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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:

Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods
Víctor Rodríguez‐Galiano, Juan Antonio Luque-Espinar, Mario Chica‐Olmo, et al.
The Science of The Total Environment (2017) Vol. 624, pp. 661-672
Open Access | Times Cited: 244

Showing 1-25 of 244 citing articles:

Binary dragonfly optimization for feature selection using time-varying transfer functions
Majdi Mafarja, Ibrahim Aljarah, Ali Asghar Heidari, et al.
Knowledge-Based Systems (2018) Vol. 161, pp. 185-204
Open Access | Times Cited: 363
Machine learning assisted materials design and discovery for rechargeable batteries
Yue Liu, Biru Guo, Xinxin Zou, et al.
Energy storage materials (2020) Vol. 31, pp. 434-450
Closed Access | Times Cited: 325
Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
Lukas Knoll, Lutz Breuer, Martin Bach
The Science of The Total Environment (2019) Vol. 668, pp. 1317-1327
Closed Access | Times Cited: 208
Machine Learning in Environmental Research: Common Pitfalls and Best Practices
Jun‐Jie Zhu, Meiqi Yang, Zhiyong Jason Ren
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17671-17689
Closed Access | Times Cited: 203
Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods
Omid Rahmati, Bahram Choubin, Abolhasan Fathabadi, et al.
The Science of The Total Environment (2019) Vol. 688, pp. 855-866
Open Access | Times Cited: 193
Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques
Omar Abuodeh, Jamal A. Abdalla, Rami A. Hawileh
Applied Soft Computing (2020) Vol. 95, pp. 106552-106552
Closed Access | Times Cited: 164
Application of machine learning in groundwater quality modeling - A comprehensive review
Ryan Haggerty, Jianxin Sun, Hongfeng Yu, et al.
Water Research (2023) Vol. 233, pp. 119745-119745
Open Access | Times Cited: 130
Machine learning aided design of perovskite oxide materials for photocatalytic water splitting
Qiuling Tao, Tian Lu, Sheng Ye, et al.
Journal of Energy Chemistry (2021) Vol. 60, pp. 351-359
Closed Access | Times Cited: 101
Application of novel data-mining technique based nitrate concentration susceptibility prediction approach for coastal aquifers in India
Subodh Chandra Pal, Dipankar Ruidas, Asish Saha, et al.
Journal of Cleaner Production (2022) Vol. 346, pp. 131205-131205
Closed Access | Times Cited: 76
Data quantity governance for machine learning in materials science
Yue Liu, Zhengwei Yang, Xinxin Zou, et al.
National Science Review (2023) Vol. 10, Iss. 7
Open Access | Times Cited: 68
A Machine Learning Method for Classification of Cervical Cancer
Jesse Jeremiah Tanimu, Mohamed Hamada, Mohammed Hassan, et al.
Electronics (2022) Vol. 11, Iss. 3, pp. 463-463
Open Access | Times Cited: 66
Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China
Xizhi Nong, Laifei Cheng, Lihua Chen, et al.
Ecological Indicators (2023) Vol. 146, pp. 109845-109845
Open Access | Times Cited: 59
Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
Farzin Kazemi, Torkan Shafighfard, Doo‐Yeol Yoo
Archives of Computational Methods in Engineering (2024) Vol. 31, Iss. 4, pp. 2049-2078
Closed Access | Times Cited: 43
Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions
Arslan Yousaf, Vahid Kayvanfar, Annamaria Mazzoni, et al.
Frontiers in Sustainable Food Systems (2023) Vol. 6
Open Access | Times Cited: 40
Application of machine learning in perovskite materials and devices: A review
Ming Chen, Zhenhua Yin, Zhicheng Shan, et al.
Journal of Energy Chemistry (2024) Vol. 94, pp. 254-272
Closed Access | Times Cited: 13
Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making
Saleh Seyedzadeh, Farzad Pour Rahimian, Stephen Oliver, et al.
Applied Energy (2020) Vol. 279, pp. 115908-115908
Open Access | Times Cited: 128
Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques
Omar Abuodeh, Jamal A. Abdalla, Rami A. Hawileh
Composite Structures (2019) Vol. 234, pp. 111698-111698
Closed Access | Times Cited: 119
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
Leyi Wei, Wenjia He, Adeel Malik, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 107
An improved support vector machine-based diabetic readmission prediction
Shaoze Cui, Dujuan Wang, Yanzhang Wang, et al.
Computer Methods and Programs in Biomedicine (2018) Vol. 166, pp. 123-135
Closed Access | Times Cited: 98
Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties
Yue Liu, Junming Wu, Maxim Avdeev, et al.
Advanced Theory and Simulations (2020) Vol. 3, Iss. 2
Open Access | Times Cited: 97
Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques
Wenjing Li, Huaiyang Fang, Guangxiong Qin, et al.
The Science of The Total Environment (2020) Vol. 731, pp. 139099-139099
Closed Access | Times Cited: 85
Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction
Sujan Ghimire, Ravinesh C. Deo, Nawin Raj, et al.
Energies (2019) Vol. 12, Iss. 12, pp. 2407-2407
Open Access | Times Cited: 84
Data driven model improved by multi-objective optimisation for prediction of building energy loads
Saleh Seyedzadeh, Farzad Pour Rahimian, Stephen Oliver, et al.
Automation in Construction (2020) Vol. 116, pp. 103188-103188
Open Access | Times Cited: 77
A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification
Lin Sun, Xianglin Kong, Jiucheng Xu, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 75
Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning
Lukas Knoll, Lutz Breuer, Martin Bach
Environmental Research Letters (2020) Vol. 15, Iss. 6, pp. 064004-064004
Open Access | Times Cited: 74
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