{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:15:06Z","timestamp":1742973306224,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031843556"},{"type":"electronic","value":"9783031843563"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-84356-3_26","type":"book-chapter","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T13:47:18Z","timestamp":1740491238000},"page":"320-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Viscosity Estimation in\u00a0Water-PVP Solutions Through Droplet Image Analysis Using a\u00a0MobileNet-Enhanced CNN"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5223-3680","authenticated-orcid":false,"given":"Mohamed Azouz","family":"Mrad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristof","family":"Csorba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dori\u00e1n L\u00e1szl\u00f3","family":"Galata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zsombor Krist\u00f3f","family":"Nagy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hassan","family":"Charaf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"26_CR1","unstructured":"Viswanath, D.S., Ghosh, T.K., Prasad, D.H., Dutt, N.V., Rani, K.Y.: Viscosity of Liquids: Theory, Estimation, Experiment, and Data. Springer Science & Business Media, Dordrecht, The Netherlands (2007)"},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"452","DOI":"10.3390\/pharmaceutics13040452","volume":"13","author":"E Toropainen","year":"2021","unstructured":"Toropainen, E., et al.: Biopharmaceutics of topical ophthalmic suspensions: importance of viscosity and particle size in ocular absorption of indomethacin. Pharmaceutics 13, 452 (2021)","journal-title":"Pharmaceutics"},{"key":"26_CR3","first-page":"414","volume":"7","author":"AB Lokhande","year":"2013","unstructured":"Lokhande, A.B., Mishra, S., Kulkarni, R.D., Naik, J.B.: Influence of different viscosity grade ethylcellulose polymers on encapsulation and in vitro release study of drug loaded nanoparticles. J. Pharm. Res. 7, 414\u2013420 (2013)","journal-title":"J. Pharm. Res."},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Bourne, M.: Food Texture and Viscosity: Concept and Measurement. Elsevier, Amsterdam, The Netherlands (2002)","DOI":"10.1016\/B978-012119062-0\/50007-3"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Nunes, V.M., Louren\u00e7o, M.J., Santos, F.J., Nieto de Castro, C.A.: Importance of accurate data on viscosity and thermal conductivity in molten salts applications. J. Chem. Eng. Data 2003, 48, 446-450","DOI":"10.1021\/je020160l"},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s10596-012-9280-8","volume":"16","author":"B Rashid","year":"2012","unstructured":"Rashid, B., Bal, A.L., Williams, G.J., Muggeridge, A.H.: Using vorticity to quantify the relative importance of heterogeneity, viscosity ratio, gravity and diffusion on oil recovery. Comput. Geosci. 16, 409\u2013422 (2012)","journal-title":"Comput. Geosci."},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.fuel.2013.07.072","volume":"116","author":"A Hemmati-Sarapardeh","year":"2014","unstructured":"Hemmati-Sarapardeh, A., Shokrollahi, A., Tatar, A., Gharagheizi, F., Mohammadi, A.H., Naseri, A.: Reservoir oil viscosity determination using a rigorous approach. Fuel 116, 39\u201348 (2014)","journal-title":"Fuel"},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1088\/0957-0233\/16\/2\/005","volume":"16","author":"R Brooks","year":"2005","unstructured":"Brooks, R., Dinsdale, A., Quested, P.: The measurement of viscosity of alloys-a review of methods, data and models. Meas. Sci. Technol. 16, 354 (2005)","journal-title":"Meas. Sci. Technol."},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"5277","DOI":"10.1021\/acs.energyfuels.6b00300","volume":"30","author":"H Zhao","year":"2016","unstructured":"Zhao, H., et al.: Heavy oil viscosity measurements: best practices and guidelines. Energy Fuels 30, 5277\u20135290 (2016)","journal-title":"Energy Fuels"},{"key":"26_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2022.127320","volume":"339","author":"M Caponi","year":"2023","unstructured":"Caponi, M., Cox, A., Misra, S.: Viscosity prediction using image processing and supervised learning. Fuel 339, 127320 (2023)","journal-title":"Fuel"},{"key":"26_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2021.122812","volume":"312","author":"T Zhang","year":"2022","unstructured":"Zhang, T., et al.: Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions. Fuel 312, 122812 (2022)","journal-title":"Fuel"},{"key":"26_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2022.123422","volume":"316","author":"E Cengiz","year":"2022","unstructured":"Cengiz, E., Babagiray, M., Aysal, F.E., Aksoy, F.: Kinematic viscosity estimation of fuel oil with comparison of machine learning methods. Fuel 316, 123422 (2022)","journal-title":"Fuel"},{"key":"26_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2020.119146","volume":"285","author":"H Rahmanifard","year":"2021","unstructured":"Rahmanifard, H., Maroufi, P., Alimohamadi, H., Plaksina, T., Gates, I.: The application of supervised machine learning techniques for multivariate modelling of gas component viscosity: a comparative study. Fuel 285, 119146 (2021)","journal-title":"Fuel"},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.icheatmasstransfer.2016.05.023","volume":"76","author":"M Afrand","year":"2016","unstructured":"Afrand, M., et al.: Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network. Int. Commun. Heat Mass Transf. 76, 209\u2013214 (2016)","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"26_CR15","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.icheatmasstransfer.2015.06.013","volume":"68","author":"MH Esfe","year":"2015","unstructured":"Esfe, M.H., Saedodin, S., Sina, N., Afrand, M., Rostami, S.: Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid. Int. Commun. Heat Mass Transf. 68, 50\u201357 (2015)","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Al-Amoudi, L.A., Patil, S., Baarimah, S.O.: Development of artificial intelligence models for prediction of crude oil viscosity. In SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 18\u201321, OnePetro: Richardson, p. 2019. TX, USA (2019)","DOI":"10.2118\/194741-MS"},{"key":"26_CR17","first-page":"181","volume":"51","author":"O Omole","year":"2009","unstructured":"Omole, O., Falode, O., Deng, A.D.: Prediction of Nigerian crude oil viscosity using artificial neural network. Pet. Coal 51, 181\u2013188 (2009)","journal-title":"Pet. Coal"},{"key":"26_CR18","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1006\/jaer.1997.0151","volume":"67","author":"H Zhu","year":"1997","unstructured":"Zhu, H., Dexter, R., Fox, R., Reichard, D., Brazee, R., Ozkan, H.: Effects of polymer composition and viscosity on droplet size of recirculated spray solutions. J. Agric. Eng. Res. 67, 35\u201345 (1997)","journal-title":"J. Agric. Eng. Res."},{"key":"26_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2020.115621","volume":"220","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Liu, H., Zhang, Z., Sun, B., Zhang, J., Lou, W.: Research on the effects of liquid viscosity on droplet size in vertical gas-liquid annular flows. Chem. Eng. Sci. 220, 115621 (2020)","journal-title":"Chem. Eng. Sci."},{"key":"26_CR20","doi-asserted-by":"publisher","DOI":"10.1063\/1.2781603","volume":"19","author":"C Gotaas","year":"2007","unstructured":"Gotaas, C., et al.: Effect of viscosity on droplet-droplet collision outcome: experimental study and numerical comparison. Phys. Fluids 19, 102106 (2007)","journal-title":"Phys. Fluids"},{"key":"26_CR21","first-page":"15","volume":"24","author":"N Kheloufi","year":"2014","unstructured":"Kheloufi, N., Lounis, M.: An optical technique for Newtonian fluid viscosity measurement using multiparameter analysis. Appl. Rheol. 24, 15\u201322 (2014)","journal-title":"Appl. Rheol."},{"key":"26_CR22","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.3390\/pr10091780","volume":"10","author":"MA Mrad","year":"2022","unstructured":"Mrad, M.A., Csorba, K., Galata, D.L., Nagy, Z.K.: Classification of droplets of water-PVP solutions with different viscosity values using artificial neural networks. Processes 10, 1780 (2022)","journal-title":"Processes"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Mrad, M.A., Csorba, K., Galata, D., Nagy, Z.K., Charaf, H.: Viscosity estimation of water-PVP solutions from droplets using artificial neural networks and image processing. In International Conference on Artificial Intelligence and Soft Computing, pp. 157\u2013166. Springer Nature Switzerland, Cham (2023)","DOI":"10.1007\/978-3-031-42505-9_14"},{"issue":"7","key":"26_CR24","doi-asserted-by":"publisher","first-page":"1917","DOI":"10.3390\/pr11071917","volume":"11","author":"MA Mrad","year":"2023","unstructured":"Mrad, M.A., Csorba, K., Galata, D.L., Nagy, Z.K., Charaf, H.: Droplet based estimation of viscosity of water-PVP solutions using convolutional neural networks. Processes 11(7), 1917 (2023)","journal-title":"Processes"},{"key":"26_CR25","doi-asserted-by":"publisher","first-page":"98693","DOI":"10.17485\/ijst\/2016\/v9i30\/98693","volume":"9","author":"K Santhosh","year":"2016","unstructured":"Santhosh, K., Shenoy, V.: Analysis of liquid viscosity by image processing techniques. Indian J. Sci. Technol. 9, 98693 (2016)","journal-title":"Indian J. Sci. Technol."},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Sakib, S., Ahmed, N., Kabir, A.J., Ahmed, H.: An overview of convolutional neural network: its architecture and applications (2019)","DOI":"10.20944\/preprints201811.0546.v4"},{"key":"26_CR27","volume":"4","author":"H Iwata","year":"2022","unstructured":"Iwata, H., Hayashi, Y., Hasegawa, A., Terayama, K., Okuno, Y.: Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning. Int. J. Pharm. 4, 100135 (2022)","journal-title":"Int. J. Pharm."},{"key":"26_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2021.117884","volume":"289","author":"Z Ghorbani","year":"2021","unstructured":"Ghorbani, Z., Behzadan, A.H.: Monitoring offshore oil pollution using multi-class convolutional neural networks. Environ. Pollut. 289, 117884 (2021)","journal-title":"Environ. Pollut."},{"key":"26_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104382","volume":"133","author":"L Vasconcelos","year":"2021","unstructured":"Vasconcelos, L., Kijanka, P., Urban, M.W.: Viscoelastic parameter estimation using simulated shear wave motion and convolutional neural networks. Comput. Biol. Med. 133, 104382 (2021)","journal-title":"Comput. Biol. Med."},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Vishnu Mohan, M.S., Menon, V.: Measuring Viscosity of Fluids: a deep learning approach using a CNN-RNN architecture. In: Proceedings of the First International Conference on AI-ML-Systems, Bangalore, India, 21\u201323 October 2021; pp.\u00a01\u20135 (2021)","DOI":"10.1145\/3486001.3486232"},{"key":"26_CR31","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770\u2013778) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"26_CR34","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"26_CR35","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1246\/bcsj.40.2217","volume":"40","author":"T Mineshita","year":"1967","unstructured":"Mineshita, T., Watanabe, T., Ono, S.: The flow properties of polyvinylpyrrolidone solutions. Bull. Chem. Soc. Jpn 40, 2217\u20132223 (1967)","journal-title":"Bull. Chem. Soc. Jpn"},{"key":"26_CR36","unstructured":"Naveenkumar, M., Vadivel, A.: OpenCV for computer vision applications. In: Proceedings of the National Conference on Big Data and Cloud Computing (NCBDC\u201915), Tiruchirappalli, India, 20 March 2015; pp. 52\u201356 (2015)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-84356-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T13:51:03Z","timestamp":1740491463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-84356-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031843556","9783031843563"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-84356-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icaisc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}