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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 the Diameter of Biodegradable Electrospun Nanofiber Membranes: An Integrated Framework of Taguchi Design and Machine Learning
Md. Nahid Pervez, Wan Sieng Yeo, Monira Rahman Mishu, et al.
Journal of Polymers and the Environment (2023) Vol. 31, Iss. 9, pp. 4080-4096
Closed Access | Times Cited: 14

Showing 14 citing articles:

Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach
Md. Nahid Pervez, Wan Sieng Yeo, Mst. Monira Rahman Mishu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 27
Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies
Stefano Cairone, Shadi W. Hasan, Kwang‐Ho Choo, et al.
Journal of Water Process Engineering (2024) Vol. 63, pp. 105486-105486
Open Access | Times Cited: 11
Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives
Stefano Cairone, Shadi W. Hasan, Kwang‐Ho Choo, et al.
The Science of The Total Environment (2024) Vol. 944, pp. 173999-173999
Open Access | Times Cited: 8
Data-driven prediction of electrospun nanofiber diameter using machine learning: A comprehensive study and web-based tool development
Somboon Sukpancharoen, Thossaporn Wijakmatee, Tossapon Katongtung, et al.
Results in Engineering (2024) Vol. 24, pp. 102826-102826
Open Access | Times Cited: 6
Electrospinning Technology, Machine Learning, and Control Approaches: A Review
Arya Shabani, Gorkem Anil Al, Nael Berri, et al.
Advanced Engineering Materials (2025)
Open Access
An In Situ Characterisation Method for 3-D Electrospun Foams
Kyriakos Almpanidis, Chloe Jayne Howard, Vlad Stolojan
Nanomaterials (2025) Vol. 15, Iss. 5, pp. 339-339
Open Access
A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities
Mohammadreza Rostami, Maliheh Gharibshahian, Mehrnaz Mostafavi, et al.
Frontiers in Bioengineering and Biotechnology (2025) Vol. 13
Open Access
Data-Driven Machine Learning Approach for Modeling the Production and Predicting the Characteristics of Aligned Electrospun Nanofibers
Francisco Javier López-Flores, Jorge Andrés Ornelas-Guillén, Alejandra Pérez-Nava, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 22, pp. 9904-9913
Closed Access | Times Cited: 4
A comprehensive overview of the applications of kernel functions and data-driven models in regression and classification tasks in the context of software sensors
Joyce Chen Yen Ngu, Wan Sieng Yeo, Teck Fu Thien, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111975-111975
Open Access | Times Cited: 4
Introduction of LSSVR for the Prediction of the Yellowness Index
Wan Sieng Yeo, Agus Saptoro
International Journal of Computing and Digital Systems (2024) Vol. 15, Iss. 1, pp. 81-90
Open Access | Times Cited: 3
The optimization of whiteness of polyester fabric treated with nanoparticles of 2,2′-(vinylenedi-p-phenylene)bis-benzoxazole (OB-1) by the Taguchi method
Yingjie Cai, Le Li, Tianjie Wang, et al.
Colloids and Surfaces A Physicochemical and Engineering Aspects (2023) Vol. 676, pp. 132320-132320
Open Access | Times Cited: 8
The least-square support vector regression model for the dyes and heavy metal ions removal prediction
Wan Sieng Yeo, Shaula Japarin
Chemical Engineering Communications (2024) Vol. 211, Iss. 7, pp. 986-999
Open Access | Times Cited: 1
A COMPARATIVE STUDY OF DIFFERENT ARTIFICIAL INTELLIGENCE MODELS AND RESPONSE SURFACE METHODOLOGY FOR HEPTACHLOR REMOVAL USING FE/CU NANOPARTICLES
Wan Sieng Yeo
ASEAN Engineering Journal (2023) Vol. 13, Iss. 4, pp. 157-163
Open Access | Times Cited: 2
Machine learning to predict morphology, topography and mechanical properties of sustainable gelatin-based electrospun scaffolds
Elisa Roldán, Neil D. Reeves, Glen Cooper, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Harnessing interpretable and ensemble machine learning techniques for precision fabrication of aligned micro-fibers
Imtiaz Qavi, George Z. Tan
Manufacturing Letters (2024) Vol. 41, pp. 364-374
Closed Access
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