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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:

Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A “conscious-lab” development
Saeed Chehreh Chelgani, Hamid Nasiri, Mehdi Alidokht
International Journal of Mining Science and Technology (2021) Vol. 31, Iss. 6, pp. 1135-1144
Open Access | Times Cited: 67

Showing 1-25 of 67 citing articles:

Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
Hamid Nasiri, Sharif Hasani
Radiography (2022) Vol. 28, Iss. 3, pp. 732-738
Open Access | Times Cited: 88
Prediction of landslide displacement with dynamic features using intelligent approaches
Yonggang Zhang, Jun Tang, Yung‐Ming Cheng, et al.
International Journal of Mining Science and Technology (2022) Vol. 32, Iss. 3, pp. 539-549
Open Access | Times Cited: 85
A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images
Hamid Nasiri, Seyed Ali Alavi
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-11
Open Access | Times Cited: 75
Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods
Mohammad Reza Abbasniya, Sayed Ali Sheikholeslamzadeh, Hamid Nasiri, et al.
Computers & Electrical Engineering (2022) Vol. 103, pp. 108382-108382
Closed Access | Times Cited: 72
Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets
Jyotirmayee Behera, Ajit Kumar Pasayat, Harekrushna Behera, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 120, pp. 105843-105843
Closed Access | Times Cited: 65
Modeling industrial hydrocyclone operational variables by SHAP-CatBoost - A “conscious lab” approach
Saeed Chehreh Chelgani, Hamid Nasiri, A. Tohry, et al.
Powder Technology (2023) Vol. 420, pp. 118416-118416
Open Access | Times Cited: 44
Integrating prior knowledge to build transformer models
Pei Jiang, Takashi Obi, Yoshikazu Nakajima
International Journal of Information Technology (2024) Vol. 16, Iss. 3, pp. 1279-1292
Open Access | Times Cited: 23
Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach
Rasoul Fatahi, Hamid Nasiri, Ehsan Dadfar, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 46
Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction
Zhenkun Liu, Ping Jiang, Koen W. De Bock, et al.
Technological Forecasting and Social Change (2023) Vol. 198, pp. 122945-122945
Closed Access | Times Cited: 35
Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation
Deliang Sun, D C Chen, Jialan Zhang, et al.
Land (2023) Vol. 12, Iss. 5, pp. 1018-1018
Open Access | Times Cited: 33
Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods
Alireza Farzipour, Roya Elmi, Hamid Nasiri
Diagnostics (2023) Vol. 13, Iss. 14, pp. 2391-2391
Open Access | Times Cited: 23
Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence
Hamid Nasiri, Arman Homafar, Saeed Chehreh Chelgani
Results in Geophysical Sciences (2021) Vol. 8, pp. 100034-100034
Open Access | Times Cited: 43
Modeling operational cement rotary kiln variables with explainable artificial intelligence methods – a “conscious lab” development
Rasoul Fatahi, Hamid Nasiri, Arman Homafar, et al.
Particulate Science And Technology (2022) Vol. 41, Iss. 5, pp. 715-724
Open Access | Times Cited: 27
Prediction of Flyrock Distance in Surface Mining Using a Novel Hybrid Model of Harris Hawks Optimization with Multi-strategies-based Support Vector Regression
Chuanqi Li, Jian Zhou, Kun Du, et al.
Natural Resources Research (2023) Vol. 32, Iss. 6, pp. 2995-3023
Closed Access | Times Cited: 17
Insights into the interaction of polycarboxylate with coal and kaolinite and their application in flotation
Pengcheng Wen, Xiaomin Ma, Yuping Fan, et al.
Fuel (2023) Vol. 340, pp. 127481-127481
Closed Access | Times Cited: 16
Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions
Mohammad Azadi, Mahmood Matin
Frattura ed Integrità Strutturale (2024) Vol. 18, Iss. 68, pp. 357-370
Open Access | Times Cited: 6
Explainable artificial intelligence modeling of internal arc in a medium voltage switchgear based on different CFD simulations
Mahmood Matin, A. Dehghanian, Mohammad Dastranj, et al.
Heliyon (2024) Vol. 10, Iss. 8, pp. e29594-e29594
Open Access | Times Cited: 6
CatBoost-SHAP for modeling industrial operational flotation variables – A “conscious lab” approach
Saeed Chehreh Chelgani, Arman Homafar, Hamid Nasiri, et al.
Minerals Engineering (2024) Vol. 213, pp. 108754-108754
Open Access | Times Cited: 5
Anomaly characterization for the condition monitoring of rotating shafts exploiting data fusion and explainable convolutional neural networks
Marc Parziale, Yip Fun Yeung, Kamal Youcef‐Toumi, et al.
Structural Health Monitoring (2025)
Closed Access
Spatiotemporal Evolution of Construction Land and Its Driving Mechanism in the Yangtze River Delta Region: A Perspective of Diverse Development Orientation
Qianwen Cheng, Manchun Li, Feixue Li, et al.
Chinese Geographical Science (2025) Vol. 35, Iss. 2, pp. 343-357
Closed Access
Modeling the working pressure of a cement vertical roller mill using SHAP-XGBoost: A “conscious lab of grinding principle” approach
Rasoul Fatahi, Hadi Abdollahi, Mohammad Noaparast, et al.
Powder Technology (2025), pp. 120923-120923
Closed Access
Explainable machine learning rapid approach to evaluate coal ash content based on X-ray fluorescence
Zhiping Wen, Hangtao Liu, Maiqiang Zhou, et al.
Fuel (2022) Vol. 332, pp. 125991-125991
Closed Access | Times Cited: 27
Modeling coking coal indexes by SHAP-XGBoost: Explainable artificial intelligence method
Arman Homafar, Hamid Nasiri, Saeed Chehreh Chelgani
Fuel Communications (2022) Vol. 13, pp. 100078-100078
Closed Access | Times Cited: 24
Diagnosis of Parkinson’s disease based on voice signals using SHAP and hard voting ensemble method
Paria Ghaheri, Hamid Nasiri, Ahmadreza Shateri, et al.
Computer Methods in Biomechanics & Biomedical Engineering (2023) Vol. 27, Iss. 13, pp. 1858-1874
Open Access | Times Cited: 14
Utilizing DEM and interpretable ML algorithms to examine particle size distribution's role in small-strain shear modulus of gap-graded granular mixtures
Xingyang Liu, Jiaqi Yang, Degao Zou, et al.
Construction and Building Materials (2024) Vol. 428, pp. 136232-136232
Closed Access | Times Cited: 4
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