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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:

Computational intelligence approach for modeling hydrogen production: a review
Sina Ardabili, Bahman Najafi, Shahaboddin Shamshirband, et al.
Engineering Applications of Computational Fluid Mechanics (2018) Vol. 12, Iss. 1, pp. 438-458
Open Access | Times Cited: 201

Showing 1-25 of 201 citing articles:

Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Rohit Nishant, Mike Kennedy, Jacqueline Corbett
International Journal of Information Management (2020) Vol. 53, pp. 102104-102104
Closed Access | Times Cited: 706
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Amir Mosavi, Mohsen Salimi, Sina Ardabili, et al.
Energies (2019) Vol. 12, Iss. 7, pp. 1301-1301
Open Access | Times Cited: 425
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Sheraz Aslam, Herodotos Herodotou, Syed Muhammad Mohsin, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 144, pp. 110992-110992
Closed Access | Times Cited: 411
A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources
Shahab S. Band, Timon Rabczuk, Kwok‐wing Chau
IEEE Access (2019) Vol. 7, pp. 164650-164666
Open Access | Times Cited: 278
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Ümit Çavuş Büyükşahin, Şeyda Ertekin
Neurocomputing (2019) Vol. 361, pp. 151-163
Open Access | Times Cited: 264
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
Jian Zhou, Yingui Qiu, Shuangli Zhu, et al.
Engineering Applications of Artificial Intelligence (2020) Vol. 97, pp. 104015-104015
Closed Access | Times Cited: 261
An efficient optimization approach for designing machine learning models based on genetic algorithm
Khader M. Hamdia, Xiaoying Zhuang, Timon Rabczuk
Neural Computing and Applications (2020) Vol. 33, Iss. 6, pp. 1923-1933
Open Access | Times Cited: 223
A current perspective on the accuracy of incoming solar energy forecasting
Robert Blaga, Andreea Săbăduş, Nicoleta Stefu, et al.
Progress in Energy and Combustion Science (2018) Vol. 70, pp. 119-144
Closed Access | Times Cited: 217
Progress and prospects of hydrogen production: Opportunities and challenges
Bing Zhang, Suixin Zhang, Rui Yao, et al.
Journal of Electronic Science and Technology (2021) Vol. 19, Iss. 2, pp. 100080-100080
Open Access | Times Cited: 200
Prediction of cement-based mortars compressive strength using machine learning techniques
Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, et al.
Neural Computing and Applications (2021) Vol. 33, Iss. 19, pp. 13089-13121
Closed Access | Times Cited: 188
Artificial intelligence and machine learning in energy systems: A bibliographic perspective
Ashkan Entezari, Alireza Aslani, Rahim Zahedi, et al.
Energy Strategy Reviews (2022) Vol. 45, pp. 101017-101017
Open Access | Times Cited: 185
Experimental and numerical analysis of a nanofluidic thermosyphon heat exchanger
Mahdi Ramezanizadeh, Mohammad Alhuyi Nazari, Mohammad Hossein Ahmadi, et al.
Engineering Applications of Computational Fluid Mechanics (2018) Vol. 13, Iss. 1, pp. 40-47
Open Access | Times Cited: 171
Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks
Md. Mijanur Rahman, Mohammad Shakeri, Tiong Sieh Kiong, et al.
Sustainability (2021) Vol. 13, Iss. 4, pp. 2393-2393
Open Access | Times Cited: 130
Applications of artificial intelligence‐based modeling for bioenergy systems: A review
Mochen Liao, Yuan Yao
GCB Bioenergy (2021) Vol. 13, Iss. 5, pp. 774-802
Open Access | Times Cited: 110
Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches
Mohamed Mahmoud Samy, Rabia Emhamed Al Mamlook, Heba I. Elkhouly, et al.
Sustainable Cities and Society (2022) Vol. 84, pp. 104015-104015
Closed Access | Times Cited: 90
Progress of artificial neural networks applications in hydrogen production
Mohammad Ali Abdelkareem, Bassel Soudan, Mohamed S. Mahmoud, et al.
Process Safety and Environmental Protection (2022) Vol. 182, pp. 66-86
Closed Access | Times Cited: 83
A perspective on increasing the efficiency of proton exchange membrane water electrolyzers– a review
Ashkan Makhsoos, Mohsen Kandidayeni, Bruno G. Pollet, et al.
International Journal of Hydrogen Energy (2023) Vol. 48, Iss. 41, pp. 15341-15370
Open Access | Times Cited: 83
Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)
Nader Karballaeezadeh, Danial Mohammadzadeh S., Shahaboddin Shamshirband, et al.
Engineering Applications of Computational Fluid Mechanics (2019) Vol. 13, Iss. 1, pp. 188-198
Open Access | Times Cited: 137
EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization
Gaurav Dhiman, Krishna Kant Singh, Adam Słowik, et al.
International Journal of Machine Learning and Cybernetics (2020) Vol. 12, Iss. 2, pp. 571-596
Closed Access | Times Cited: 126
Numerical simulation of nanofluid flow inside a root canal
Mohammad Ghalandari, Elaheh Mirzadeh Koohshahi, Fatemeh Mohamadian, et al.
Engineering Applications of Computational Fluid Mechanics (2019) Vol. 13, Iss. 1, pp. 254-264
Open Access | Times Cited: 116
Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids
Alireza Baghban, Ali Jalali, Mojtaba Shafiee, et al.
Engineering Applications of Computational Fluid Mechanics (2018) Vol. 13, Iss. 1, pp. 26-39
Open Access | Times Cited: 115
Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning
Amir Mosavi, Shahaboddin Shamshirband, Ely Salwana, et al.
Engineering Applications of Computational Fluid Mechanics (2019) Vol. 13, Iss. 1, pp. 482-492
Open Access | Times Cited: 114
Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments
Mohammad Ghalandari, Alireza Ziamolki, Amir Mosavi, et al.
Engineering Applications of Computational Fluid Mechanics (2019) Vol. 13, Iss. 1, pp. 892-904
Open Access | Times Cited: 113
BU-Net: Brain Tumor Segmentation Using Modified U-Net Architecture
Mobeen Ur Rehman, SeungBin Cho, Jeehong Kim, et al.
Electronics (2020) Vol. 9, Iss. 12, pp. 2203-2203
Open Access | Times Cited: 103
A hybrid DNN–LSTM model for detecting phishing URLs
Alper Özcan, Cagatay Catal, Emrah Dönmez, et al.
Neural Computing and Applications (2021) Vol. 35, Iss. 7, pp. 4957-4973
Open Access | Times Cited: 81
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