{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T21:13:24Z","timestamp":1778102004929,"version":"3.51.4"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114800","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:18:01Z","timestamp":1775809081000},"page":"114800","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Artificial neural network-assisted optimization of slippery boundaries on the fluid transport in permeable renal tubules"],"prefix":"10.1016","volume":"176","author":[{"given":"Venkateshwarlu","family":"G.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravikiran","family":"G.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varunkumar","family":"M.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.S.K.","family":"Raju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114800_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111159","article-title":"Machine learning study for three-dimensional magnetohydrodynamics casson fluid flow with Cattaneo Christov heat flux using linear regression technique: Application in engineering science and technology","volume":"156","author":"Abdal","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114800_b2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0092-8240(81)90013-6","article-title":"A hydrodynamical study of the flow in renal tubules","volume":"43","author":"Acharya","year":"1981","journal-title":"Bull. Math. Biol."},{"issue":"11","key":"10.1016\/j.engappai.2026.114800_b3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11242-025-02239-4","article-title":"A general framework for predicting permeability in porous structures using convolutional neural networks with error estimation","volume":"152","author":"Adam","year":"2025","journal-title":"Transp. Porous Media"},{"key":"10.1016\/j.engappai.2026.114800_b4","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/0022-247X(86)90160-5","article-title":"Application of the decomposition method to the Navier\u2013Stokes equations","volume":"119","author":"Adomian","year":"1986","journal-title":"J. Math. Anal. Appl."},{"key":"10.1016\/j.engappai.2026.114800_b5","first-page":"1","article-title":"A mathematical analysis of capillary tissue fluid exchange","volume":"11","author":"Apelblat","year":"1974","journal-title":"Biorheology"},{"key":"10.1016\/j.engappai.2026.114800_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110947","article-title":"Forced convective micropolar nanofluidic transportation with Cattaneo Christov heat and mass flux model: Levenberg Marquardt backpropagation neural network approach","volume":"154","author":"Baithalu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114800_b7","first-page":"519","article-title":"Solution of two-dimensional nonlinear differential equations by the Adomian decomposition method","volume":"163","author":"Bayramoglu","year":"2005","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.engappai.2026.114800_b8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1017\/S0022112067001375","article-title":"Boundary conditions at a naturally permeable wall","volume":"30","author":"Beavers","year":"1967","journal-title":"J. Fluid Mech."},{"key":"10.1016\/j.engappai.2026.114800_b9","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1063\/1.1721476","article-title":"Laminar flow in channels with porous walls","volume":"24","author":"Berman","year":"1953","journal-title":"J. Appl. Phys."},{"key":"10.1016\/j.engappai.2026.114800_b10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1063\/1.1722948","article-title":"Laminar flow in an annulus with porous walls","volume":"29","author":"Berman","year":"1958","journal-title":"J. Appl. Phys."},{"key":"10.1016\/j.engappai.2026.114800_b11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/BF01170591","article-title":"Flow of Newtonian fluid in non-uniform tubes with variable wall permeability with application to flow in renal tubules","volume":"88","author":"Chaturani","year":"1991","journal-title":"Acta Mech."},{"issue":"12","key":"10.1016\/j.engappai.2026.114800_b12","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1140\/epjp\/s13360-023-04740-5","article-title":"An evolutionary-based neural network approach to investigate heat and mass transportation by using non-Fourier double-diffusion theories for Prandtl nanofluid under hall and ion slip effects","volume":"138","author":"Habib","year":"2023","journal-title":"Eur. Phys. J. Plus"},{"key":"10.1016\/j.engappai.2026.114800_b13","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1016\/j.aej.2016.03.036","article-title":"Stokes flow through a slit with periodic reabsorption: Application to renal tubule","volume":"55","author":"Haroon","year":"2016","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.engappai.2026.114800_b14","first-page":"2477","article-title":"Steady creeping slip flow of viscous fluid through a permeable slit with exponential reabsorption","volume":"11","author":"Haroon","year":"2017","journal-title":"Appl. Math. Sci."},{"issue":"1","key":"10.1016\/j.engappai.2026.114800_b15","article-title":"An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid","volume":"19","author":"Hussain","year":"2025","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"10.1016\/j.engappai.2026.114800_b16","doi-asserted-by":"crossref","first-page":"18747","DOI":"10.1038\/s41598-022-23308-4","article-title":"Influence of variable velocity slip condition and activation energy on MHD peristaltic flow of Prandtl nanofluid through a non-uniform channel","volume":"12","author":"Ibrahim","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.114800_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.icheatmasstransfer.2026.110592","article-title":"PINN-based numerical modeling of MHD flows in porous media over linear stretching boundaries","volume":"172","author":"Jan","year":"2026","journal-title":"Int. Commun. Heat Mass Transfer"},{"key":"10.1016\/j.engappai.2026.114800_b18","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02477961","article-title":"A theoretical note on exponential flow in the proximal part of the mammalian nephron","volume":"24","author":"Kelman","year":"1962","journal-title":"Bull. Math. Biophys."},{"issue":"12","key":"10.1016\/j.engappai.2026.114800_b19","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1080\/10407790.2023.2274448","article-title":"Intelligent computing levenberg\u2013marquardt paradigm for the analysis of hall current on thermal radiative hybrid nanofluid flow over a spinning surface","volume":"85","author":"Khan","year":"2024","journal-title":"Numer. Heat Transfer B"},{"key":"10.1016\/j.engappai.2026.114800_b20","first-page":"1","article-title":"Physics-informed neural networks for heat transfer in non-Newtonian Casson fluid flow around a horizontal cylinder","author":"Kim","year":"2025","journal-title":"Int. J. Comput. Methods Eng. Sci. Mech."},{"issue":"3","key":"10.1016\/j.engappai.2026.114800_b21","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1021\/i160035a033","article-title":"Velocity profiles in porous-walled ducts","volume":"9","author":"Kozinski","year":"1970","journal-title":"Ind. Eng. Chem. Fundam."},{"key":"10.1016\/j.engappai.2026.114800_b22","doi-asserted-by":"crossref","first-page":"102242","DOI":"10.1109\/ACCESS.2024.3422099","article-title":"Neural network model using levenberg marquardt backpropagation algorithm for the prandtl fluid flow over stratified curved sheet","volume":"12","author":"Kumar","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114800_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111256","article-title":"Artificial neural networks for mass transfer and bioconvection analysis in radiative eyring Powell flow over a convective cylinder surface: Application to microbial fuel cells","volume":"156","author":"Kumar","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114800_b24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF02477766","article-title":"Pressure flow patterns in a cylinder with reabsorbing walls","volume":"25","author":"Macey","year":"1963","journal-title":"Bull. Math. Biophys."},{"key":"10.1016\/j.engappai.2026.114800_b25","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/BF02498766","article-title":"Hydrodynamics of renal tubule","volume":"27","author":"Macey","year":"1965","journal-title":"Bull. Math. Biophys."},{"issue":"3","key":"10.1016\/j.engappai.2026.114800_b26","doi-asserted-by":"crossref","DOI":"10.1063\/5.0082942","article-title":"Thermo-fluidic transport process in a novel M-shaped cavity packed with non-darcian porous medium and hybrid nanofluid: Application of artificial neural network (ANN)","volume":"34","author":"Mandal","year":"2022","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.engappai.2026.114800_b27","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/BF02463260","article-title":"Flow of a Newtonian fluid through a permeable tube: Application to the proximal renal tubule","volume":"36","author":"Marshall","year":"1974","journal-title":"Bull. Math. Biophys."},{"issue":"1","key":"10.1016\/j.engappai.2026.114800_b28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.5890\/DNC.2023.03.002","article-title":"Mathematical model of fluid flow in a channel with reabsorption at permeable walls","volume":"12","author":"Merugu","year":"2023","journal-title":"Discontinuity Nonlinearity Complex."},{"key":"10.1016\/j.engappai.2026.114800_b29","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1142\/S0219519407002303","article-title":"Role of slip velocity in blood flow through stenosed arteries: A non-Newtonian model","volume":"7","author":"Misra","year":"2007","journal-title":"J. Mech. Med. Biology"},{"key":"10.1016\/j.engappai.2026.114800_b30","first-page":"431","article-title":"Blood flow in capillary under starling hypothesis","volume":"149","author":"Moustafa","year":"2004","journal-title":"Appl. Math. Comput."},{"issue":"4","key":"10.1016\/j.engappai.2026.114800_b31","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1615\/SpecialTopicsRevPorousMedia.v3.i4.30","article-title":"Flow through nonuniform channel with permeable wall and slip effect","volume":"3","author":"Muthu","year":"2012","journal-title":"Spec. Top. Rev. Porous Media"},{"issue":"2","key":"10.1016\/j.engappai.2026.114800_b32","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1615\/InterJFluidMechRes.v43.i2.40","article-title":"Flow in a channel with an overlapping constriction and permeability","volume":"43","author":"Muthu","year":"2016","journal-title":"Int. J. Fluid Mech. Res."},{"key":"10.1016\/j.engappai.2026.114800_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113696","article-title":"Artificial neural network analysis of magnetohydrodynamics hybrid nanofluid flow over a convectively heated vertical cone in presence of chemical reaction effects","volume":"167","author":"Nandi","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114800_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.138743","article-title":"Modeling and performance optimization of non-Newtonian hybrid nanofluid solar HVAC systems with magnetic effects using neural networks","author":"Nasir","year":"2025","journal-title":"Energy"},{"issue":"4","key":"10.1016\/j.engappai.2026.114800_b35","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1143\/JJAP.9.345","article-title":"A theoretical study of the flow of blood in a capillary with permeable wall","volume":"9","author":"Oka","year":"1970","journal-title":"Japan. J. Appl. Phys."},{"key":"10.1016\/j.engappai.2026.114800_b36","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0022-5193(74)90161-1","article-title":"A hydrodynamical model of a permeable tubule","volume":"44","author":"Palatt","year":"1974","journal-title":"J. Theoret. Biol."},{"issue":"23","key":"10.1016\/j.engappai.2026.114800_b37","doi-asserted-by":"crossref","first-page":"19261","DOI":"10.1007\/s10973-025-14823-3","article-title":"Artificial neural network modeling of heat transfer in bioconvective Oldroyd-B nanofluids with nonlinear radiation and activation energy","volume":"150","author":"Razzaq","year":"2025","journal-title":"J. Therm. Anal. Calorim."},{"key":"10.1016\/j.engappai.2026.114800_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.icheatmasstransfer.2025.109947","article-title":"Advanced computational intelligence approach for heat transfer and entropy generation in non-Newtonian third grade magnetohydrodynamics peristaltic transport through asymmetric channels","volume":"169","author":"Rehman","year":"2025","journal-title":"Int. Commun. Heat Mass Transfer"},{"issue":"6","key":"10.1016\/j.engappai.2026.114800_b39","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1140\/epjb\/s10051-025-00974-7","article-title":"Sensitivity exploration of micropolar fluid through porous permeable walls using ANN","volume":"98","author":"Renupriya","year":"2025","journal-title":"Eur. Phys. J. B"},{"issue":"2","key":"10.1016\/j.engappai.2026.114800_b40","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/sapm197150293","article-title":"On the boundary condition at the surface of a porous medium","volume":"50","author":"Saffman","year":"1971","journal-title":"Stud. Math."},{"key":"10.1016\/j.engappai.2026.114800_b41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0026-2862(76)90072-8","article-title":"A mathematical analysis of fluid movement across capillary walls","volume":"11","author":"Salathe","year":"1976","journal-title":"Microvasc. Res."},{"issue":"2","key":"10.1016\/j.engappai.2026.114800_b42","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s40710-015-0076-4","article-title":"Artificial neural network (ANN) for evaluating permeability decline in permeable reactive barrier (PRB)","volume":"2","author":"Santisukkasaem","year":"2015","journal-title":"Environ. Process."},{"key":"10.1016\/j.engappai.2026.114800_b43","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/BF00042765","article-title":"Slip at uniformly porous boundary: Effect on fluid flow and mass transfer","volume":"26","author":"Shankararaman","year":"1992","journal-title":"J. Engrg. Math."},{"key":"10.1016\/j.engappai.2026.114800_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108119","article-title":"An analysis of effect of higher order endothermic\/exothermic chemical reaction on magnetized casson hybrid nanofluid flow using fuzzy triangular number","volume":"133","author":"Shanmugapriya","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"3","key":"10.1016\/j.engappai.2026.114800_b45","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10483-016-2032-6","article-title":"Hydrodynamics of viscous fluid through porous slit with linear absorption","volume":"37","author":"Siddiqui","year":"2016","journal-title":"Appl. Math. Mech."},{"key":"10.1016\/j.engappai.2026.114800_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2020.113103","article-title":"Surrogate permeability modelling of low-permeable rocks using convolutional neural networks","volume":"366","author":"Tian","year":"2020","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"issue":"4","key":"10.1016\/j.engappai.2026.114800_b47","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1007\/s00366-020-01012-z","article-title":"Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods","volume":"37","author":"Tian","year":"2021","journal-title":"Eng. Comput."},{"key":"10.1016\/j.engappai.2026.114800_b48","first-page":"719","article-title":"Laminar pipe flow with injection and suction through a porous wall","volume":"78","author":"Yuan","year":"1956","journal-title":"Trans. ASME"},{"key":"10.1016\/j.engappai.2026.114800_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2024.115600","article-title":"Analysis of nonlinear complex heat transfer MHD flow of jeffrey nanofluid over an exponentially stretching sheet via three phase artificial intelligence and machine learning techniques","volume":"189","author":"Zeeshan","year":"2024","journal-title":"Chaos Solitons Fractals"},{"key":"10.1016\/j.engappai.2026.114800_b50","series-title":"Nanomaterials and Nanoliquids: Applications in Energy and Environment","first-page":"281","article-title":"Sensitivity analysis and numerical investigation of hybrid nanofluid in contracting and expanding channel with mhd and thermal radiation effects","author":"Zeeshan","year":"2023"},{"issue":"1","key":"10.1016\/j.engappai.2026.114800_b51","doi-asserted-by":"crossref","first-page":"21457","DOI":"10.1038\/s41598-022-24468-z","article-title":"Integrating computational fluid dynamic, artificial intelligence techniques, and pore network modeling to predict relative permeability of gas condensate","volume":"12","author":"Zeinedini","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.114800_b52","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.applthermaleng.2016.01.063","article-title":"Simulation and prediction of MHD dissipative nanofluid flow on a permeable stretching surface using artificial neural network","volume":"99","author":"Ziaei-Rad","year":"2016","journal-title":"Appl. Therm. Eng."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010821?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010821?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T20:32:43Z","timestamp":1778099563000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626010821"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":52,"alternative-id":["S0952197626010821"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114800","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial neural network-assisted optimization of slippery boundaries on the fluid transport in permeable renal tubules","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114800","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114800"}}