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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A well-trained artificial neural network (ANN) using the trainlm algorithm for predicting the rheological behavior of water – Ethylene glycol/WO3 – MWCNTs nanofluid
Guangli Fan, El-Shafay A.S., S. Ali Eftekhari, et al.
International Communications in Heat and Mass Transfer (2021) Vol. 131, pp. 105857-105857
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid
Mohammadreza Baghoolizadeh, Dheyaa J. Jasim, S. Mohammad Sajadi, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e26279-e26279
Open Access | Times Cited: 17
Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling
Mojtaba Sepehrnia, Hamid Maleki, Mahsa Karimi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 40
Artificial neural network modeling of thermal characteristics of WO3-CuO (50:50)/water hybrid nanofluid with a back-propagation algorithm
Yiran Qu, Dheyaa J. Jasim, S. Mohammad Sajadi, et al.
Materials Today Communications (2024) Vol. 38, pp. 108169-108169
Closed Access | Times Cited: 8
Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models
Hulin Jin, Yong-Guk Kim, Zhiran Jin, et al.
Energy Reports (2022) Vol. 8, pp. 13979-13996
Open Access | Times Cited: 32
ANFIS and ANN models to predict heliostat tracking errors
M Sarr, Ababacar Thiam, Biram Dieng
Heliyon (2023) Vol. 9, Iss. 1, pp. e12804-e12804
Open Access | Times Cited: 13
Using different Heuristic strategies and an adaptive Neuro-Fuzzy inference system for multi-objective optimization of Hybrid Nanofluid to provide an efficient thermal behavior
Zhe Wang, Hayder Oleiwi Shami, Khudhaier. J. Kazim, et al.
Swarm and Evolutionary Computation (2024) Vol. 86, pp. 101536-101536
Closed Access | Times Cited: 5
Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms
WeiHong Jin, Ali Basem, Mohammadreza Baghoolizadeh, et al.
International Communications in Heat and Mass Transfer (2024) Vol. 155, pp. 107578-107578
Closed Access | Times Cited: 5
Using Different Evolutionary Algorithms and Artificial Neural Networks to Predict the Rheological Behavior of a New Nano-lubricant Containing Multi-walled carbon nanotube and zinc oxide Nano-powders in Oil 10W40 Base Fluid
ABDULHUSSEIN HAREEJA REFAISH, Ihab Omar, Muntadher Abed Hussein, et al.
International Journal of Thermofluids (2025), pp. 101092-101092
Open Access
Machine learning-based predictive modeling and optimization: Artificial neural network-genetic algorithm vs. response surface methodology for black soldier fly (Hermetia illucens) farm waste fermentation.
Oseweuba Valentine Okoro, DE Caevel Hippolyte, Lei Nie, et al.
Biochemical Engineering Journal (2025), pp. 109685-109685
Closed Access
Artificial neural networks assisted by DFT for boosting ligand design in ethylene tri-/tetramerization using silicon-bridged diphosphine (SBDP) ligands
Yanwen Zhang, Ruoxing Shao, Yan Jiang, et al.
Molecular Catalysis (2025) Vol. 576, pp. 114952-114952
Closed Access
Intelligent framework for dual solutions of copper oxide nanoparticles suspension in thermally varied fluid reservoirs using the Koo–Kleinstreuer–Li (KKL) Model
Muhammad Zeb, Muhammad Awais, Asif Waheed, et al.
Alexandria Engineering Journal (2025) Vol. 124, pp. 435-445
Closed Access
Statistical analysis and Neural Network Modeling of functionally graded porous nanobeams vibration in an elastic medium by considering the surface effects
Xiaofei Cheng, Sara Hakem Al-Khafaji, Mohammad Hashemian, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106313-106313
Closed Access | Times Cited: 12
A hybrid SEM-neural network method for modeling the academic satisfaction factors of architecture students
Soolmaz Aghaei, Yaser Shahbazi, Mohammadtaghi Pirbabaei, et al.
Computers and Education Artificial Intelligence (2023) Vol. 4, pp. 100122-100122
Open Access | Times Cited: 11
Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning
Yazeed AbuShanab, Wahib A. Al‐Ammari, Samer Gowid, et al.
Heliyon (2023) Vol. 9, Iss. 6, pp. e16716-e16716
Open Access | Times Cited: 11
Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50
Yuelei Zhang, Karrar A. Hammoodi, S. Mohammad Sajadi, et al.
Tribology International (2023) Vol. 191, pp. 109185-109185
Closed Access | Times Cited: 11
Neural Network Model Using Levenberg Marquardt Backpropagation Algorithm for the Prandtl Fluid Flow Over Stratified Curved Sheet
Pradeep Kumar, F. Almeida, B. Nagaraja, et al.
IEEE Access (2024) Vol. 12, pp. 102242-102260
Open Access | Times Cited: 4
Neuro-computing analysis of model-based Casson hybrid nanofluid flow via three-dimensional radiative Riga plate with irregular heat source/sink
Subhajit Panda, Surender Ontela, Prasant Kumar Pattnaik, et al.
Partial Differential Equations in Applied Mathematics (2024) Vol. 11, pp. 100906-100906
Open Access | Times Cited: 4
Predicting heat transfer Performance in transient flow of CNT nanomaterials with thermal radiation past a heated spinning sphere using an artificial neural network: A machine learning approach
S. R. Mishra, P. K. Pattnaik, Rupa Baithalu, et al.
Partial Differential Equations in Applied Mathematics (2024), pp. 100936-100936
Open Access | Times Cited: 4
Effect of particle behavior on the apparent viscosity of semi-solid metal: CFD-DEM simulation and artificial neural networks prediction
Dianqiao Geng, Dandan Yan, Wenjie Yu, et al.
Particuology (2025)
Closed Access
Using the RSM to evaluate the rheological behavior of SiO2 (60%) - MWCNT (40%)/SAE40 oil hybrid nanofluid and investigating the effect of different parameters on the viscosity
Mohammad Hemmat Esfe, Seyed Naser Hosseini Tamrabad, Hossein Hatami, et al.
Tribology International (2023) Vol. 184, pp. 108479-108479
Closed Access | Times Cited: 10
Obtaining the optimal lubrication conditions by investigating the viscosity of MWCNT (25%)-TiO2(75%)/ oil SAE40 hybrid nanofluid by response surface methodology
Davood Toghraie, Seyed Naser Hosseini Tamrabad, Soheyl Alidoust, et al.
Tribology International (2023) Vol. 186, pp. 108585-108585
Closed Access | Times Cited: 9
Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications
Nik Eirdhina Binti Nik Salimi, Suhaib Umer Ilyas, Syed Ali Ammar Taqvi, et al.
Digital Chemical Engineering (2024) Vol. 12, pp. 100170-100170
Open Access | Times Cited: 3
Research on the water quality detection method based on fluorescence spectrometry and PSO-RBF network
Yinshan Yu, Hongyun Zhang
Measurement (2023) Vol. 218, pp. 113197-113197
Closed Access | Times Cited: 8
Dynamic Viscosity Prediction for MWCNT-MgO (10%-90%) -SAE40 Oil Hybrid Nano-lubricant Using Artificial Neural Network and Multi-Dimensional Nonlinear Least Square Curve Fitting
Xiaojian Lin, Ali Basem, Mortatha Al‐Yasiri, et al.
Chinese Journal of Chemical Engineering (2024)
Closed Access | Times Cited: 2
Presenting the best correlation relationship for predicting the dynamic viscosity of CuO nanoparticles in ethylene glycol -water base fluid using response surface methodology
Mohammad Hemmat Esfe, Seyed Naser Hosseini Tamrabad, Davood Toghraie, et al.
Arabian Journal of Chemistry (2023) Vol. 17, Iss. 1, pp. 105467-105467
Open Access | Times Cited: 6
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