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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:

Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data
Bonelwa Sidumo, Energy Sonono, Isaac Takaidza
Annals of Data Science (2023) Vol. 11, Iss. 3, pp. 803-817
Open Access | Times Cited: 15

Showing 15 citing articles:

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques
Vanessa Steindorf, H B, Nico Stollenwerk, et al.
Parasites & Vectors (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 1
Surveillance of high‐yield processes using deep learning models
M. Wadah Sabri Ibrahim, Chunxia Zhang, Tahir Mahmood
Quality and Reliability Engineering International (2024) Vol. 40, Iss. 8, pp. 4365-4393
Open Access | Times Cited: 5
Spatial Analysis of Informal Recyclable Station System for Integration to Formal Solid Waste Management System and Circular Economy–Proposing Integrated Methodology of GIS, Remote Sensing, and Statistical Learning
Cuong Le Dinh, Takeshi Fujiwara, Song Toan Pham Phu
Environmental Quality Management (2025) Vol. 34, Iss. 3
Closed Access
Causal Effect of Count Treatment on Ordinal Outcome Using Generalized Propensity Score: Application to Number of Antenatal Care and Age Specific Childhood Vaccination
Ashagrie Sharew Iyassu, Haile Mekonnen Fenta, Zelalem G. Dessie, et al.
Journal of Epidemiology and Global Health (2025) Vol. 15, Iss. 1
Open Access
Understanding Forest Fragmentation Dynamics and Identifying Drivers for Forest Cover Loss Using Random Forest Models to Develop Effective Forest Management Strategies in North-East India
Soumik Mahapatra, Bishal Kumar Majhi, Mriganka Shekhar Sarkar, et al.
Results in Engineering (2025), pp. 104640-104640
Open Access
Modeling the number of new cases of childhood type 1 diabetes using Poisson regression and machine learning methods; a case study in Saudi Arabia
Ahood Alazwari, Laleh Tafakori, A.C. Johnstone, et al.
PLoS ONE (2025) Vol. 20, Iss. 4, pp. e0321480-e0321480
Open Access
Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms
Sunghae Jun
Electronics (2024) Vol. 13, Iss. 4, pp. 798-798
Open Access | Times Cited: 2
Quantifying impacts of recreation on elk (Cervus canadensis) using novel modeling approaches
Michael Procko, Samantha G. Winder, Spencer A. Wood, et al.
Ecosphere (2024) Vol. 15, Iss. 6
Closed Access | Times Cited: 2
Patent Keyword Analysis Using Bayesian Zero-Inflated Model and Text Mining
Sunghae Jun
Stats (2024) Vol. 7, Iss. 3, pp. 827-841
Open Access | Times Cited: 2
Modelling and spatial prediction of earthworms ecological-categories distribution reveal their habitat and environmental preferences
Gabriel Salako, Andrey S. Zaitsev, Bibiana Betancur‐Corredor, et al.
Ecological Indicators (2024) Vol. 169, pp. 112832-112832
Closed Access | Times Cited: 2
Zero-Inflated Text Data Analysis using Generative Adversarial Networks and Statistical Modeling
Sunghae Jun
Computers (2023) Vol. 12, Iss. 12, pp. 258-258
Open Access | Times Cited: 5
A novel model for count data: zero-inflated Probit Bell model with applications
Essoham Ali, Kim-Hung Pho
Communications in Statistics - Simulation and Computation (2024), pp. 1-19
Closed Access
Technology Keyword Analysis Using Graphical Causal Models
Sunghae Jun
Electronics (2024) Vol. 13, Iss. 18, pp. 3670-3670
Open Access
Dynamic ensemble-based machine learning models for predicting pest populations
Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, et al.
Frontiers in Applied Mathematics and Statistics (2024) Vol. 10
Open Access
Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset
Gadir Alomair
PLoS ONE (2024) Vol. 19, Iss. 12, pp. e0314975-e0314975
Open Access
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