/261089"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,6,30]]},"published-print":{"date-parts":[[2022,11,22]]}},"alternative-id":["10.1108\/LHT-02-2021-0063"],"URL":"https:\/\/doi.org\/10.1108\/lht-02-2021-0063","relation":{},"ISSN":["0737-8831"],"issn-type":[{"type":"print","value":"0737-8831"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}