{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:20:23Z","timestamp":1761582023115,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030623647"},{"type":"electronic","value":"9783030623654"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62365-4_13","type":"book-chapter","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T06:02:51Z","timestamp":1603951371000},"page":"137-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cloud Type Identification Using Data Fusion and Ensemble Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4127-5505","authenticated-orcid":false,"given":"Javier","family":"Huertas-Tato","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0800-7632","authenticated-orcid":false,"given":"Alejandro","family":"Mart\u00edn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5051-3475","authenticated-orcid":false,"given":"David","family":"Camacho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Boers, R., et al.: Optimized fractional cloudiness determination from five ground-based remote sensing techniques. J. Geophys. Res. Atmos. 115(24) (2010). www.scopus.com","DOI":"10.1029\/2010JD014661"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Breiman, L.: Random Forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/link.springer.com\/article\/10.1023\/A:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning, pp. 96\u2013103 (2008). www.scopus.com","DOI":"10.1145\/1390156.1390169"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, H., Lin, C.: Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques. Atmos. Meas. Tech. 10(1), 199\u2013208 (2017). www.scopus.com","DOI":"10.5194\/amt-10-199-2017"},{"key":"13_CR5","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"13_CR6","unstructured":"Haiden, T., Forbes, R., Ahlgrimm, M., Bozzo, A.: The skill of ECMWF cloudiness forecasts. ECMWF Newsl. 143, 14\u201319 (2015). www.scopus.com"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Haralick, R.M., Dinstein, I., Shanmugam, K.: Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610\u2013621 (1973). www.scopus.com","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Heinle, A., Macke, A., Srivastav, A.: Automatic cloud classification of whole sky images. Atmos. Meas. Tech. 3(3), 557\u2013567 (2010). www.scopus.com","DOI":"10.5194\/amt-3-557-2010"},{"issue":"3","key":"13_CR9","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1175\/1520-0450(1978)017<0354:ICCVIT>2.0.CO;2","volume":"17","author":"DV Hoyt","year":"1978","unstructured":"Hoyt, D.V.: Interannual cloud-cover variations in the contiguous united states. J. Appl. Meteorol. 17(3), 354\u2013357 (1978)","journal-title":"J. Appl. Meteorol."},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Huertas Tato, J., et al.: Automatic cloud-type classification based on the combined use of a sky camera and a ceilometer. J. Geogr. Res. Atmos. (2017). https:\/\/e-archivo.uc3m.es\/handle\/10016\/28557","DOI":"10.1002\/2017JD027131"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Kazantzidis, A., Tzoumanikas, P., Bais, A.F., Fotopoulos, S., Economou, G.: Cloud detection and classification with the use of whole-sky ground-based images. Atmos. Res. 113, 80\u201388 (2012). www.scopus.com","DOI":"10.1016\/j.atmosres.2012.05.005"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Kliangsuwan, T., Heednacram, A.: Feature extraction techniques for ground-based cloud type classification. Expert Syst. Appl. 42(21), 8294\u20138303 (2015). www.scopus.com","DOI":"10.1016\/j.eswa.2015.05.016"},{"issue":"7553","key":"13_CR13","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Lee, J., Weger, R.C., Sengupta, S.K., Welch, R.M.: A Neural Network Approach to Cloud Classification. IEEE Trans. Geosci. Remote Sens. 28(5), 846\u2013855 (1990). www.scopus.com","DOI":"10.1109\/36.58972"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Li, Y., Thompson, D.W.J., Stephens, G.L., Bony, S.: A global survey of the instantaneous linkages between cloud vertical structure and large-scale climate. J. Geophys. Res. 119(7), 3770\u20133792 (2014). www.scopus.com","DOI":"10.1002\/2013JD020669"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.jpdc.2017.09.006","volume":"117","author":"A Mart\u00edn","year":"2018","unstructured":"Mart\u00edn, A., Lara-Cabrera, R., Fuentes-Hurtado, F., Naranjo, V., Camacho, D.: EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation. J. Parallel Distrib. Comput. 117, 180\u2013191 (2018)","journal-title":"J. Parallel Distrib. Comput."},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"106144","DOI":"10.1016\/j.asoc.2020.106144","volume":"90","author":"A Mart\u00edn","year":"2020","unstructured":"Mart\u00edn, A., Vargas, V.M., Guti\u00e9rrez, P.A., Camacho, D., Herv\u00e1s-Mart\u00ednez, C.: Optimising convolutional neural networks using a hybrid statistically-driven coral reef optimisation algorithm. Appl. Soft Comput. 90, 106144 (2020)","journal-title":"Appl. Soft Comput."},{"key":"13_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Singh, M., Glennen, M.: Automated ground-based cloud recognition. Pattern Anal. Appl. 8(3), 258\u2013271 (2005). www.scopus.com","DOI":"10.1007\/s10044-005-0007-5"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Tzoumanikas, P., Nikitidou, E., Bais, A.F., Kazantzidis, A.: The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system. Renew. Energy 95, 314\u2013322 (2016). www.scopus.com","DOI":"10.1016\/j.renene.2016.04.026"},{"key":"13_CR23","unstructured":"World Meteorological Organization : World Meteorological Organization\/World Weather Research Programme (WMO\/WWRP). Recommended methods for evaluating cloud and related parameters World Weather Research Programme (WWRP)\/Working Group on Numerical Experimentation (WGNE) Joint Working Group on Forecast Verification Research (JWGFVR) (2012). www.scopus.com"},{"issue":"10","key":"13_CR24","doi-asserted-by":"publisher","first-page":"5729","DOI":"10.1109\/TGRS.2017.2712809","volume":"55","author":"L Ye","year":"2017","unstructured":"Ye, L., Cao, Z., Xiao, Y.: DeepCloud: ground-based cloud image categorization using deep convolutional features. IEEE Trans. Geosci. Remote Sens. 55(10), 5729\u20135740 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"16","key":"13_CR25","doi-asserted-by":"publisher","first-page":"8665","DOI":"10.1029\/2018GL077787","volume":"45","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Liu, P., Zhang, F., Song, Q.: CloudNet: ground-based cloud classification with deep convolutional neural network. Geophys. Res. Lett. 45(16), 8665\u20138672 (2018)","journal-title":"Geophys. Res. Lett."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62365-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:04:48Z","timestamp":1710266688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62365-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030623647","9783030623654"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62365-4_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"27 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guimaraes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/islab.di.uminho.pt\/ideal2020\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","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":"93","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":"0","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":"69% - 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":"2.8","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}