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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:

Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters
Chonghyo Joo, Hyundo Park, Jongkoo Lim, et al.
International Journal of Intelligent Systems (2021) Vol. 37, Iss. 6, pp. 3625-3653
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Corporate finance risk prediction based on LightGBM
Di-ni Wang, Lang Li, Zhao Da
Information Sciences (2022) Vol. 602, pp. 259-268
Closed Access | Times Cited: 125
Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques
Elizabeth Champa-Bujaico, Ana M. Díez‐Pascual, Alba Lomas Redondo, et al.
Composites Part B Engineering (2023) Vol. 269, pp. 111099-111099
Open Access | Times Cited: 48
Multi-objective optimization of CO2 emission and thermal efficiency for on-site steam methane reforming hydrogen production process using machine learning
Seokyoung Hong, Jaewon Lee, Hyungtae Cho, et al.
Journal of Cleaner Production (2022) Vol. 359, pp. 132133-132133
Closed Access | Times Cited: 63
Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations
Chonghyo Joo, Hyundo Park, Jongkoo Lim, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106873-106873
Closed Access | Times Cited: 33
Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane
Jiwon Roh, Hyundo Park, Hyukwon Kwon, et al.
Applied Catalysis B Environment and Energy (2023) Vol. 343, pp. 123454-123454
Open Access | Times Cited: 24
Deep neural network-based optimal selection and blending ratio of waste seashells as an alternative to high-grade limestone depletion for SOX capture and utilization
Jonghun Lim, Soohwan Jeong, Junghwan Kim
Chemical Engineering Journal (2021) Vol. 431, pp. 133244-133244
Open Access | Times Cited: 44
Multiobjective Optimization of CO2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm
Hyungtae Cho, Hyukwon Kwon, Jonghun Lim, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 7, pp. 2841-2851
Closed Access | Times Cited: 6
Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana
Eric Asamoah, G.B.M. Heuvelink, Ikram Chairi, et al.
Heliyon (2024) Vol. 10, Iss. 17, pp. e37065-e37065
Open Access | Times Cited: 6
Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data
Chonghyo Joo, Hyundo Park, Hyukwon Kwon, et al.
Polymers (2022) Vol. 14, Iss. 17, pp. 3500-3500
Open Access | Times Cited: 21
A new tool to predict the advanced oxidation process efficiency: Using machine learning methods to predict the degradation of organic pollutants with Fe-carbon catalyst as a sample
Shu-Zhe Zhang, Shuo Chen, Hong Jiang
Chemical Engineering Science (2023) Vol. 280, pp. 119069-119069
Closed Access | Times Cited: 12
A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Soo-Mi Choi
Journal of Environmental Management (2023) Vol. 345, pp. 118790-118790
Closed Access | Times Cited: 11
Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane
Jiwon Roh, Seunghyeon Oh, Donggyun Lee, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 10, pp. 4121-4131
Closed Access | Times Cited: 4
A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential
Hyungtae Cho, Jaewon Lee, Jonghun Lim, et al.
Process Safety and Environmental Protection (2024) Vol. 186, pp. 715-727
Closed Access | Times Cited: 4
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm
Chunhui Xie, Haoke Qiu, Lu Liu, et al.
SmartMat (2025) Vol. 6, Iss. 1
Open Access
A novel graph-based missing values imputation method for industrial lubricant data
Soohwan Jeong, Chonghyo Joo, Jongkoo Lim, et al.
Computers in Industry (2023) Vol. 150, pp. 103937-103937
Closed Access | Times Cited: 10
Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data
Hyundo Park, Chonghyo Joo, Jongkoo Lim, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108864-108864
Closed Access | Times Cited: 2
A framework for energy optimization of distillation process using machine learning‐based predictive model
Hyundo Park, Hyukwon Kwon, Hyungtae Cho, et al.
Energy Science & Engineering (2022) Vol. 10, Iss. 6, pp. 1913-1924
Open Access | Times Cited: 12
Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition
Jeongdong Kim, Chonghyo Joo, Minsu Kim, et al.
Expert Systems with Applications (2022) Vol. 216, pp. 119464-119464
Closed Access | Times Cited: 12
A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence
Sungchul Choi, Mokh Afifuddin, Wonchul Seo
IEEE Access (2022) Vol. 10, pp. 19284-19300
Open Access | Times Cited: 11
Visual analytics of an interpretable prediction model for the glass transition temperature of fluoroelastomers
Jiling Liu, Yadong Wu, Zhoujun Lin, et al.
Materials Today Communications (2024) Vol. 40, pp. 110155-110155
Open Access | Times Cited: 2
pyAPEP: An all-in-one software package for the automated preparation of adsorption process simulations
Seongbin Ga, Nahyeon An, Hyungtae Cho, et al.
Computer Physics Communications (2023) Vol. 291, pp. 108830-108830
Open Access | Times Cited: 4
Multi-objective optimization of clean utilization for zinc leaching residues by rotary kiln using neural network coupled modeling
Chenmu Zhang, Zhi Zan, Yao Shi, et al.
Journal of Cleaner Production (2024) Vol. 470, pp. 143287-143287
Closed Access | Times Cited: 1
New Perspective for the Prediction of Pollutant Removal Efficiency in Constructed Wetlands: Using a Genetic Algorithm-Assisted Machine Learning Model
Shu-Zhe Zhang, Hong Jiang
ACS ES&T Water (2024) Vol. 4, Iss. 11, pp. 5053-5064
Closed Access | Times Cited: 1
Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions
Kristina Berladir, Dmytro Zhyhylii, O. P. Gaponova, et al.
Polymers (2022) Vol. 14, Iss. 10, pp. 2087-2087
Open Access | Times Cited: 7
Data-driven modeling for physical property prediction of polypropylene composites using artificial neural network and principal component analysis
J. Chonghyo, P. Hyundo, H. Seokyoung, et al.
Computer-aided chemical engineering/Computer aided chemical engineering (2022), pp. 1369-1374
Closed Access | Times Cited: 4
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