{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:00:16Z","timestamp":1789653616831,"version":"4.0.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61906036"],"award-info":[{"award-number":["61906036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["42075139"],"award-info":[{"award-number":["42075139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011246","name":"state key laboratory of novel software technology","doi-asserted-by":"publisher","award":["KFKT2019B02"],"award-info":[{"award-number":["KFKT2019B02"]}],"id":[{"id":"10.13039\/501100011246","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2242021k30056"],"award-info":[{"award-number":["2242021k30056"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The visibility estimation of the environment has great research and application value in the fields of production. To estimate the visibility, we can utilize the camera to obtain some images as evidence. However, the camera only solves the image acquisition problem, and the analysis of image visibility requires strong computational power. To realize effective and efficient visibility estimation, we employ the cloud computing technique to realize high-through image analysis. Our method combines cloud computing and image-based visibility estimation into a powerful and efficient monitoring framework. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. The estimation result can be improved by fusing the predicting results of multiple images from different views. Our experiment shows that labeling the image with visibility distribution can boost the learning performance, and our method can obtain the visibility from the image efficiently.<\/jats:p>","DOI":"10.1186\/s13677-021-00261-7","type":"journal-article","created":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T12:02:54Z","timestamp":1630152174000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Visibility estimation via deep label distribution learning in cloud environment"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9912-1560","authenticated-orcid":false,"given":"Mofei","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao Fan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,28]]},"reference":[{"key":"261_CR1","doi-asserted-by":"publisher","unstructured":"Xu X, Fang Z, Qi L, Zhang X, He Q, Zhou X (2021) Tripres: Traffic flow prediction driven resource reservation for multimedia iov with edge computing. ACM Trans Multimed Comput Commun Appl 17(2). https:\/\/doi.org\/10.1145\/3401979.","DOI":"10.1145\/3401979"},{"key":"261_CR2","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1103\/RevModPhys.71.313","volume":"71","author":"MCW van Rossum","year":"1999","unstructured":"van Rossum MCW, Nieuwenhuizen TM (1999) Multiple scattering of classical waves: microscopy, mesoscopy, and diffusion. Rev Mod Phys 71:313\u2013371. https:\/\/doi.org\/10.1103\/RevModPhys.71.313.","journal-title":"Rev Mod Phys"},{"issue":"1","key":"261_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.26599\/BDMA.2020.9020017","volume":"4","author":"J Mabrouki","year":"2021","unstructured":"Mabrouki J, Azrour M, Fattah G, Dhiba D, Hajjaji SE (2021) Intelligent monitoring system for biogas detection based on the internet of things: Mohammedia, morocco city landfill case. Big Data Min Analytics 4(1):10\u201317. https:\/\/doi.org\/10.26599\/BDMA.2020.9020017.","journal-title":"Big Data Min Analytics"},{"issue":"30","key":"261_CR4","doi-asserted-by":"publisher","first-page":"5316","DOI":"10.1016\/j.atmosenv.2011.06.053","volume":"45","author":"R Babari","year":"2011","unstructured":"Babari R, Hauti\u00e8re N, Dumont \u00c9, Br\u00e9mond R, Paparoditis N (2011) A model-driven approach to estimate atmospheric visibility with ordinary cameras. Atmos Environ 45(30):5316\u20135324. https:\/\/doi.org\/10.1016\/j.atmosenv.2011.06.053.","journal-title":"Atmos Environ"},{"key":"261_CR5","doi-asserted-by":"publisher","unstructured":"Babari R, Hauti\u00e8re N, Dumont E, Papelard J-P, Paparoditis N (2011) Computer vision for the remote sensing of atmospheric visibility In: Proc. IEEE Int. Conf. Comput. Vis. Workshops, 219\u2013226. https:\/\/doi.org\/10.1109\/ICCVW.2011.6130246.","DOI":"10.1109\/ICCVW.2011.6130246"},{"key":"261_CR6","doi-asserted-by":"publisher","first-page":"24430","DOI":"10.1109\/ACCESS.2019.2894658","volume":"7","author":"Q Li","year":"2019","unstructured":"Li Q, Li Y, Xie B (2019) Single image-based scene visibility estimation. IEEE Access 7:24430\u201324439. https:\/\/doi.org\/10.1109\/ACCESS.2019.2894658.","journal-title":"IEEE Access"},{"key":"261_CR7","doi-asserted-by":"publisher","first-page":"455","DOI":"10.7763\/IJCTE.2017.V9.1186","volume":"9","author":"S Li","year":"2017","unstructured":"Li S, Fu H, Lo W (2017) Meteorological visibility evaluation on webcam weather image using deep learning features. Int J Comput Theory Eng 9:455\u2013461.","journal-title":"Int J Comput Theory Eng"},{"issue":"6","key":"261_CR8","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.3390\/s19061343","volume":"19","author":"A Palvanov","year":"2019","unstructured":"Palvanov A, Cho Y (2019) Visnet: Deep convolutional neural networks for forecasting atmospheric visibility. Sensors 19(6):1343.","journal-title":"Sensors"},{"key":"261_CR9","doi-asserted-by":"publisher","first-page":"6434","DOI":"10.1109\/TIP.2021.3093795","volume":"30","author":"J Qiu","year":"2021","unstructured":"Qiu J, Chen C, Liu S, Zhang H-Y, Zeng B (2021) Slimconv: Reducing channel redundancy in convolutional neural networks by features recombining. IEEE Trans Image Process 30:6434\u20136445. https:\/\/doi.org\/10.1109\/TIP.2021.3093795.","journal-title":"IEEE Trans Image Process"},{"key":"261_CR10","doi-asserted-by":"publisher","unstructured":"Liu J, Zhuang B, Zhuang Z, Guo Y, Huang J, Zhu J, Tan M (2021) Discrimination-aware network pruning for deep model compression. IEEE Trans Pattern Anal Mach Intell:1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2021.3066410.","DOI":"10.1109\/TPAMI.2021.3066410"},{"issue":"2","key":"261_CR11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.26599\/BDMA.2019.9020015","volume":"3","author":"MS Mahmud","year":"2020","unstructured":"Mahmud MS, Huang JZ, Salloum S, Emara TZ, Sadatdiynov K (2020) A survey of data partitioning and sampling methods to support big data analysis. Big Data Min Analytics 3(2):85\u2013101. https:\/\/doi.org\/10.26599\/BDMA.2019.9020015.","journal-title":"Big Data Min Analytics"},{"issue":"5","key":"261_CR12","doi-asserted-by":"publisher","first-page":"724","DOI":"10.26599\/TST.2020.9010035","volume":"26","author":"N Chen","year":"2021","unstructured":"Chen N, Wang Z, He R, Jiang J, Cheng F, Han C (2021) Efficient scheduling mapping algorithm for row parallel coarse-grained reconfigurable architecture. Tsinghua Sci Technol 26(5):724\u2013735. https:\/\/doi.org\/10.26599\/TST.2020.9010035.","journal-title":"Tsinghua Sci Technol"},{"issue":"1","key":"261_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.26599\/BDMA.2020.9020010","volume":"4","author":"M Azrour","year":"2021","unstructured":"Azrour M, Mabrouki J, Guezzaz A, Farhaoui Y (2021) New enhanced authentication protocol for internet of things. Big Data Min Analytics 4(1):1\u20139. https:\/\/doi.org\/10.26599\/BDMA.2020.9020010.","journal-title":"Big Data Min Analytics"},{"issue":"3","key":"261_CR14","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/TITS.2020.2995622","volume":"22","author":"X Xu","year":"2021","unstructured":"Xu X, Wu Q, Qi L, Dou W, Tsai S-B, Bhuiyan MZA (2021) Trust-aware service offloading for video surveillance in edge computing enabled internet of vehicles. IEEE Trans Intell Transp Syst 22(3):1787\u20131796. https:\/\/doi.org\/10.1109\/TITS.2020.2995622.","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1740","key":"261_CR15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1098\/rsta.1998.0211","volume":"356","author":"J Koenderink","year":"1998","unstructured":"Koenderink J, J (1998) Pictorial relief. Phil Trans R Soc A 356(1740):6\u20136.","journal-title":"Phil Trans R Soc A"},{"issue":"1","key":"261_CR16","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TIP.2018.2857219","volume":"28","author":"Y You","year":"2019","unstructured":"You Y, Lu C, Wang W, Tang C-K (2019) Relative cnn-rnn: Learning relative atmospheric visibility from images. IEEE Trans Image Process 28(1):45\u201355. https:\/\/doi.org\/10.1109\/TIP.2018.2857219.","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"261_CR17","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TKDE.2016.2545658","volume":"28","author":"X Geng","year":"2016","unstructured":"Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734\u20131748. https:\/\/doi.org\/10.1109\/TKDE.2016.2545658.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"261_CR18","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/5254.736004","volume":"13","author":"C Busch","year":"1998","unstructured":"Busch C, Debes E (1998) Wavelet transform for analyzing fog visibility. IEEE Intell Syst Appl 13(6):66\u201371. https:\/\/doi.org\/10.1109\/5254.736004.","journal-title":"IEEE Intell Syst Appl"},{"issue":"1","key":"261_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1111\/j.1365-2818.1988.tb04559.x","volume":"149","author":"M Jourlin","year":"1988","unstructured":"Jourlin M, Pinoli J-C (1988) A model for logarithmic image processing. J Microsc 149(1):21\u201335. https:\/\/doi.org\/10.1111\/j.1365-2818.1988.tb04559.x.","journal-title":"J Microsc"},{"key":"261_CR20","doi-asserted-by":"publisher","unstructured":"Bronte S, Bergasa LM, Alcantarilla PF (2009) Fog detection system based on computer vision techniques In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, 1\u20136. https:\/\/doi.org\/10.1109\/ITSC.2009.5309842.","DOI":"10.1109\/ITSC.2009.5309842"},{"key":"261_CR21","doi-asserted-by":"publisher","unstructured":"Graves N, Newsam S (2011) Using visibility cameras to estimate atmospheric light extinction In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), 577\u2013584. https:\/\/doi.org\/10.1109\/WACV.2011.5711556.","DOI":"10.1109\/WACV.2011.5711556"},{"key":"261_CR22","doi-asserted-by":"publisher","unstructured":"Xiang W, Xiao J, Wang C, Liu Y (2013) A new model for daytime visibility index estimation fused average sobel gradient and dark channel ratio In: Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, 109\u2013112. https:\/\/doi.org\/10.1109\/ICCSNT.2013.6967074.","DOI":"10.1109\/ICCSNT.2013.6967074"},{"key":"261_CR23","doi-asserted-by":"publisher","unstructured":"Giyenko A, Palvanov A, Cho Y (2018) Application of convolutional neural networks for visibility estimation of cctv images In: 2018 International Conference on Information Networking (ICOIN), 875\u2013879. https:\/\/doi.org\/10.1109\/ICOIN.2018.8343247.","DOI":"10.1109\/ICOIN.2018.8343247"},{"key":"261_CR24","doi-asserted-by":"publisher","first-page":"217057","DOI":"10.1109\/ACCESS.2020.3031283","volume":"8","author":"H Wang","year":"2020","unstructured":"Wang H, Shen K, Yu P, Shi Q, Ko H (2020) Multimodal deep fusion network for visibility assessment with a small training dataset. IEEE Access 8:217057\u2013217067. https:\/\/doi.org\/10.1109\/ACCESS.2020.3031283.","journal-title":"IEEE Access"},{"issue":"3","key":"261_CR25","doi-asserted-by":"publisher","first-page":"997","DOI":"10.3390\/app11030997","volume":"11","author":"J Li","year":"2021","unstructured":"Li J, Lo WL, Fu H, Chung HSH (2021) A transfer learning method for meteorological visibility estimation based on feature fusion method. Appl Sci 11(3):997. https:\/\/doi.org\/10.3390\/app11030997.","journal-title":"Appl Sci"},{"issue":"7","key":"261_CR26","doi-asserted-by":"publisher","first-page":"828","DOI":"10.3390\/atmos12070828","volume":"12","author":"WL Lo","year":"2021","unstructured":"Lo WL, Chung HSH, Fu H (2021) Experimental evaluation of pso based transfer learning method for meteorological visibility estimation. Atmosphere 12(7):828. https:\/\/doi.org\/10.3390\/atmos12070828.","journal-title":"Atmosphere"},{"issue":"6","key":"261_CR27","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1109\/TCSS.2019.2909137","volume":"6","author":"X Xu","year":"2019","unstructured":"Xu X, Liu Q, Zhang X, Zhang J, Qi L, Dou W (2019) A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans Comput Soc Syst 6(6):1407\u20131419. https:\/\/doi.org\/10.1109\/TCSS.2019.2909137.","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"6","key":"261_CR28","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","volume":"57","author":"Y Hua","year":"2019","unstructured":"Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H (2019) Deep learning with long short-term memory for time series prediction. IEEE Commun Mag 57(6):114\u2013119. https:\/\/doi.org\/10.1109\/MCOM.2019.1800155.","journal-title":"IEEE Commun Mag"},{"issue":"2","key":"261_CR29","doi-asserted-by":"publisher","first-page":"227","DOI":"10.26599\/TST.2019.9010007","volume":"25","author":"Y Jin","year":"2020","unstructured":"Jin Y, Guo W, Zhang Y (2020) A time-aware dynamic service quality prediction approach for services. Tsinghua Sci Technol 25(2):227\u2013238. https:\/\/doi.org\/10.26599\/TST.2019.9010007.","journal-title":"Tsinghua Sci Technol"},{"key":"261_CR30","doi-asserted-by":"publisher","unstructured":"Zixuan W, Qingchi Y, Zhihong Y, Yang W, Zhiming F (2021) Visibility prediction of plateau airport based on lstm In: 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 1886\u20131891. https:\/\/doi.org\/10.1109\/IAEAC50856.2021.9391060.","DOI":"10.1109\/IAEAC50856.2021.9391060"},{"issue":"2","key":"261_CR31","doi-asserted-by":"publisher","first-page":"239","DOI":"10.26599\/TST.2019.9010062","volume":"26","author":"R Bi","year":"2021","unstructured":"Bi R, Liu Q, Ren J, Tan G (2021) Utility aware offloading for mobile-edge computing. Tsinghua Sci Technol 26(2):239\u2013250. https:\/\/doi.org\/10.26599\/TST.2019.9010062.","journal-title":"Tsinghua Sci Technol"},{"issue":"6","key":"261_CR32","doi-asserted-by":"publisher","first-page":"4187","DOI":"10.1109\/TII.2019.2936869","volume":"16","author":"X Xu","year":"2020","unstructured":"Xu X, Zhang X, Gao H, Xue Y, Qi L, Dou W (2020) Become: Blockchain-enabled computation offloading for iot in mobile edge computing. IEEE Trans Ind Informal 16(6):4187\u20134195. https:\/\/doi.org\/10.1109\/TII.2019.2936869.","journal-title":"IEEE Trans Ind Informal"},{"key":"261_CR33","doi-asserted-by":"publisher","unstructured":"Wang F, Zhu H, Srivastava G, Li S, Khosravi MR, Qi L (2021) Robust collaborative filtering recommendation with user-item-trust records. IEEE Trans Comput Soc Syst:1\u201311. https:\/\/doi.org\/10.1109\/TCSS.2021.3064213.","DOI":"10.1109\/TCSS.2021.3064213"},{"issue":"6","key":"261_CR34","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1109\/TII.2020.3012157","volume":"17","author":"L Qi","year":"2021","unstructured":"Qi L, Hu C, Zhang X, Khosravi MR, Sharma S, Pang S, Wang T (2021) Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Trans Ind Informal 17(6):4159\u20134167. https:\/\/doi.org\/10.1109\/TII.2020.3012157.","journal-title":"IEEE Trans Ind Informal"},{"key":"261_CR35","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.comcom.2019.12.014","volume":"150","author":"K Wang","year":"2020","unstructured":"Wang K (2020) Migration strategy of cloud collaborative computing for delay-sensitive industrial iot applications in the context of intelligent manufacturing. Comput Commu 150:413\u2013420. https:\/\/doi.org\/10.1016\/j.comcom.2019.12.014.","journal-title":"Comput Commu"},{"issue":"2","key":"261_CR36","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1049\/iet-com.2019.0537","volume":"14","author":"M Nazari Jahantigh","year":"2020","unstructured":"Nazari Jahantigh M, Masoud Rahmani A, Jafari Navimirour N, Rezaee A (2020) Integration of internet of things and cloud computing: a systematic survey. IET Commun 14(2):165\u2013176. https:\/\/doi.org\/10.1049\/iet-com.2019.0537. https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/iet-com.2019.0537.","journal-title":"IET Commun"},{"issue":"3","key":"261_CR37","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1145\/1267070.1267073","volume":"39","author":"EF Nakamura","year":"2007","unstructured":"Nakamura EF, Loureiro AAF, Frery AC (2007) Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Comput Surv 39(3):9. https:\/\/doi.org\/10.1145\/1267070.1267073.","journal-title":"ACM Comput Surv"},{"key":"261_CR38","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.inffus.2017.03.006","volume":"38","author":"S Sun","year":"2017","unstructured":"Sun S, Lin H, Ma J, Li X (2017) Multi-sensor distributed fusion estimation with applications in networked systems: A review paper. Inf Fusion 38:122\u2013134. https:\/\/doi.org\/10.1016\/j.inffus.2017.03.006.","journal-title":"Inf Fusion"},{"key":"261_CR39","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.inffus.2018.04.003","volume":"46","author":"F Xiao","year":"2019","unstructured":"Xiao F (2019) Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Inf Fusion 46:23\u201332. https:\/\/doi.org\/10.1016\/j.inffus.2018.04.003.","journal-title":"Inf Fusion"},{"issue":"3","key":"261_CR40","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1109\/TFUZZ.2018.2859899","volume":"27","author":"RR Yager","year":"2019","unstructured":"Yager RR (2019) Generalized dempster\u2013shafer structures. IEEE Trans Fuzzy Syst 27(3):428\u2013435. https:\/\/doi.org\/10.1109\/TFUZZ.2018.2859899.","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"261_CR41","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.eswa.2018.04.026","volume":"107","author":"S Majumder","year":"2018","unstructured":"Majumder S, Pratihar DK (2018) Multi-sensors data fusion through fuzzy clustering and predictive tools. Expert Syst Appl 107:165\u2013172. https:\/\/doi.org\/10.1016\/j.eswa.2018.04.026.","journal-title":"Expert Syst Appl"},{"key":"261_CR42","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.inffus.2018.08.007","volume":"48","author":"W Wei","year":"2019","unstructured":"Wei W, Liang J (2019) Information fusion in rough set theory : An overview. Inf Fusion 48:107\u2013118. https:\/\/doi.org\/10.1016\/j.inffus.2018.08.007.","journal-title":"Inf Fusion"},{"key":"261_CR43","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.knosys.2017.06.014","volume":"132","author":"L Zhang","year":"2017","unstructured":"Zhang L, Ding L, Wu X, Skibniewski MJ (2017) An improved dempster\u2013shafer approach to construction safety risk perception. Knowl Based Syst 132:30\u201346. https:\/\/doi.org\/10.1016\/j.knosys.2017.06.014.","journal-title":"Knowl Based Syst"},{"key":"261_CR44","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ins.2019.01.053","volume":"484","author":"S Razi","year":"2019","unstructured":"Razi S, Karami Mollaei MR, Ghasemi J (2019) A novel method for classification of bci multi-class motor imagery task based on dempster\u2013shafer theory. Inf Sci 484:14\u201326. https:\/\/doi.org\/10.1016\/j.ins.2019.01.053.","journal-title":"Inf Sci"},{"key":"261_CR45","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.culher.2018.01.011","volume":"32","author":"F Liu","year":"2018","unstructured":"Liu F, Zhao Q, Yang Y (2018) An approach to assess the value of industrial heritage based on dempster\u2013shafer theory. J Cult Herit 32:210\u2013220. https:\/\/doi.org\/10.1016\/j.culher.2018.01.011.","journal-title":"J Cult Herit"},{"key":"261_CR46","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.neucom.2020.03.011","volume":"399","author":"Y Chen","year":"2020","unstructured":"Chen Y, Kang Y, Chen Y, Wang Z (2020) Probabilistic forecasting with temporal convolutional neural network. Neurocomputing 399:491\u2013501. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.011.","journal-title":"Neurocomputing"},{"issue":"10","key":"261_CR47","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2017) Lstm: A search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222\u20132232. https:\/\/doi.org\/10.1109\/TNNLS.2016.2582924.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"261_CR48","doi-asserted-by":"publisher","unstructured":"Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (gru) neural networks In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 1597\u20131600. https:\/\/doi.org\/10.1109\/MWSCAS.2017.8053243.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"261_CR49","first-page":"1097","volume-title":"Proc. Adv. Neural Inf. Process. Syst., vol. 25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks In: Proc. Adv. Neural Inf. Process. Syst., vol. 25, 1097\u20131105.. MIT press, Cambridge."},{"issue":"10","key":"261_CR50","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TPAMI.2013.51","volume":"35","author":"X Geng","year":"2013","unstructured":"Geng X, Yin C, Zhou Z-H (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35(10):2401\u20132412. https:\/\/doi.org\/10.1109\/TPAMI.2013.51.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"261_CR51","first-page":"315","volume-title":"Proc. Adv. Neural Inf. Process. Syst., vol. 26","author":"R Johnson","year":"2013","unstructured":"Johnson R, Zhang T (2013) Accelerating stochastic gradient descent using predictive variance reduction In: Proc. Adv. Neural Inf. Process. Syst., vol. 26, 315\u2013323.. MIT press, Cambridge."},{"key":"261_CR52","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.ins.2018.03.001","volume":"448-449","author":"A Sarabi-Jamab","year":"2018","unstructured":"Sarabi-Jamab A, Araabi BN (2018) How to decide when the sources of evidence are unreliable: A multi-criteria discounting approach in the dempster\u2013shafer theory. Inf Sci 448-449:233\u2013248. https:\/\/doi.org\/10.1016\/j.ins.2018.03.001.","journal-title":"Inf Sci"},{"key":"261_CR53","doi-asserted-by":"publisher","unstructured":"Tarel J-P, Hauti\u00e8re N, Cord A, Gruyer D, Halmaoui H (2010) Improved visibility of road scene images under heterogeneous fog In: Proc. IEEE Intell. Veh. Symp, 478\u2013485. https:\/\/doi.org\/10.1109\/IVS.2010.5548128.","DOI":"10.1109\/IVS.2010.5548128"},{"key":"261_CR54","doi-asserted-by":"publisher","unstructured":"Belaroussi R, Gruyer D (2014) Impact of reduced visibility from fog on traffic sign detection In: Proc. IEEE Intell. Veh. Symp, 1302\u20131306. https:\/\/doi.org\/10.1109\/IVS.2014.6856535.","DOI":"10.1109\/IVS.2014.6856535"},{"key":"261_CR55","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Yoshua B Yann L (eds)Proc. Int. Conf. Learn. Represent, San Diego."},{"key":"261_CR56","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"261_CR57","doi-asserted-by":"publisher","unstructured":"Palvanov A, Im Cho Y (2018) Dhcnn for visibility estimation in foggy weather conditions In: Proc. Joint 10th Int. Conf. Soft Comput. Intell. Syst. (SCIS) 19th Int. Symp. Adv. Intell. Syst. (ISIS), 240\u2013243. https:\/\/doi.org\/10.1109\/SCIS-ISIS.2018.00050.","DOI":"10.1109\/SCIS-ISIS.2018.00050"},{"key":"261_CR58","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.090","volume":"448","author":"X Yan","year":"2021","unstructured":"Yan X, Hu S, Mao Y, Ye Y, Yu H (2021) Deep multi-view learning methods: A review. Neurocomputing 448:106\u2013129. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.090.","journal-title":"Neurocomputing"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-021-00261-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-021-00261-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-021-00261-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T13:11:08Z","timestamp":1630156268000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-021-00261-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,28]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["261"],"URL":"https:\/\/doi.org\/10.1186\/s13677-021-00261-7","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,28]]},"assertion":[{"value":"28 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2021","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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"46"}}