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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:

Prediction of asphaltene stability in crude oils using machine learning algorithms
Syed Imran Ali, Shaine Mohammadali Lalji, Awan Zahoor, et al.
Chemometrics and Intelligent Laboratory Systems (2023) Vol. 235, pp. 104784-104784
Closed Access | Times Cited: 14

Showing 14 citing articles:

Synthetic oversampling with Mahalanobis distance and local information for highly imbalanced class-overlapped data
Yuanting Yan, Lei Zheng, S. G. Han, et al.
Expert Systems with Applications (2024), pp. 125422-125422
Closed Access | Times Cited: 8
Risk quantification and ranking of oil fields and wells facing asphaltene deposition problem using fuzzy TOPSIS coupled with AHP
Syed Imran Ali, Shaine Mohammadali Lalji, Saud Hashmi, et al.
Ain Shams Engineering Journal (2023) Vol. 15, Iss. 1, pp. 102289-102289
Open Access | Times Cited: 16
Analyzing machine learning algorithms in predicting Ranikot swelling at different compaction pressures in presence of carbon supported TiO2 water based mud
Faiq Azhar Abbasi, Syed Mohammad Ali Shah, Muhammad Mustafa, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Closed Access
Machine learning assisted prediction of disperse dye exhaustion on polylactic acid fiber with interpretable model
Shicheng Liu, Du Chen, Fengxuan Zhang, et al.
Dyes and Pigments (2025), pp. 112693-112693
Closed Access
Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility
Ivelina Shiskova, Dicho Stratiev, Mariana P. Tavlieva, et al.
Processes (2024) Vol. 12, Iss. 4, pp. 780-780
Open Access | Times Cited: 3
Study of the precipitation trend of asphaltenes and waxes in crude oil using computational chemistry and statistical thermodynamics methods
Edgardo Jonathan Suárez-Domínguez, Josúe Francisco Pérez-Sánchez, Hugo Herrera-Pilotzi, et al.
Results in Engineering (2023) Vol. 21, pp. 101672-101672
Open Access | Times Cited: 7
Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets
Katelyn Le, Jagoš R. Radović, Justin L. MacCallum, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 32, pp. 22563-22569
Closed Access | Times Cited: 2
Predicting Petroleum SARA Composition from Density, Sulfur Content, Flash Point, and Simulated Distillation Data Using Regression and Artificial Neural Network Techniques
Ivelina Shiskova, Dicho Stratiev, Sotir Sotirov, et al.
Processes (2024) Vol. 12, Iss. 8, pp. 1755-1755
Open Access | Times Cited: 2
Predicting the explosion limits of hydrogen-oxygen-diluent mixtures using machine learning approach
Jianhang Li, Wenkai Liang, Wenhu Han
International Journal of Hydrogen Energy (2023) Vol. 50, pp. 1306-1313
Closed Access | Times Cited: 4
Asphaltene Stability Prediction Using Hybrid Artificial Neural Network Modeling Approach
Aliyu Adebayo Sulaimon, Joshua Nsiah Turkson, Abubakar Abubakar Umar, et al.
SPE Nigeria Annual International Conference and Exhibition (2024)
Closed Access | Times Cited: 1
Local Binary Pattern and RVFL for Covid-19 Diagnosis
Mengke Wang
(2024), pp. 325-343
Closed Access
Predicting asphaltene adsorption on Fe3O4 nanoparticle using machine learning algorithms
Syed Imran Ali, Shaine Mohammadali Lalji, Usama Ahsan, et al.
Arabian Journal of Geosciences (2024) Vol. 17, Iss. 4
Closed Access
Stability Analysis of Breakwater Armor Blocks Based on Deep Learning
Pengrui Zhu, Xin Bai, Hongbiao Liu, et al.
Water (2024) Vol. 16, Iss. 12, pp. 1689-1689
Open Access
Quantitatively Detecting Camellia Oil Products Adulterated by Rice Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms
Henan Liu, Sijia Ma, Ni Liang, et al.
Foods (2024) Vol. 13, Iss. 24, pp. 4182-4182
Open Access
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