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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:

Modeling of particle sizes for industrial HPGR products by a unique explainable AI tool- A “Conscious Lab” development
Saeed Chehreh Chelgani, Hamid Nasiri, A. Tohry
Advanced Powder Technology (2021) Vol. 32, Iss. 11, pp. 4141-4148
Open Access | Times Cited: 44

Showing 1-25 of 44 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
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
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
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
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
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
Impact of HPGR operational pressing force and material moisture on energy consumption and crushing product fineness in high-pressure grinding processes
D. Saramak, Katarzyna Leśniak
Energy (2024) Vol. 302, pp. 131908-131908
Closed Access | Times Cited: 7
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
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
Modeling Nonlinear Deformation in Magnetic Polyelectrolyte Hydrogels: A Hybrid FEM-Machine Learning Framework
Hadi Mehdipour, H. Darijani, Mahmood Matin, et al.
Results in Engineering (2025), pp. 104503-104503
Open 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
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
DEM analysis of wear evolution and its effect on the operation of a lab-scale HPGR mill
Yudong Zou, Chengwei Zhang, Dazhao Gou, et al.
Minerals Engineering (2023) Vol. 204, pp. 108401-108401
Open Access | Times Cited: 15
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
Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM
Hamid Nasiri, Ghazal Kheyroddin, Morteza Dorrigiv, et al.
2022 IEEE World AI IoT Congress (AIIoT) (2022), pp. 201-206
Open Access | Times Cited: 19
PD-ADSV: An automated diagnosing system using voice signals and hard voting ensemble method for Parkinson’s disease
Paria Ghaheri, Ahmadreza Shateri, Hamid Nasiri
Software Impacts (2023) Vol. 16, pp. 100504-100504
Open Access | Times Cited: 11
Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures
Zhiwei Zhang, Yuyan Zhang, Yintang Wen, et al.
Complex & Intelligent Systems (2023) Vol. 9, Iss. 5, pp. 5881-5892
Open Access | Times Cited: 10
Online prediction of pressing iron ore concentrates in an industrial HPGR. Part 1: Modeling approach
Túlio M. Campos, Horácio A. Petit, Ricardo O. Freitas, et al.
Minerals Engineering (2023) Vol. 201, pp. 108206-108206
Closed Access | Times Cited: 10
Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry
Manuel Saldaña, Edelmira D. Gálvez, Alessandro Navarra, et al.
Materials (2023) Vol. 16, Iss. 8, pp. 3220-3220
Open Access | Times Cited: 9
Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM
Mobina Ezzoddin, Hamid Nasiri, Morteza Dorrigiv
(2022)
Open Access | Times Cited: 15
COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19
Sharif Hasani, Hamid Nasiri
Software Impacts (2021) Vol. 11, pp. 100210-100210
Open Access | Times Cited: 19
Experimental analysis of combustion characteristics of corn starch dust clouds under the action of unilateral obstacles and machine learning modeling based on PSO-XGBoost
Jinglin Zhang, Xiumei Cao, Chang Li, et al.
Advanced Powder Technology (2024) Vol. 35, Iss. 11, pp. 104641-104641
Closed Access | Times Cited: 2
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