{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T06:29:32Z","timestamp":1778912972532,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Tomsk State University Development Programme","award":["Priority-2030"],"award-info":[{"award-number":["Priority-2030"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00521-023-08708-5","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T06:02:12Z","timestamp":1688364132000},"page":"19719-19727","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0965-2358","authenticated-orcid":false,"given":"Mohammad","family":"Ghalambaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Edalatifar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1858-5604","authenticated-orcid":false,"given":"Sara","family":"Moradi Maryamnegari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikhail","family":"Sheremet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"8708_CR1","doi-asserted-by":"publisher","first-page":"115487","DOI":"10.1016\/j.applthermaleng.2020.115487","volume":"178","author":"M Hemmat Esfe","year":"2020","unstructured":"Hemmat Esfe M, Bahiraei M, Hajbarati H, Valadkhani M (2020) A comprehensive review on convective heat transfer of nanofluids in porous media: energy-related and thermohydraulic characteristics. Appl Therm Eng 178:115487","journal-title":"Appl Therm Eng"},{"key":"8708_CR2","doi-asserted-by":"publisher","first-page":"112453","DOI":"10.1016\/j.rser.2022.112453","volume":"162","author":"M G\u00fcrdal","year":"2022","unstructured":"G\u00fcrdal M, Arslan K, Gedik E, Minea AA (2022) Effects of using nanofluid, applying a magnetic field, and placing turbulators in channels on the convective heat transfer: a comprehensive review. Renew Sustain Energy Rev 162:112453","journal-title":"Renew Sustain Energy Rev"},{"key":"8708_CR3","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.cjph.2022.06.023","volume":"80","author":"MB Rekha","year":"2022","unstructured":"Rekha MB, Sarris IE, Madhukesh JK, Raghunatha KR, Prasannakumara BC (2022) Impact of thermophoretic particle deposition on heat transfer and nanofluid flow through different geometries: an application to solar energy. Chinese J Phys. 80:190\u2013205","journal-title":"Chinese J Phys."},{"key":"8708_CR4","doi-asserted-by":"publisher","first-page":"107651","DOI":"10.1016\/j.ijthermalsci.2022.107651","volume":"179","author":"FM Altunay","year":"2022","unstructured":"Altunay FM, Pazarl\u0131o\u011flu HK, G\u00fcrdal M, Tekir M, Arslan K, Gedik E (2022) Thermal performance of Fe3O4\/water nanofluid flow in a newly designed dimpled tube under the influence of non-uniform magnetic field. Int J Therm Sci 179:107651","journal-title":"Int J Therm Sci"},{"key":"8708_CR5","doi-asserted-by":"publisher","first-page":"107767","DOI":"10.1016\/j.ijthermalsci.2022.107767","volume":"181","author":"SA Nada","year":"2022","unstructured":"Nada SA, El-Zoheiry RM, Elsharnoby M, Osman OS (2022) Enhancing the thermal performance of different flow configuration minichannel heat sink using Al2O3 and CuO-water nanofluids for electronic cooling: an experimental assessment. Int J Therm Sci 181:107767","journal-title":"Int J Therm Sci"},{"key":"8708_CR6","doi-asserted-by":"publisher","first-page":"339927","DOI":"10.1016\/j.aca.2022.339927","volume":"1221","author":"H Wang","year":"2022","unstructured":"Wang H, Chen X (2022) Numerical simulation of heat transfer and flow of Al2O3-water nanofluid in microchannel heat sink with cantor fractal structure based on genetic algorithm. Anal Chim Acta 1221:339927","journal-title":"Anal Chim Acta"},{"key":"8708_CR7","first-page":"101888","volume":"50","author":"S Samiezadeh","year":"2022","unstructured":"Samiezadeh S, Khodaverdian R, Doranehgard MH, Chehrmonavari H, Xiong Q (2022) CFD simulation of thermal performance of hybrid oil-Cu-Al2O3 nanofluid flowing through the porous receiver tube inside a finned parabolic trough solar collector. Sustain Energy Technol Assess 50:101888","journal-title":"Sustain Energy Technol Assess"},{"key":"8708_CR8","first-page":"101340","volume":"47","author":"M Khodadadi","year":"2021","unstructured":"Khodadadi M, Ali Farshad S, Ebrahimpour Z, Sheikholeslami M (2021) Thermal performance of nanofluid with employing of NEPCM in a PVT-LFR system. Sustain Energy Technol Assess 47:101340","journal-title":"Sustain Energy Technol Assess"},{"key":"8708_CR9","doi-asserted-by":"publisher","first-page":"114232","DOI":"10.1016\/j.enconman.2021.114232","volume":"240","author":"F Yazdanifard","year":"2021","unstructured":"Yazdanifard F, Ameri M, Taylor R (2021) Parametric investigation of a nanofluid-NEPCM based spectral splitting photovoltaic\/thermal system. Energy Convers Manage 240:114232","journal-title":"Energy Convers Manage"},{"key":"8708_CR10","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.ijheatmasstransfer.2019.04.037","volume":"138","author":"M Ghalambaz","year":"2019","unstructured":"Ghalambaz M, Chamkha AJ, Wen D (2019) Natural convective flow and heat transfer of nano-encapsulated phase change materials (NEPCMs) in a cavity. Int J Heat Mass Transf 138:738\u2013749","journal-title":"Int J Heat Mass Transf"},{"key":"8708_CR11","doi-asserted-by":"publisher","first-page":"104975","DOI":"10.1016\/j.est.2022.104975","volume":"53","author":"S Hussain","year":"2022","unstructured":"Hussain S, Molana M, Armaghani T, Rashad A, Nabwey HA (2022) Energy storage performance and irreversibility analysis of a water-based suspension containing nano-encapsulated phase change materials in a porous staggered cavity. J Energy Storage 53:104975","journal-title":"J Energy Storage"},{"key":"8708_CR12","doi-asserted-by":"publisher","first-page":"104168","DOI":"10.1016\/j.est.2022.104168","volume":"51","author":"A Alhashash","year":"2022","unstructured":"Alhashash A, Saleh H (2022) Free convection flow of a heterogeneous mixture of water and nano-encapsulated phase change particle (NEPCP) in enclosure subject to rotation. J Energy Storage 51:104168","journal-title":"J Energy Storage"},{"issue":"5","key":"8708_CR13","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.1007\/s13369-018-3401-1","volume":"44","author":"H G\u00fcll\u00fc","year":"2019","unstructured":"G\u00fcll\u00fc H, Canakci H, Alhashemy A (2019) A ranking distance analysis for performance assessment of UCS versus SPT-N correlations. Arab J Sci Eng 44(5):4325\u20134337","journal-title":"Arab J Sci Eng"},{"issue":"5","key":"8708_CR14","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1080\/19648189.2016.1210036","volume":"22","author":"H G\u00fcll\u00fc","year":"2018","unstructured":"G\u00fcll\u00fc H, Canakci H, Alhashemy A (2018) Use of ranking measure for performance assessment of correlations for the compression index. Eur J Environ Civ Eng 22(5):578\u2013595","journal-title":"Eur J Environ Civ Eng"},{"key":"8708_CR15","doi-asserted-by":"publisher","first-page":"101983","DOI":"10.1016\/j.csite.2022.101983","volume":"34","author":"MM Matheswaran","year":"2022","unstructured":"Matheswaran MM, Arjunan TV, Muthusamy S, Natrayan L, Panchal H, Subramaniam S, Khedkar NK, El-Shafay A, Sonawane C (2022) A case study on thermo-hydraulic performance of jet plate solar air heater using response surface methodology. Case Stud Therm Eng 34:101983","journal-title":"Case Stud Therm Eng"},{"issue":"5","key":"8708_CR16","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1007\/s12205-016-0724-x","volume":"21","author":"H G\u00fcll\u00fc","year":"2017","unstructured":"G\u00fcll\u00fc H, Fedakar H\u0130 (2017) Response surface methodology for optimization of stabilizer dosage rates of marginal sand stabilized with sludge ash and fiber based on UCS performances. KSCE J Civ Eng 21(5):1717\u20131727","journal-title":"KSCE J Civ Eng"},{"issue":"3","key":"8708_CR17","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.sandf.2017.05.006","volume":"57","author":"H G\u00fcll\u00fc","year":"2017","unstructured":"G\u00fcll\u00fc H (2017) A new prediction method for the rheological behavior of grout with bottom ash for jet grouting columns. Soils Found 57(3):384\u2013396","journal-title":"Soils Found"},{"issue":"1","key":"8708_CR18","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00521-016-2360-2","volume":"28","author":"H G\u00fcll\u00fc","year":"2017","unstructured":"G\u00fcll\u00fc H (2017) A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming. Neural Comput Appl 28(1):407\u2013420","journal-title":"Neural Comput Appl"},{"issue":"3","key":"8708_CR19","doi-asserted-by":"publisher","first-page":"441","DOI":"10.12989\/gae.2017.12.3.441","volume":"12","author":"H Gullu","year":"2017","unstructured":"Gullu H (2017) On the prediction of unconfined compressive strength of silty soil stabilized with bottom ash, jute and steel fibers via artificial intelligence. Geomech Eng 12(3):441\u2013464","journal-title":"Geomech Eng"},{"issue":"11","key":"8708_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3567591","volume":"55","author":"C Legaard","year":"2023","unstructured":"Legaard C, Schranz T, Schweiger G, Drgo\u0148a J, Falay B, Gomes C, Iosifidis A, Abkar M, Larsen P (2023) Constructing neural network based models for simulating dynamical systems. ACM Comput Surv 55(11):1\u201334","journal-title":"ACM Comput Surv"},{"issue":"2","key":"8708_CR21","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/MSP.2022.3183809","volume":"40","author":"Q Yang","year":"2023","unstructured":"Yang Q, Wang Z, Guo K, Cai C, Qu X (2023) Physics-driven synthetic data learning for biomedical magnetic resonance: the imaging physics-based data synthesis paradigm for artificial intelligence. IEEE Signal Process Mag 40(2):129\u2013140","journal-title":"IEEE Signal Process Mag"},{"issue":"2","key":"8708_CR22","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/BF01272517","volume":"1","author":"J H\u00e5stad","year":"1991","unstructured":"H\u00e5stad J, Goldmann M (1991) On the power of small-depth threshold circuits. Comput Complex 1(2):113\u2013129","journal-title":"Comput Complex"},{"issue":"15","key":"8708_CR23","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1016\/j.ijheatmasstransfer.2006.12.017","volume":"50","author":"K Ermis","year":"2007","unstructured":"Ermis K, Erek A, Dincer I (2007) Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network. Int J Heat Mass Transf 50(15):3163\u20133175","journal-title":"Int J Heat Mass Transf"},{"issue":"12","key":"8708_CR24","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1080\/10407782.2013.757154","volume":"63","author":"CSN Azwadi","year":"2013","unstructured":"Azwadi CSN, Zeinali M, Safdari A, Kazemi A (2013) Adaptive-network-based fuzzy inference system analysis to predict the temperature and flow fields in a lid-driven cavity. Numeri Heat Transf, Part A Appl 63(12):906\u2013920","journal-title":"Numeri Heat Transf, Part A Appl"},{"issue":"2","key":"8708_CR25","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1080\/02286203.2018.1555446","volume":"40","author":"E Akbari","year":"2020","unstructured":"Akbari E, Karami A, Nazari S, Ashjaee M (2020) An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the natural convection in an open round cavity. Int J Model Simul 40(2):87\u2013103","journal-title":"Int J Model Simul"},{"issue":"3","key":"8708_CR26","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s10973-019-08865-7","volume":"140","author":"F Selimefendigil","year":"2020","unstructured":"Selimefendigil F, Akbulut Y, Sengur A, Oztop HF (2020) MHD conjugate natural convection in a porous cavity involving a curved conductive partition and estimations by using long short-term memory networks. J Therm Anal Calorim 140(3):1457\u20131468","journal-title":"J Therm Anal Calorim"},{"key":"8708_CR27","doi-asserted-by":"publisher","first-page":"3481","DOI":"10.2298\/TSCI171113084Z","volume":"23","author":"S Zhou","year":"2019","unstructured":"Zhou S, Liu X, Du G, Liu C, Zhou Y (2019) Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure. Therm sci 23:3481\u20133492","journal-title":"Therm sci"},{"issue":"4","key":"8708_CR28","first-page":"1270","volume":"8","author":"M Edalatifar","year":"2022","unstructured":"Edalatifar M, Tavakoli MB, Setoudeh F (2022) A deep learning approach to predict the flow field and thermal patterns of nonencapsulated phase change materials suspensions in an enclosure. J Appl Comput Mech 8(4):1270\u20131278","journal-title":"J Appl Comput Mech"},{"key":"8708_CR29","doi-asserted-by":"publisher","unstructured":"Edalatifar M, Ghalambaz M, Tavakoli MB, Setoudeh F(2023) deep learning approach to natural convection heat transfer in a cavity: a simulation dataset for nano-encapsulated phase change material suspensions. Mendeley Data. https:\/\/doi.org\/10.17632\/j5f6r56jnb.1","DOI":"10.17632\/j5f6r56jnb.1"},{"key":"8708_CR30","doi-asserted-by":"publisher","unstructured":"Ghalambaz M, Edalatifar M, Moradimaryamnegari S, Sheremet M (2023) Nano-PCM intelligence classification dataset for energy storage and heat transfer analysis using deep neural networks. Mendeley Data. https:\/\/doi.org\/10.17632\/jp96vj3frz.1","DOI":"10.17632\/jp96vj3frz.1"},{"key":"8708_CR31","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167."},{"key":"8708_CR32","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems."},{"issue":"6","key":"8708_CR33","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":"8708_CR34","volume-title":"Artificial neural networks and machine learning\u2013ICANN 2018","author":"C Tan","year":"2018","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: K\u016frkov\u00e1 V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (eds) Artificial neural networks and machine learning\u2013ICANN 2018. Springer, Cham"},{"key":"8708_CR35","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics."},{"key":"8708_CR36","unstructured":"Chollet, F.,Others, Keras. 2015, https:\/\/keras.io."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08708-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08708-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08708-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:03:13Z","timestamp":1693353793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08708-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,3]]},"references-count":36,"journal-issue":{"issue":"27","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["8708"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08708-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,3]]},"assertion":[{"value":"18 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2023","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 clarify that there is no conflict of interest to report.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}