{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:02:15Z","timestamp":1775066535111,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031510229","type":"print"},{"value":"9783031510236","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-51023-6_31","type":"book-chapter","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:02:36Z","timestamp":1705993356000},"page":"371-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Air Quality Forecasting Through Deep Learning and\u00a0Continuous Wavelet Transform"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4873-1369","authenticated-orcid":false,"given":"Pietro","family":"Manganelli Conforti","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0764-3965","authenticated-orcid":false,"given":"Andrea","family":"Fanti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9093-1532","authenticated-orcid":false,"given":"Pietro","family":"Nardelli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1886-3491","authenticated-orcid":false,"given":"Paolo","family":"Russo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","unstructured":"Akansu, A.N., Serdijn, W.A., Selesnick, I.W.: Emerging applications of wavelets: a review. Phys. Commun. 3(1), 1\u201318 (2010). https:\/\/doi.org\/10.1016\/j.phycom.2009.07.001, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1874490709000482","DOI":"10.1016\/j.phycom.2009.07.001"},{"issue":"8","key":"31_CR2","doi-asserted-by":"publisher","first-page":"3005","DOI":"10.1007\/s00521-019-04687-8","volume":"32","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Wang, B., Gorban, A.N.: Multivariate Gaussian and student-t process regression for multi-output prediction. Neural Comput.Appl. 32(8), 3005\u20133028 (2020). https:\/\/doi.org\/10.1007\/s00521-019-04687-8","journal-title":"Neural Comput.Appl."},{"key":"31_CR3","doi-asserted-by":"publisher","unstructured":"De Vito, S., Massera, E., Piga, M., Martinotto, L., Di Francia, G.: On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens. Actuators B 129(2), 750\u2013757 (2008). https:\/\/doi.org\/10.1016\/j.snb.2007.09.060, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925400507007691","DOI":"10.1016\/j.snb.2007.09.060"},{"issue":"2","key":"31_CR4","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/MCAS.2009.932556","volume":"9","author":"C Gargour","year":"2009","unstructured":"Gargour, C., Gabrea, M., Ramachandran, V., Lina, J.M.: A short introduction to wavelets and their applications. IEEE Circuits Syst. Mag. 9(2), 57\u201368 (2009). https:\/\/doi.org\/10.1109\/MCAS.2009.932556","journal-title":"IEEE Circuits Syst. Mag."},{"key":"31_CR5","doi-asserted-by":"publisher","unstructured":"Huang, L., et al.: Exploring deep learning for air pollutant emission estimation. Geoscientific Model Dev. 14(7), 4641\u20134654 (2021). https:\/\/doi.org\/10.5194\/gmd-14-4641-2021, https:\/\/gmd.copernicus.org\/articles\/14\/4641\/2021\/, publisher: Copernicus GmbH","DOI":"10.5194\/gmd-14-4641-2021"},{"issue":"12","key":"31_CR6","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1109\/TASLP.2014.2354236","volume":"22","author":"M Krawczyk","year":"2014","unstructured":"Krawczyk, M., Gerkmann, T.: STFT phase reconstruction in voiced speech for an improved single-channel speech enhancement. IEEE\/ACM Trans. Audio Speech Lang. Process. 22(12), 1931\u20131940 (2014). https:\/\/doi.org\/10.1109\/TASLP.2014.2354236","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"31_CR7","doi-asserted-by":"publisher","unstructured":"Liu, H., Li, Q., Yu, D., Gu, Y.: Air quality index and air pollutant concentration prediction based on machine learning algorithms. Appl. Sci. 9(19), 4069 (2019). https:\/\/doi.org\/10.3390\/app9194069, https:\/\/www.mdpi.com\/2076-3417\/9\/19\/4069","DOI":"10.3390\/app9194069"},{"key":"31_CR8","doi-asserted-by":"publisher","unstructured":"Ly, H.B., et al.: Development of an AI model to measure traffic air pollution from Multisensor and Weather data. Sensors 19(22), 4941 (2019). https:\/\/doi.org\/10.3390\/s19224941, https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4941","DOI":"10.3390\/s19224941"},{"key":"31_CR9","doi-asserted-by":"publisher","unstructured":"Ma, X., Karkus, P., Hsu, D., Lee, W.S.: Particle filter recurrent neural networks. Proc. AAAI Conf. Artif. Intell. 34(04), 5101\u20135108 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.5952, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5952","DOI":"10.1609\/aaai.v34i04.5952"},{"key":"31_CR10","doi-asserted-by":"publisher","unstructured":"Ma, Z., Mei, G.: Deep learning for geological hazards analysis: data, models, applications, and opportunities. Earth Sci. Rev. 223, 103858 (2021). https:\/\/doi.org\/10.1016\/j.earscirev.2021.103858, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0012825221003597","DOI":"10.1016\/j.earscirev.2021.103858"},{"key":"31_CR11","doi-asserted-by":"publisher","unstructured":"Manganelli Conforti, P., D\u2019Acunto, M., Russo, P.: Deep learning for chondrogenic tumor classification through wavelet transform of Raman spectra. Sensors 22(19), 7492 (2022). https:\/\/doi.org\/10.3390\/s22197492, https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7492","DOI":"10.3390\/s22197492"},{"issue":"9","key":"31_CR12","doi-asserted-by":"publisher","first-page":"10834","DOI":"10.1109\/JSEN.2021.3059849","volume":"21","author":"L Pan","year":"2020","unstructured":"Pan, L., Pipitsunthonsan, P., Daengngam, C., Chongcheawchamnan, M.: Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network. IEEE Sens. J. 21(9), 10834\u201310843 (2020)","journal-title":"IEEE Sens. J."},{"key":"31_CR13","doi-asserted-by":"publisher","unstructured":"Qi, Y., et al.: Accurate diagnosis of lung tissues for 2D Raman spectrogram by deep learning based on short-time Fourier transform. Anal. Chim. Acta 1179, 338821 (2021). https:\/\/doi.org\/10.1016\/j.aca.2021.338821, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0003267021006474","DOI":"10.1016\/j.aca.2021.338821"},{"issue":"1","key":"31_CR14","doi-asserted-by":"publisher","first-page":"3916","DOI":"10.1038\/s41598-023-30683-z","volume":"13","author":"P Russo","year":"2023","unstructured":"Russo, P., Schaerf, M.: Anomaly detection in railway bridges using imaging techniques. Sci. Rep. 13(1), 3916 (2023)","journal-title":"Sci. Rep."},{"key":"31_CR15","doi-asserted-by":"publisher","unstructured":"Sabzekar, M., Hasheminejad, S.M.H.: Robust regression using support vector regressions. Chaos, Solitons Fractals 144, 110738 (2021). https:\/\/doi.org\/10.1016\/j.chaos.2021.110738, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960077921000916","DOI":"10.1016\/j.chaos.2021.110738"},{"key":"31_CR16","doi-asserted-by":"publisher","unstructured":"Saleem, S., Dilawari, A., Khan, U.G.: Spoofed voice detection using dense features of STFT and MDCT spectrograms. In: 2021 International Conference on Artificial Intelligence (ICAI), pp. 56\u201361 (2021). https:\/\/doi.org\/10.1109\/ICAI52203.2021.9445259","DOI":"10.1109\/ICAI52203.2021.9445259"},{"key":"31_CR17","doi-asserted-by":"publisher","unstructured":"Salman, A.G., Kanigoro, B., Heryadi, Y.: Weather forecasting using deep learning techniques. In: 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 281\u2013285 (2015). https:\/\/doi.org\/10.1109\/ICACSIS.2015.7415154","DOI":"10.1109\/ICACSIS.2015.7415154"},{"key":"31_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/978-3-030-01424-7_27","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"C Tan","year":"2018","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270\u2013279. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01424-7_27"},{"issue":"2126","key":"31_CR19","doi-asserted-by":"publisher","first-page":"20170254","DOI":"10.1098\/rsta.2017.0254","volume":"376","author":"JB Tary","year":"2018","unstructured":"Tary, J.B., Herrera, R.H., Van Der Baan, M.: Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376(2126), 20170254 (2018)","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-51023-6_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:07:34Z","timestamp":1705993654000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-51023-6_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031510229","9783031510236"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-51023-6_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"24 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Udine","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","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":"82","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":"13","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":"57% - 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":"3","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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"https:\/\/iciap2023.org\/satellite-event\/workshops\/","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)"}}]}}