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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:

Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids
Zhongwei Zhang, Mohammed Al‐Bahrani, Behrooz Ruhani, et al.
Chemical Engineering Journal (2023) Vol. 471, pp. 144362-144362
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making
Tao Zhang, Anahita Manafi Khajeh Pasha, S. Mohammad Sajadi, et al.
Chemical Engineering Journal (2024) Vol. 485, pp. 150059-150059
Closed Access | Times Cited: 22
Combining artificial intelligence and computational fluid dynamics for optimal design of laterally perforated finned heat sinks
Seyyed Amirreza Abdollahi, Ali Basem, As’ad Alizadeh, et al.
Results in Engineering (2024) Vol. 21, pp. 102002-102002
Open Access | Times Cited: 16
Using different machine learning algorithms to predict the rheological behavior of oil SAE40-based nano-lubricant in the presence of MWCNT and MgO nanoparticles
Mohammadreza Baghoolizadeh, Navid Nasajpour-Esfahani, Mostafa Pirmoradian, et al.
Tribology International (2023) Vol. 187, pp. 108759-108759
Closed Access | Times Cited: 24
A novel insight into the design of perforated-finned heat sinks based on a hybrid procedure: Computational fluid dynamics, machine learning, multi-objective optimization, and multi-criteria decision-making
Seyyed Amirreza Abdollahi, A.H. Aljassar E. Al-Enezi, As’ad Alizadeh, et al.
International Communications in Heat and Mass Transfer (2024) Vol. 155, pp. 107535-107535
Closed Access | Times Cited: 11
Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models
Mishal Alsehli, Ali Basem, Dheyaa J. Jasim, et al.
Fuel (2024) Vol. 374, pp. 132431-132431
Closed Access | Times Cited: 10
Enhancing Solar Energy Conversion Efficiency: Thermophysical Property Predicting of MXene/Graphene Hybrid Nanofluids via Bayesian-Optimized Artificial Neural Networks
Dheyaa J. Jasim, Husam Rajab, As’ad Alizadeh, et al.
Results in Engineering (2024) Vol. 24, pp. 102858-102858
Open Access | Times Cited: 10
Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs
Tao Hai, Ali Basem, As’ad Alizadeh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Research and application of a Model selection forecasting system for wind speed and theoretical power generation in wind farms based on classification and wind conversion
Xiaojia Huang, Chen Wang, Shenghui Zhang
Energy (2024) Vol. 293, pp. 130606-130606
Closed Access | Times Cited: 5
Maximizing Thermal Performance of Heat Pipe Heat Exchangers for Industrial Applications Using Silver Nanofluids
R. Sethuraman, Thambidurai Muthuvelan, Sivasubramanian Mahadevan, et al.
International Journal of Thermophysics (2024) Vol. 45, Iss. 4
Closed Access | Times Cited: 5
Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems
Tao Hai, Ali Basem, As’ad Alizadeh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Harnessing meta-heuristic, Bayesian, and search-based techniques in optimizing machine learning models for improved energy storage with microencapsulated PCMs
Lotfi Ben Said, Ali Basem, Abbas J. Sultan, et al.
International Communications in Heat and Mass Transfer (2025) Vol. 162, pp. 108537-108537
Closed Access
Accurate prediction of the rheological behavior of MWCNT-Al2O3/water-ethylene glycol nanofluid with metaheuristic-optimized machine learning models
Yi Ru, Ali B.M. Ali, Karwan Hussein Qader, et al.
International Journal of Thermal Sciences (2025) Vol. 211, pp. 109691-109691
Closed Access
Synergizing Neural Networks with Multi-Objective Thermal Exchange Optimization and PROMETHEE Decision-Making to Improve PCM-based Photovoltaic Thermal Systems
Li Yongxin, Ali Basem, As’ad Alizadeh, et al.
Case Studies in Thermal Engineering (2025), pp. 105851-105851
Open Access
Machine Learning-Driven Approach for Reducing Tool Wear in Die-Sinking Electrical Discharge Machining
Can Çoğun, Ece Aylı
Arabian Journal for Science and Engineering (2025)
Open Access
Enhanced Prediction of Heating Value of Municipal Solid Waste Using hybrid neuro-fuzzy model and Decision Tree-Based Feature Importance Assessment
Oluwatobi Adeleke, Obafemi O. Olatunji, Tien‐Chien Jen, et al.
Green Energy and Resources (2025), pp. 100119-100119
Open Access
SC-FSM: a new hybrid framework based on subtractive clustering and fuzzy similarity measures for imbalanced data classification
Hua Ren, Shuying Zhai, Xiaowu Wang
Signal Image and Video Processing (2025) Vol. 19, Iss. 5
Closed Access
Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption
Dilek Duranoğlu, Esat Sinan Altın, İlknur Küçük
Heliyon (2024) Vol. 10, Iss. 3, pp. e25813-e25813
Open Access | Times Cited: 4
Optimization and Prediction of Biogas Yield from Pretreated Ulva Intestinalis Linnaeus Applying Statistical-Based Regression Approach and Machine Learning Algorithms.
Uyiosa Osagie Aigbe, Kingsley Eghonghon Ukhurebor, Otolorin Adelaja Osibote, et al.
Renewable Energy (2024) Vol. 235, pp. 121347-121347
Closed Access | Times Cited: 4
Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach
Ahmad Akbarifar, Adel Maghsoudpour, Fatemeh Mohammadian, et al.
AIP Advances (2024) Vol. 14, Iss. 6
Open Access | Times Cited: 3
Hybrid ANFIS-ant colony optimization model for prediction of carbamazepine degradation using electro-Fenton process catalyzed by Fe@Fe2O3 nanowire from aqueous solution
Farzaneh Mohammadi, Somayeh Rahimi, Mohammad Mehdi Amin, et al.
Results in Engineering (2024) Vol. 23, pp. 102447-102447
Open Access | Times Cited: 3
Prediction of nanofluid thermal conductivity and viscosity with machine learning and molecular dynamics
Freddy Ajila, Saravanan Manokaran, Kanimozhi Ramaswamy, et al.
Thermal Science (2024) Vol. 28, Iss. 1 Part B, pp. 717-729
Open Access | Times Cited: 2
On the evaluation of mono-nanofluids’ density using a radial basis function neural network optimized by evolutionary algorithms
Omid Deymi, Farzaneh Rezaei, Saeid Atashrouz, et al.
Thermal Science and Engineering Progress (2024) Vol. 53, pp. 102750-102750
Closed Access | Times Cited: 2
Optimizing the energy values of solid biofuel through acidic pre-treatment: An evolutionary-based neuro-fuzzy modelling and feature importance analysis
Oluwatobi Adeleke, Abayomi Bamisaye, Kayode Adesina Adegoke, et al.
Fuel (2024) Vol. 380, pp. 133182-133182
Open Access | Times Cited: 2
Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems
Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, et al.
Energy Conversion and Management X (2024), pp. 100835-100835
Open Access | Times Cited: 2
Predicting slope failure with intelligent hybrid modeling of ANFIS with GA and PSO
Jayanti Prabha Bharti, Pijush Samui
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 4539-4555
Closed Access | Times Cited: 2
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