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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:

Application of logistic regression algorithm in the diagnosis of expression disorder in Parkinson's disease
Yaqi Guan
2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (2021) Vol. 16, pp. 1117-1120
Closed Access | Times Cited: 12

Showing 12 citing articles:

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms
Md Abu Sayed, Maliha Tayaba, MD Tanvir Islam, et al.
Journal of Computer Science and Technology Studies (2023) Vol. 5, Iss. 4, pp. 142-149
Open Access | Times Cited: 31
Leveraging Action Unit Derivatives for Early-Stage Parkinson's Disease Detection
Anas Filali Razzouki, Laetitia Jeancolas, Graziella Mangone, et al.
IRBM (2025), pp. 100874-100874
Open Access | Times Cited: 1
Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders
Nooshin Goudarzi, Zahra Taheri, Amir Mohammad Nezhad Salari, et al.
Reviews in the Neurosciences (2025)
Closed Access
Research on the Application of an Improved Machine Learning Algorithm Based on Weighted Grey Projection in Educational Data Mining
You Chen, Hongming Yuan
(2025), pp. 955-960
Closed Access
Parkinson’s Disease Assessment Using Dominant Voice Features
Priya Dilip Ghate, Anuradha C. Phadke
Lecture notes in networks and systems (2025), pp. 57-65
Closed Access
Voice biomarkers as prognostic indicators for Parkinson’s disease using machine learning techniques
Ifrah Naeem, Allah Ditta, Tehseen Mazhar, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A Hybrid Machine Learning Framework to Improve Parkinson’s Disease Prediction Accuracy
Ronak Bediya, R N Ravikumar, Krishnanand Mishra, et al.
(2023), pp. 33-38
Closed Access | Times Cited: 4
Analysis of Different Modality of Data to Diagnose Parkinson's Disease Using Machine Learning and Deep Learning Approaches: A Review
Sheikh Bahauddin Arnab, Md Istakiak Adnan Palash, Rakibul Islam, et al.
Expert Systems (2024)
Open Access | Times Cited: 1
Recognition of Parkinson’s Disease Using Different Machine Learning Models
Vishal Kumar, Nikhil Sinha, Aman Yadav, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 2
Privacy-Preserving Vertical Collaborative Logistic Regression without Trusted Third-Party Coordinator
Xiaopeng Yu, Wei Zhao, Dianhua Tang, et al.
Security and Communication Networks (2022) Vol. 2022, pp. 1-12
Open Access | Times Cited: 4
Early Diagnosing Parkinson's Disease Via a Deep Learning Model Based on Augmented Facial Expression Data
Yintao Zhou, Meng Pang, Wei Huang, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2024), pp. 1621-1625
Closed Access
Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models
Rhythm, Rajat Khandelwal, Prerna Badlani, et al.
(2024), pp. 1475-1479
Closed Access
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