{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:32:24Z","timestamp":1778693544072,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e1 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2021\/10921-2"],"award-info":[{"award-number":["2021\/10921-2"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e1 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2013\/07375-0"],"award-info":[{"award-number":["2013\/07375-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e1 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2022\/09644-7"],"award-info":[{"award-number":["2022\/09644-7"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10489-024-05466-2","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T05:01:48Z","timestamp":1713762108000},"page":"5536-5557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A deep learning workflow enhanced with optical flow fields for flood risk estimation"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5680-9085","authenticated-orcid":false,"given":"Caetano Mazzoni","family":"Ranieri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tha\u00eds Luiza Donega e","family":"Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marislei","family":"Nishijima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhaskar","family":"Krishnamachari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f3","family":"Ueyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"5466_CR1","doi-asserted-by":"publisher","unstructured":"Antzoulatos G, Kouloglou IO, Bakratsas M, Moumtzidou A, Gialampoukidis I, Karakostas A, Lombardo F, Fiorin R, Norbiato D, Ferri M et al (2022) Flood hazard and risk mapping by applying an explainable machine learning framework using satellite imagery and gis data. Sustainability 14(6):3251. https:\/\/doi.org\/10.3390\/su14063251","DOI":"10.3390\/su14063251"},{"key":"5466_CR2","doi-asserted-by":"publisher","DOI":"10.1201\/9781003160823","volume-title":"Handbook of Flood Risk Management in Developing Countries","author":"VO Oladokun","year":"2023","unstructured":"Oladokun VO, Proverbs D, Adebimpe OA, Adedeji T (2023) Handbook of Flood Risk Management in Developing Countries. Routledge, Milton Park, Abingdon-on-Thames, Oxfordshire, England, UK"},{"key":"5466_CR3","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/J.SUSCOM.2017.12.001","volume":"20","author":"SK Sood","year":"2018","unstructured":"Sood SK, Sandhu R, Singla K, Chang V (2018) IoT, big data and HPC based smart flood management framework. Sustainable Computing: Informatics and Systems 20:102\u2013117. https:\/\/doi.org\/10.1016\/J.SUSCOM.2017.12.001","journal-title":"Sustainable Computing: Informatics and Systems"},{"issue":"13","key":"5466_CR4","doi-asserted-by":"publisher","first-page":"10543","DOI":"10.3390\/su151310543","volume":"15","author":"V Kumar","year":"2023","unstructured":"Kumar V, Azamathulla HM, Sharma KV, Mehta DJ, Maharaj KT (2023) The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management. Sustainability 15(13):10543","journal-title":"Sustainability"},{"issue":"1","key":"5466_CR5","doi-asserted-by":"publisher","first-page":"12582","DOI":"10.1111\/JFR3.12582","volume":"13","author":"D Faulkner","year":"2020","unstructured":"Faulkner D, Warren S, Spencer P, Sharkey P (2020) Can we still predict the future from the past? Implementing non-stationary flood frequency analysis in the UK. Journal of Flood Risk Management 13(1):12582. https:\/\/doi.org\/10.1111\/JFR3.12582","journal-title":"Journal of Flood Risk Management"},{"key":"5466_CR6","doi-asserted-by":"publisher","unstructured":"Refice A, Capolongo D, Chini M, D\u2019Addabbo A (2022) Improving flood detection and monitoring through remote sensing. Water 14(3):364. https:\/\/doi.org\/10.3390\/w14030364","DOI":"10.3390\/w14030364"},{"key":"5466_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107235","volume":"127","author":"CM Ranieri","year":"2024","unstructured":"Ranieri CM, Foletto AV, Garcia RD, Matos SN, Medina MM, Marcolino LS, Ueyama J (2024) Water level identification with laser sensors, inertial units, and machine learning. Eng Appl Artif Intell 127:107235","journal-title":"Eng Appl Artif Intell"},{"key":"5466_CR8","doi-asserted-by":"publisher","unstructured":"Raj JR, Charless I, Latheef MA, Srinivasulu S (2021) Identifying the Flooded Area Using Deep Learning Model. In: Proceedings of 2021 2nd International Conference on Intelligent Engineering and Management, ICIEM 2021, pp. 582\u2013586. Institute of Electrical and Electronics Engineers Inc., London, United Kingdom. https:\/\/doi.org\/10.1109\/ICIEM51511.2021.9445356","DOI":"10.1109\/ICIEM51511.2021.9445356"},{"issue":"8","key":"5466_CR9","doi-asserted-by":"publisher","first-page":"4435","DOI":"10.5194\/hess-25-4435-2021","volume":"25","author":"R Vandaele","year":"2021","unstructured":"Vandaele R, Dance SL, Ojha V (2021) Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning. Hydrol Earth Syst Sci 25(8):4435\u20134453. https:\/\/doi.org\/10.5194\/hess-25-4435-2021","journal-title":"Hydrol Earth Syst Sci"},{"key":"5466_CR10","doi-asserted-by":"publisher","unstructured":"Gan JL, Zailah W (2021) Water level classification for flood monitoring system using convolutional neural network. In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019, pp. 299\u2013318. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-15-5281-6_21","DOI":"10.1007\/978-981-15-5281-6_21"},{"key":"5466_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/J.JHYDROL.2019.124482","volume":"582","author":"Y Wang","year":"2020","unstructured":"Wang Y, Fang Z, Hong H, Peng L (2020) Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol 582:124482. https:\/\/doi.org\/10.1016\/J.JHYDROL.2019.124482","journal-title":"J Hydrol"},{"issue":"3","key":"5466_CR12","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1145\/212094.212141","volume":"27","author":"SS Beauchemin","year":"1995","unstructured":"Beauchemin SS, Barron JL (1995) The computation of optical flow. ACM computing surveys (CSUR) 27(3):433\u2013466. https:\/\/doi.org\/10.1145\/212094.212141","journal-title":"ACM computing surveys (CSUR)"},{"key":"5466_CR13","doi-asserted-by":"publisher","unstructured":"Zach C, Pock T, Bischof H (2007) A duality based approach for realtime tv-l1 optical flow. In: Hamprecht FA, Schn\u00f6rr C, J\u00e4hne B (eds.) Pattern Recognition, pp. 214\u2013223. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-74936-3_22","DOI":"10.1007\/978-3-540-74936-3_22"},{"issue":"41\u201342","key":"5466_CR14","doi-asserted-by":"publisher","first-page":"30509","DOI":"10.1007\/s11042-020-09004-3","volume":"79","author":"DR Beddiar","year":"2020","unstructured":"Beddiar DR, Nini B, Sabokrou M, Hadid A (2020) Vision-based human activity recognition: a survey. Multimedia Tools and Applications 79(41\u201342):30509\u201330555","journal-title":"Multimedia Tools and Applications"},{"key":"5466_CR15","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s10462-020-09904-8","volume":"54","author":"P Pareek","year":"2021","unstructured":"Pareek P, Thakkar A (2021) A survey on video-based human action recognition:recent updates, datasets, challenges, and applications. Artif Intell Rev 54:2259\u20132322","journal-title":"Artif Intell Rev"},{"issue":"3","key":"5466_CR16","doi-asserted-by":"publisher","first-page":"768","DOI":"10.3390\/S21030768","volume":"21","author":"CM Ranieri","year":"2021","unstructured":"Ranieri CM, MacLeod S, Dragone M, Vargas PA, Romero RF (2021) Activity Recognition for Ambient Assisted Living with Videos. Inertial Units and Ambient Sensors. Sensors 21(3):768. https:\/\/doi.org\/10.3390\/S21030768","journal-title":"Inertial Units and Ambient Sensors. Sensors"},{"key":"5466_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJDRR.2020.102030","volume":"53","author":"U Iqbal","year":"2021","unstructured":"Iqbal U, Perez P, Li W, Barthelemy J (2021) How computer vision can facilitate flood management: A systematic review. International Journal of Disaster Risk Reduction 53:102030. https:\/\/doi.org\/10.1016\/J.IJDRR.2020.102030","journal-title":"International Journal of Disaster Risk Reduction"},{"issue":"1","key":"5466_CR18","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00521-022-07699-z","volume":"35","author":"V Demir","year":"2023","unstructured":"Demir V, Yaseen ZM (2023) Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review. Neural Comput Appl 35(1):303\u2013343. https:\/\/doi.org\/10.1007\/s00521-022-07699-z","journal-title":"Neural Comput Appl"},{"issue":"22","key":"5466_CR19","doi-asserted-by":"publisher","first-page":"5012","DOI":"10.3390\/s19225012","volume":"19","author":"B Arshad","year":"2019","unstructured":"Arshad B, Ogie R, Barthelemy J, Pradhan B, Verstaevel N, Perez P (2019) Computer vision and iot-based sensors in flood monitoring and mapping: A systematic review. Sensors 19(22):5012. https:\/\/doi.org\/10.3390\/s19225012","journal-title":"Sensors"},{"issue":"2","key":"5466_CR20","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s11269-021-03051-7","volume":"36","author":"K Khosravi","year":"2022","unstructured":"Khosravi K, Golkarian A, Tiefenbacher JP (2022) Using optimized deep learning to predict daily streamflow: a comparison to common machine learning algorithms. Water Resour Manage 36(2):699\u2013716","journal-title":"Water Resour Manage"},{"issue":"4","key":"5466_CR21","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1111\/mice.12629","volume":"36","author":"S Dong","year":"2021","unstructured":"Dong S, Yu T, Farahmand H, Mostafavi A (2021) A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data. Computer-Aided Civil and Infrastructure Engineering 36(4):402\u2013420","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"5466_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Y, Gu Z, Th\u00e9 JVG, Yang SX, Gharabaghi B (2022) The discharge forecasting of multiple monitoring station for humber river by hybrid lstm models. Water 14(11):1794","DOI":"10.3390\/w14111794"},{"key":"5466_CR23","doi-asserted-by":"crossref","unstructured":"Ouma YO, Omai L, et al (2023) Flood susceptibility mapping using image-based 2d-cnn deep learning: Overview and case study application using multipara-metric spatial data in data-scarce urban environments. International Journal of Intelligent Systems 2023","DOI":"10.1155\/2023\/5672401"},{"key":"5466_CR24","doi-asserted-by":"publisher","unstructured":"Xu Z, Feng J, Zhang Z, Duan C (2018) Water level estimation based on image of staff gauge in smart city. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), pp. 1341\u20131345. IEEE, Guangzhou, China. https:\/\/doi.org\/10.1109\/SmartWorld.2018.00233","DOI":"10.1109\/SmartWorld.2018.00233"},{"issue":"5","key":"5466_CR25","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.3390\/su12052149","volume":"12","author":"J Huang","year":"2020","unstructured":"Huang J, Kang J, Wang H, Wang Z, Qiu T (2020) A novel approach to measuring urban waterlogging depth from images based on mask region-based convolutional neural network. Sustainability 12(5):2149. https:\/\/doi.org\/10.3390\/su12052149","journal-title":"Sustainability"},{"issue":"24","key":"5466_CR26","doi-asserted-by":"publisher","first-page":"13393","DOI":"10.1007\/s00500-019-03878-8","volume":"23","author":"L Yang","year":"2019","unstructured":"Yang L, Cervone G (2019) Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Comput 23(24):13393\u201313408","journal-title":"Soft Comput"},{"key":"5466_CR27","doi-asserted-by":"publisher","unstructured":"Fernandes FE Jr, Yen GG (2021) Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy. Inf Sci 552:29\u201347. https:\/\/doi.org\/10.1016\/j.ins.2020.11.009. Accessed 2021-02-13","DOI":"10.1016\/j.ins.2020.11.009"},{"key":"5466_CR28","doi-asserted-by":"publisher","first-page":"73561","DOI":"10.1109\/ACCESS.2018.2883702","volume":"6","author":"J Pan","year":"2018","unstructured":"Pan J, Yin Y, Xiong J, Luo W, Gui G, Sari H (2018) Deep learning-based unmanned surveillance systems for observing water levels. IEEE Access 6:73561\u201373571. https:\/\/doi.org\/10.1109\/ACCESS.2018.2883702","journal-title":"IEEE Access"},{"key":"5466_CR29","doi-asserted-by":"publisher","unstructured":"Vandaele R, Dance SL, Ojha V (2021) Automated water segmentation and river level detection on camera images using transfer learning. In: Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, pp. 232\u2013245. Springer, T\u00fcbingen, Germany. https:\/\/doi.org\/10.1007\/978-3-030-71278-5_17","DOI":"10.1007\/978-3-030-71278-5_17"},{"key":"5466_CR30","doi-asserted-by":"crossref","unstructured":"Yurtkulu SC, \u015eahin YH, Unal G (2019) Semantic segmentation with extended deeplabv3 architecture. In: 2019 27th Signal Processing and Communications Applications Conference (SIU), pp. 1\u20134. IEEE","DOI":"10.1109\/SIU.2019.8806244"},{"key":"5466_CR31","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"5466_CR32","doi-asserted-by":"publisher","unstructured":"Fernandes FE, Nonato LG, Ueyama J (2022) A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications 1\u201321. https:\/\/doi.org\/10.1007\/s11042-022-12813-3","DOI":"10.1007\/s11042-022-12813-3"},{"issue":"7","key":"5466_CR33","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1007\/s00371-022-02501-6","volume":"39","author":"R Qiu","year":"2023","unstructured":"Qiu R, Cai Z, Chang Z, Liu S, Tu G (2023) A two-stage image process for water level recognition via dual-attention cornernet and ctransformer. Vis Comput 39(7):2933\u20132952","journal-title":"Vis Comput"},{"key":"5466_CR34","doi-asserted-by":"publisher","unstructured":"Saleem G, Bajwa UI, Raza RH (2022) Toward human activity recognition: a survey. Neural Comput Appl 1\u201338. https:\/\/doi.org\/10.1007\/s00521-022-07937-4","DOI":"10.1007\/s00521-022-07937-4"},{"key":"5466_CR35","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems 27 (NIPS), Montreal, Canada, pp. 568\u2013576. https:\/\/doi.org\/10.48550\/arXiv.1406.2199","DOI":"10.48550\/arXiv.1406.2199"},{"key":"5466_CR36","doi-asserted-by":"publisher","first-page":"16387","DOI":"10.1007\/s00521-018-3951-x","volume":"32","author":"A Ladjailia","year":"2020","unstructured":"Ladjailia A, Bouchrika I, Merouani HF, Harrati N, Mahfouf Z (2020) Human activity recognition via optical flow: decomposing activities into basic actions. Neural Comput Appl 32:16387\u201316400. https:\/\/doi.org\/10.1007\/s00521-018-3951-x","journal-title":"Neural Comput Appl"},{"key":"5466_CR37","doi-asserted-by":"publisher","unstructured":"McIlvenny J, Williamson B, Fairley I, Lewis M, Neill S, Masters I, Reeve DE (2022) Comparison of dense optical flow and piv techniques for mapping surface current flow in tidal stream energy sites. Int J Energy Environ Eng 1\u201313. https:\/\/doi.org\/10.1007\/s40095-022-00519-z","DOI":"10.1007\/s40095-022-00519-z"},{"key":"5466_CR38","unstructured":"Yagi J, Tani K, Fujita I, Nakayama K (2020) Application of optical flow techniques for river surface flow measurements. In: Proceedings of the 22nd IAHR APD Congress, Sapporo, Japan, pp. 14\u201317"},{"key":"5466_CR39","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.flowmeasinst.2018.11.009","volume":"65","author":"M Khalid","year":"2019","unstructured":"Khalid M, P\u00e9nard L, M\u00e9min E (2019) Optical flow for image-based river velocity estimation. Flow Meas Instrum 65:110\u2013121. https:\/\/doi.org\/10.1016\/j.flowmeasinst.2018.11.009","journal-title":"Flow Meas Instrum"},{"key":"5466_CR40","doi-asserted-by":"publisher","unstructured":"Urieva N, McDonald J, Uryeva T, Ramos ASR, Bhandari S (2020) Collision detection and avoidance using optical flow for multicopter uavs. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 607\u2013614. IEEE. https:\/\/doi.org\/10.1109\/ICUAS48674.2020.9213957","DOI":"10.1109\/ICUAS48674.2020.9213957"},{"key":"5466_CR41","doi-asserted-by":"publisher","unstructured":"Furquim G, Mello R, Pessin G, Fai\u00e7al BS, Mendiondo EM, Ueyama J (2014) An accurate flood forecasting model using wireless sensor networks and chaos theory: A case study with real wsn deployment in brazil. In: Mladenov V, Jayne C, Iliadis L (eds.)Engineering Applications of Neural Networks, pp. 92\u2013102. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-11071-4_9","DOI":"10.1007\/978-3-319-11071-4_9"},{"key":"5466_CR42","doi-asserted-by":"publisher","unstructured":"Furquim G, Filho G, Jalali R, Pessin G, Pazzi R, Ueyama J (2018) How to improve fault tolerance in disaster predictions: A case study about flash floods using (iot), ml and real data. Sensors 18(3):907. https:\/\/doi.org\/10.3390\/s18030907","DOI":"10.3390\/s18030907"},{"key":"5466_CR43","doi-asserted-by":"publisher","unstructured":"Sharma V, Gupta M, Pandey AK, Mishra D, Kumar A (2022) A review of deep learning-based human activity recognition on benchmark video datasets. Appl Artif Intell 36(1):2093705. https:\/\/doi.org\/10.1080\/08839514.2022.2093705","DOI":"10.1080\/08839514.2022.2093705"},{"key":"5466_CR44","doi-asserted-by":"publisher","unstructured":"Farneb\u00e4ck G (2003) Two-Frame Motion Estimation Based on Polynomial Expansion. In: Scandinavian Conference on Image Analysis (SCIA), pp. 363\u2013370. Springer, Halmstad, Sweden. https:\/\/doi.org\/10.1007\/3-540-45103-X_50","DOI":"10.1007\/3-540-45103-X_50"},{"issue":"3","key":"5466_CR45","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vision"},{"key":"5466_CR46","doi-asserted-by":"publisher","unstructured":"Neyshabur B, Sedghi H, Zhang C (2020) What is being transferred in transfer learning? Advances in neural information processing systems 33:512\u2013523. https:\/\/doi.org\/10.48550\/arXiv.2008.11687","DOI":"10.48550\/arXiv.2008.11687"},{"issue":"1\u20132","key":"5466_CR47","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3233\/HIS-210008","volume":"17","author":"S Bharati","year":"2021","unstructured":"Bharati S, Podder P, Mondal M, Prasath V (2021) Co-resnet: Optimized resnet model for covid-19 diagnosis from x-ray images. International Journal of Hybrid Intelligent Systems 17(1\u20132):71\u201385. https:\/\/doi.org\/10.3233\/HIS-210008","journal-title":"International Journal of Hybrid Intelligent Systems"},{"key":"5466_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jafr.2022.100323","volume":"9","author":"RG Dawod","year":"2022","unstructured":"Dawod RG, Dobre C (2022) Resnet interpretation methods applied to the classification of foliar diseases in sunflower. Journal of Agriculture and Food Research 9:100323. https:\/\/doi.org\/10.1016\/j.jafr.2022.100323","journal-title":"Journal of Agriculture and Food Research"},{"key":"5466_CR49","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","volume":"32","author":"L Wen","year":"2020","unstructured":"Wen L, Li X, Gao L (2020) A transfer convolutional neural network for fault diagnosis based on resnet-50. Neural Comput Appl 32:6111\u20136124. https:\/\/doi.org\/10.1007\/s00521-019-04097-w","journal-title":"Neural Comput Appl"},{"key":"5466_CR50","doi-asserted-by":"publisher","unstructured":"Oliveira MLL, Bekooij MJ (2022) Resnet applied for a single-snapshot doa estimation. In: 2022 IEEE Radar Conference (RadarConf22), pp. 1\u20136. IEEE, New York City, NY, USA. https:\/\/doi.org\/10.1109\/RadarConf2248738.2022.9763905","DOI":"10.1109\/RadarConf2248738.2022.9763905"},{"key":"5466_CR51","volume-title":"Deep Learning","author":"Y Bengio","year":"2017","unstructured":"Bengio Y, Goodfellow I, Courville A (2017) Deep Learning, vol 1. MIT press Cambridge, MA, USA"},{"key":"5466_CR52","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. In: 2010 20th International Conference on Pattern Recognition, pp. 3121\u20133124. IEEE","DOI":"10.1109\/ICPR.2010.764"},{"issue":"27","key":"5466_CR53","first-page":"12","volume":"1","author":"H Haibo","year":"2013","unstructured":"Haibo H, Yunqian M (2013) Imbalanced learning: foundations, algorithms, and applications. Wiley-IEEE Press 1(27):12","journal-title":"Wiley-IEEE Press"},{"key":"5466_CR54","doi-asserted-by":"crossref","unstructured":"Kubat M, Kubat J (2017) An Introduction to Machine Learning vol. 2. Springer","DOI":"10.1007\/978-3-319-63913-0"},{"key":"5466_CR55","doi-asserted-by":"publisher","first-page":"171263","DOI":"10.1109\/ACCESS.2020.3014362","volume":"8","author":"N Liu","year":"2020","unstructured":"Liu N, Li X, Qi E, Xu M, Li L, Gao B (2020) A novel ensemble learning paradigm for medical diagnosis with imbalanced data. IEEE Access 8:171263\u2013171280","journal-title":"IEEE Access"},{"key":"5466_CR56","doi-asserted-by":"publisher","first-page":"28210","DOI":"10.1109\/ACCESS.2020.2972009","volume":"8","author":"SN Kalid","year":"2020","unstructured":"Kalid SN, Ng KH, Tong GK, Khor KC (2020) A multiple classifiers system for anomaly detection in credit card data with unbalanced and overlapped classes. IEEE access 8:28210\u201328221","journal-title":"IEEE access"},{"key":"5466_CR57","unstructured":"Lakshmanan V, Robinson S, Munn M (2020) Machine Learning Design Patterns. O\u2019Reilly Media"},{"key":"5466_CR58","doi-asserted-by":"publisher","unstructured":"Garc\u00eda V, Mollineda RA, S\u00e1nchez JS (2009) Index of balanced accuracy: A performance measure for skewed class distributions. In: Araujo H, Mendon\u00e7a AM, Pinho AJ, Torres MI (eds.) Pattern Recognition and Image Analysis, pp. 441\u2013448. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-02172-5_57","DOI":"10.1007\/978-3-642-02172-5_57"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05466-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05466-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05466-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T14:14:12Z","timestamp":1715609652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05466-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":58,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5466"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05466-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"13 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors agreed to publish this paper in its present form.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}