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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:

The Prediction of Spark-Ignition Engine Performance and Emissions Based on the SVR Algorithm
Yu Zhang, Qifan Wang, Xiaofei Chen, et al.
Processes (2022) Vol. 10, Iss. 2, pp. 312-312
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran
Mahsa Hajihosseinlou, Abbas Maghsoudi, Reza Ghezelbash
Expert Systems with Applications (2023) Vol. 237, pp. 121668-121668
Closed Access | Times Cited: 56
Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants
Tajrian Mollick, Galib Hashmi, Saifur Rahman Sabuj
Sustainable Energy Research (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 8
Long-term atmospheric pollutant emissions from a combined cycle gas turbine: Trend monitoring and prediction applying machine learning
David A. Wood
Fuel (2023) Vol. 343, pp. 127722-127722
Closed Access | Times Cited: 15
Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil
Tadas Žvirblis, Jacek Hunicz, Jonas Matijošius, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2023) Vol. 25, Iss. 4
Open Access | Times Cited: 15
NOx Emission Predictions in Gas Turbines Through Integrated Data-Driven Machine Learning Approaches
Kazi Ekramul Hoque, Tahiya Hossain, Md Azazul Haque, et al.
Journal of Energy Resources Technology (2024) Vol. 146, Iss. 7
Closed Access | Times Cited: 5
Monthly streamflow prediction and performance comparison of machine learning and deep learning methods
Ömer Ayana, Deniz Furkan Kanbak, Mümine Kaya Keleş, et al.
Acta Geophysica (2023) Vol. 71, Iss. 6, pp. 2905-2922
Closed Access | Times Cited: 11
A machine learning method for buckling design of internally pressurized torispherical heads considering geometric imperfection
Fang Liu, Jie Yang, Shuo Weng, et al.
Thin-Walled Structures (2023) Vol. 189, pp. 110908-110908
Closed Access | Times Cited: 11
Neural Network-Based Prediction of NH3 Leakage from SCR Systems for Diesel Engines
Qiang Zhu, Jiehui Li
International Journal of Automotive Technology (2024) Vol. 25, Iss. 1, pp. 97-106
Closed Access | Times Cited: 4
Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach
Upendra Rajak, Prem Kumar Chaurasiya, Tikendra Nath Verma, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 22, pp. 32449-32463
Open Access | Times Cited: 4
Machine learning for fast development of advanced energy materials
Bita Farhadi, Jiaxue You, Dexu Zheng, et al.
Next Materials (2023) Vol. 1, Iss. 3, pp. 100025-100025
Open Access | Times Cited: 10
Carbon Dioxide Emission Forecast: A Review of Existing Models and Future Challenges
Yaxin Tian, Xiang Ren, Keke Li, et al.
Sustainability (2025) Vol. 17, Iss. 4, pp. 1471-1471
Open Access
Machine Learning Algorithms Applied to Machine Vision and Inertial Systems
Gabriel Trujillo-Hernández, Luis Roberto Ramírez-Hernández, Jorge Alejandro Valdez-Rodríguez, et al.
IGI Global eBooks (2025), pp. 347-386
Closed Access
Machine learning assisted approximation of descriptors (CO and OH) binding energy on Cu-based bimetallic alloys
Pallavi Dandekar, Aditya Singh Ambesh, Tuhin Suvra Khan, et al.
Physical Chemistry Chemical Physics (2025)
Open Access
Comparative Analysis of Load Profile Forecasting: LSTM, SVR, and Ensemble Approaches for Singular and Cumulative Load Categories
Ahmad Fayyazbakhsh, Thomas Kienberger, Julia Vopava-Wrienz
Smart Cities (2025) Vol. 8, Iss. 2, pp. 65-65
Open Access
Machine learning-driven universal models for gasoline engine performance maps: Accuracy and generality evaluation
Shaban Alyari Shourehdeli, Ali Mirmohammadi, Hamed Gholipour
Thermal Science and Engineering Progress (2025), pp. 103593-103593
Closed Access
Optoelectronic Devices Analytics: MachineLearning-Driven Models for Predicting the Performance of a Dye-Sensitized Solar Cell
Emeka Harrison Onah, Ndanduleni Lethole, Patrick Mukumba
Electronics (2025) Vol. 14, Iss. 10, pp. 1948-1948
Open Access
Integrating Machine Learning for Predicting Internal Combustion Engine Performance and Segment-Based CO2 Emissions Across Urban and Rural Settings
Naghmeh Niroomand, Christian Bach
IEEE Access (2024) Vol. 12, pp. 66223-66236
Open Access | Times Cited: 3
The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines
Ruomiao Yang, Tianfang Xie, Zhentao Liu
Energies (2022) Vol. 15, Iss. 9, pp. 3242-3242
Open Access | Times Cited: 13
An Integration of Genetic Feature Selector, Histogram-Based Outlier Score, and Deep Learning for Wind Turbine Power Prediction
Parastou Fahim, Nima Vaezi, Amin Shahraki, et al.
Energy Sources Part A Recovery Utilization and Environmental Effects (2022) Vol. 44, Iss. 4, pp. 9342-9365
Closed Access | Times Cited: 8
Comparative Analysis of Data-Driven Models for Marine Engine In-Cylinder Pressure Prediction
Chaitanya Patil, Γεράσιμος Θεοτοκάτος
Machines (2023) Vol. 11, Iss. 10, pp. 926-926
Open Access | Times Cited: 4
The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method
Qifan Wang, Ruomiao Yang, Xiaoxia Sun, et al.
Processes (2022) Vol. 10, Iss. 4, pp. 717-717
Open Access | Times Cited: 7
Effect of fuel compositions coupled with engine configuration parameters on a gasoline engine based on sensitivity analysis
Yinlong Li, Yongqiang Han, Kechao Zhang
Energy Sources Part A Recovery Utilization and Environmental Effects (2024) Vol. 46, Iss. 1, pp. 16657-16674
Closed Access | Times Cited: 1
Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance
Bo-Jiun Huang, Hong Wei, Kun Shao, et al.
Sustainability (2024) Vol. 16, Iss. 24, pp. 11107-11107
Open Access | Times Cited: 1
Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches
Jafar Abdi, Golshan Mazloom, Fahimeh Hadavimoghaddam, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 3
Pre-research on Enhanced Heat Transfer Method for Special Vehicles at High Altitude Based on Machine Learning
Chunming Li, Xiaoxia Sun, Hongyang Gao, et al.
Chinese Journal of Mechanical Engineering (2023) Vol. 36, Iss. 1
Open Access | Times Cited: 1
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