{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:07:48Z","timestamp":1767985668118,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"31","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00521-024-10230-1","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:02:56Z","timestamp":1722942176000},"page":"19499-19514","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Smoke detection in foggy surveillance environment using parallel vision transformer network"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1044-8509","authenticated-orcid":false,"given":"Shubhangi","family":"Chaturvedi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Poornima Singh","family":"Thakur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pritee","family":"Khanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aparajita","family":"Ojha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"10230_CR1","unstructured":"James M, Jessica R, Sasha T, Mikaela W, Nancy H (2023) The latest data confirms: forest fires are getting worse. World Resources Institute. https:\/\/www.wri.org\/insights\/global-trends-forest-fires. Accessed on 29 Aug 2023"},{"issue":"6","key":"10230_CR2","doi-asserted-by":"publisher","first-page":"9237","DOI":"10.1109\/JIOT.2019.2896120","volume":"6","author":"S Khan","year":"2019","unstructured":"Khan S, Muhammad K, Mumtaz S, Baik SW, Albuquerque VHC (2019) Energy-efficient deep cnn for smoke detection in foggy iot environment. IEEE Internet Things J 6(6):9237\u20139245. https:\/\/doi.org\/10.1109\/JIOT.2019.2896120","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"10230_CR3","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TII.2019.2915592","volume":"16","author":"K Muhammad","year":"2019","unstructured":"Muhammad K, Khan S, Palade V, Mehmood I, De Albuquerque VHC (2019) Edge intelligence-assisted smoke detection in foggy surveillance environments. IEEE Trans Ind Inf 16(2):1067\u20131075. https:\/\/doi.org\/10.1109\/TII.2019.2915592","journal-title":"IEEE Trans Ind Inf"},{"key":"10230_CR4","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.neucom.2021.01.024","volume":"434","author":"L He","year":"2021","unstructured":"He L, Gong X, Zhang S, Wang L, Li F (2021) Efficient attention based deep fusion cnn for smoke detection in fog environment. Neurocomputing 434:224\u2013238. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.024","journal-title":"Neurocomputing"},{"key":"10230_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115125","volume":"182","author":"S Khan","year":"2021","unstructured":"Khan S, Muhammad K, Hussain T, Del Ser J, Cuzzolin F, Bhattacharyya S, Akhtar Z, Albuquerque VHC (2021) Deepsmoke: deep learning model for smoke detection and segmentation in outdoor environments. Expert Syst Appl 182:115125. https:\/\/doi.org\/10.1016\/j.eswa.2021.115125","journal-title":"Expert Syst Appl"},{"issue":"11","key":"10230_CR6","doi-asserted-by":"publisher","first-page":"7889","DOI":"10.1109\/TII.2021.3138752","volume":"18","author":"JS Almeida","year":"2022","unstructured":"Almeida JS, Huang C, Nogueira FG, Bhatia S, Albuquerque VHC (2022) Edgefiresmoke: a novel lightweight cnn model for real-time video fire-smoke detection. IEEE Trans Ind Inf 18(11):7889\u20137898. https:\/\/doi.org\/10.1109\/TII.2021.3138752","journal-title":"IEEE Trans Ind Inf"},{"key":"10230_CR7","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929"},{"key":"10230_CR8","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10230_CR9","doi-asserted-by":"crossref","unstructured":"Filonenko A, Kurnianggoro L, Jo KH (2017) Comparative study of modern convolutional neural networks for smoke detection on image data. In: 2017 10th International conference on human system interactions (HSI). IEEE, pp 64\u201368","DOI":"10.1109\/HSI.2017.8004998"},{"issue":"6","key":"10230_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"10230_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"10230_CR12","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"10230_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10230_CR14","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"10230_CR15","doi-asserted-by":"publisher","first-page":"18429","DOI":"10.1109\/ACCESS.2017.2747399","volume":"5","author":"Z Yin","year":"2017","unstructured":"Yin Z, Wan B, Yuan F, Xia X, Shi J (2017) A deep normalization and convolutional neural network for image smoke detection. Ieee Access 5:18429\u201318438. https:\/\/doi.org\/10.1109\/ACCESS.2017.2747399","journal-title":"Ieee Access"},{"key":"10230_CR16","unstructured":"Yuan F (2012) Video smoke detection. http:\/\/staff.ustc.edu.cn\/~yfn\/vsd.html. Accessed 17 May, 2022"},{"issue":"4","key":"10230_CR17","doi-asserted-by":"publisher","first-page":"121","DOI":"10.4316\/AECE.2018.04015","volume":"18","author":"A Namozov","year":"2018","unstructured":"Namozov A, Im Cho Y (2018) An efficient deep learning algorithm for fire and smoke detection with limited data. Adv Electr Comput Eng 18(4):121\u2013128. https:\/\/doi.org\/10.4316\/AECE.2018.04015","journal-title":"Adv Electr Comput Eng"},{"key":"10230_CR18","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6843869","author":"H Yin","year":"2020","unstructured":"Yin H, Wei Y, Liu H, Liu S, Liu C, Gao Y (2020) Deep convolutional generative adversarial network and convolutional neural network for smoke detection. Complexity. https:\/\/doi.org\/10.1155\/2020\/6843869","journal-title":"Complexity"},{"key":"10230_CR19","doi-asserted-by":"publisher","unstructured":"Yin H, Wei Y (2019) An improved algorithm based on convolutional neural network for smoke detection. In: 2019 IEEE international conferences on ubiquitous computing and communications (IUCC) and data science and computational intelligence (DSCI) and smart computing, networking and services (SmartCNS). IEEE, pp 207\u2013211. https:\/\/doi.org\/10.1109\/IUCC\/DSCI\/SmartCNS.2019.00063","DOI":"10.1109\/IUCC\/DSCI\/SmartCNS.2019.00063"},{"key":"10230_CR20","doi-asserted-by":"publisher","first-page":"60697","DOI":"10.1109\/ACCESS.2019.2915599","volume":"7","author":"Y Liu","year":"2019","unstructured":"Liu Y, Qin W, Liu K, Zhang F, Xiao Z (2019) A dual convolution network using dark channel prior for image smoke classification. IEEE Access 7:60697\u201360706. https:\/\/doi.org\/10.1109\/ACCESS.2019.2915599","journal-title":"IEEE Access"},{"key":"10230_CR21","doi-asserted-by":"publisher","unstructured":"Tao C, Zhang J, Wang P (2016). Smoke detection based on deep convolutional neural networks. In: 2016 International conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII). IEEE, pp 150\u2013153 https:\/\/doi.org\/10.1109\/ICIICII.2016.0045","DOI":"10.1109\/ICIICII.2016.0045"},{"key":"10230_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2020.103184","volume":"117","author":"C Li","year":"2020","unstructured":"Li C, Yang B, Ding H, Shi H, Jiang X, Sun J (2020) Real-time video-based smoke detection with high accuracy and efficiency. Fire Saf J 117:103184. https:\/\/doi.org\/10.1016\/j.firesaf.2020.103184","journal-title":"Fire Saf J"},{"key":"10230_CR23","unstructured":"Tan M, Le Q (2020) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"10230_CR24","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10230_CR25","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30"},{"key":"10230_CR26","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"10230_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116114","volume":"189","author":"S Majid","year":"2022","unstructured":"Majid S, Alenezi F, Masood S, Ahmad M, G\u00fcnd\u00fcz ES, Polat K (2022) Attention based cnn model for fire detection and localization in real-world images. Expert Syst Appl 189:116114. https:\/\/doi.org\/10.1016\/j.eswa.2021.116114","journal-title":"Expert Syst Appl"},{"key":"10230_CR28","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)"},{"key":"10230_CR29","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \"why should i trust you?\" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144. https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10230-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10230-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10230-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T12:05:38Z","timestamp":1727438738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10230-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,6]]},"references-count":29,"journal-issue":{"issue":"31","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10230"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10230-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,6]]},"assertion":[{"value":"25 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2024","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 known competing financial or personal interests that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent"}}]}}