{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:47:26Z","timestamp":1775350046414,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030014230","type":"print"},{"value":"9783030014247","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01424-7_50","type":"book-chapter","created":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T17:07:37Z","timestamp":1538413657000},"page":"511-521","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Estimation of Air Quality Index from Seasonal Trends Using Deep Neural Network"],"prefix":"10.1007","author":[{"given":"Arjun","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anirban","family":"Mitra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumit","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudip","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"key":"50_CR1","unstructured":"The World Health Report 2000: Health Systems - Improving Performance (2000). http:\/\/www.who.int\/whr\/2000\/en\/"},{"key":"50_CR2","unstructured":"8 People Die in Delhi Every Day due to Pollution (2018). http:\/\/www.thehindu.com\/news\/cities\/Delhi\/8-people-die-in-Delhi-every-day-due-to-pollution-SC\/article17205973.ece"},{"key":"50_CR3","unstructured":"CPCB: Average Report Criteria (2018). http:\/\/www.cpcb.gov.in\/caaqm\/Auth\/frmViewReportNew.aspx"},{"key":"50_CR4","unstructured":"Pollution Index by City 2018 (2018). https:\/\/www.numbeo.com\/pollution\/rankings.jsp"},{"key":"50_CR5","unstructured":"RMSPropOptimizer (2018). https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/train\/RMSPropOptimizer"},{"issue":"4","key":"50_CR6","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1097\/MAJ.0b013e31803b900f","volume":"333","author":"T-M Chen","year":"2007","unstructured":"Chen, T.-M., Kuschner, W.G., Gokhale, J., Shofer, S.: Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects. Am. J. Med. Sci. 333(4), 249\u2013256 (2007)","journal-title":"Am. J. Med. Sci."},{"issue":"2","key":"50_CR7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/s12199-007-0018-5","volume":"13","author":"B Chen","year":"2008","unstructured":"Chen, B., Kan, H.: Air pollution and population health: a global challenge. Environ. Health Prev. Med. 13(2), 94\u2013101 (2008)","journal-title":"Environ. Health Prev. Med."},{"key":"50_CR8","doi-asserted-by":"crossref","unstructured":"Ganesh, S.S., Modali, S.H., Palreddy, S.R., Arulmozhivarman, P.: Forecasting air quality index using regression models: a case study on Delhi and Houston. In: Proceedings of the ICEI, pp. 248\u2013254 (2017)","DOI":"10.1109\/ICOEI.2017.8300926"},{"key":"50_CR9","doi-asserted-by":"crossref","unstructured":"Ganesh, S.S., Reddy, N.B., Arulmozhivarman, P.: Forecasting air quality index based on Mamdani fuzzy inference system. In: Proceedings of the ICEI, pp. 338\u2013341 (2017)","DOI":"10.1109\/ICOEI.2017.8300944"},{"key":"50_CR10","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http:\/\/www.deeplearningbook.org"},{"key":"50_CR11","doi-asserted-by":"crossref","unstructured":"Ivanov, V., Georgieva, I.: Air quality index evaluations for Sofia City. In: Proceedings of the IEEE EUROCON, pp. 920\u2013925 (2017)","DOI":"10.1109\/EUROCON.2017.8011246"},{"issue":"2","key":"50_CR12","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.envpol.2007.06.012","volume":"151","author":"M Kampa","year":"2008","unstructured":"Kampa, M., Castanas, E.: Human health effects of air pollution. Environ. Pollut. 151(2), 362\u2013367 (2008)","journal-title":"Environ. Pollut."},{"issue":"4","key":"50_CR13","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5572\/ajae.2015.9.2.101","volume":"9","author":"K Kanchan","year":"2015","unstructured":"Kanchan, K., Goyal, P.: A review on air quality indexing system. Asian J. Atmos. Environ. 9(4), 101\u2013113 (2015)","journal-title":"Asian J. Atmos. Environ."},{"key":"50_CR14","doi-asserted-by":"crossref","unstructured":"Kang, Z., Qu, Z.: Application of BP neural network optimized by genetic simulated annealing algorithm to prediction of air quality index in Lanzhou. In: Proceedings of the IEEE ICCIA, pp. 155\u2013160 (2017)","DOI":"10.1109\/CIAPP.2017.8167199"},{"key":"50_CR15","doi-asserted-by":"crossref","unstructured":"K\u00f6k, \u0130., \u015eim\u015fek, M.U., \u00d6zdemir, S.: A deep learning model for air quality prediction in smart cities. In: Proceedings of the IEEE International Conference on Big Data, pp. 1983\u20131990 (2017)","DOI":"10.1109\/BigData.2017.8258144"},{"issue":"4","key":"50_CR16","doi-asserted-by":"publisher","first-page":"436","DOI":"10.5094\/APR.2011.050","volume":"2","author":"A Kumar","year":"2011","unstructured":"Kumar, A., Goyal, P.: Forecasting of air quality in Delhi using principal component regression technique. Atmos. Pollut. Res. 2(4), 436\u2013444 (2011)","journal-title":"Atmos. Pollut. Res."},{"issue":"5","key":"50_CR17","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.envint.2007.01.010","volume":"33","author":"G Kyrkilis","year":"2007","unstructured":"Kyrkilis, G., Chaloulakou, A., Kassomenos, P.A.: Development of an aggregate air quality index for an urban Mediterranean agglomeration: relation to potential health effects. Environ. Int. 33(5), 670\u2013676 (2007)","journal-title":"Environ. Int."},{"key":"50_CR18","doi-asserted-by":"publisher","first-page":"544","DOI":"10.4209\/aaqr.2014.08.0154","volume":"15","author":"H Petr","year":"2015","unstructured":"Petr, H., Olej, V.: Predicting common air quality index - the case of Czech Microregions. Aerosol Air Qual. Res. 15, 544\u2013555 (2015)","journal-title":"Aerosol Air Qual. Res."},{"issue":"4","key":"50_CR19","first-page":"415","volume":"3","author":"P Puri","year":"2017","unstructured":"Puri, P., Kumar, S., Kathuria, S., Ramesh, V.: Effects of air pollution on the skin: a review. Indian J. Derm.Logy, Venereol., Leprol. 3(4), 415 (2017)","journal-title":"Indian J. Derm.Logy, Venereol., Leprol."},{"issue":"3131083","key":"50_CR20","first-page":"12","volume":"2017","author":"A Saxena","year":"2017","unstructured":"Saxena, A., Shekhawat, S.: Ambient air quality classification by Grey Wolf optimizer based support vector machine. J. Environ. Public Health 2017(3131083), 12 (2017)","journal-title":"J. Environ. Public Health"},{"key":"50_CR21","doi-asserted-by":"crossref","unstructured":"Song, L.: Impact analysis of air pollutants on the air quality index in jinan winter. In: Proceedings of the IEEE CSE-EUC, pp. 471\u2013474 (2017)","DOI":"10.1109\/CSE-EUC.2017.89"},{"issue":"1","key":"50_CR22","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/JIOT.2017.2777820","volume":"5","author":"Y Yang","year":"2018","unstructured":"Yang, Y., Zheng, Z., Bian, K., Song, L., Han, Z.: Real-time profiling of fine-grained air quality index distribution using UAV sensing. IEEE Internet Things J. 5(1), 186\u2013198 (2018)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"50_CR23","doi-asserted-by":"publisher","first-page":"21","DOI":"10.4491\/eer.2017.006","volume":"23","author":"L Youping","year":"2017","unstructured":"Youping, L., Ya, T., Zhongyu, F., Hong, Z., Zhengzheng, Y.: Assessment and comparison of three different air quality indices in China. Environ. Eng. Res. 23(1), 21\u201327 (2017)","journal-title":"Environ. Eng. Res."},{"key":"50_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yuan, D.: Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: Proceedings of the IEEE, pp. 929\u2013934 (2015)","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.177"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01424-7_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:34:23Z","timestamp":1773048863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01424-7_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014230","9783030014247"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01424-7_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"27 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"360","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"139","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In addition there are 41 full poster papers and 11 short poster papers included in the proceedings","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}