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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:

Taxonomy and Survey of Interpretable Machine Learning Method
Saikat Das, Namita Agarwal, Deepak Venugopal, et al.
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2020), pp. 670-677
Closed Access | Times Cited: 15

Showing 15 citing articles:

A survey of visual analytics for Explainable Artificial Intelligence methods
Gülsüm Alicioğlu, Bo Sun
Computers & Graphics (2021) Vol. 102, pp. 502-520
Closed Access | Times Cited: 155
A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions
Talal A. A. Abdullah, Mohd Soperi Mohd Zahid, Waleed Ali
Symmetry (2021) Vol. 13, Iss. 12, pp. 2439-2439
Open Access | Times Cited: 66
Machine learning applications on IoT data in manufacturing operations and their interpretability implications: A systematic literature review
Anna Presciuttini, Alessandra Cantini, Federica Costa, et al.
Journal of Manufacturing Systems (2024) Vol. 74, pp. 477-486
Open Access | Times Cited: 10
XAI-ADS: An Explainable Artificial Intelligence Framework for Enhancing Anomaly Detection in Autonomous Driving Systems
Sazid Nazat, Lingxi Li, Mustafa Abdallah
IEEE Access (2024) Vol. 12, pp. 48583-48607
Open Access | Times Cited: 8
Interpretability of Machine Learning: Recent Advances and Future Prospects
Lei Gao, Ling Guan
IEEE Multimedia (2023) Vol. 30, Iss. 4, pp. 105-118
Open Access | Times Cited: 20
Approach to provide interpretability in machine learning models for image classification
Anja Stadlhofer, Vitaliy Mezhuyev
Industrial Artificial Intelligence (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 7
Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China
Longyue Liang, Bo Liu, Zhi Su, et al.
Journal of Forecasting (2024) Vol. 43, Iss. 7, pp. 2540-2571
Closed Access | Times Cited: 1
Machine Learning application lifecycle augmented with explanation and security
Saikat Das, Sajjan G. Shiva
2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (2021), pp. 0171-0177
Closed Access | Times Cited: 6
DDoS Explainer using Interpretable Machine Learning
Saikat Das, Namita Agarwal, Sajjan G. Shiva
2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (2021), pp. 0001-0007
Closed Access | Times Cited: 5
Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability
Waqas Ali, Mariam Sabir
Emerging technologies and engineering journal. (2024) Vol. 1, Iss. 1, pp. 15-25
Open Access
A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends
Wasswa Shafik, Ahmad Fathan Hidayatullah, Kassim Kalinaki, et al.
Research Square (Research Square) (2024)
Open Access
Abstracting General Syntax for XAI after Decomposing Explanation Sub-Components
Stephen Wormald, Matheus Kunzler Maldaner, Kristian D. O'Connor, et al.
Research Square (Research Square) (2024)
Open Access
Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation
Muhammad Asif, Chengxi Yao, Zitu Zuo, et al.
Journal of Industrial and Engineering Chemistry (2024) Vol. 144, pp. 32-47
Closed Access
Methods of Explainable Artificial Intelligence (XAI), Trustworthy Artificial Intelligence (TAI) and Interpretable Machine Learning (IML) in Renewable Energy
Betül Ersöz, Şeref Sağıroğlu, Halil İbrahim Bülbül
International Journal of Smart grid (2023), Iss. V6i4
Open Access | Times Cited: 1
Requirements Elicitation and Stakeholder Communications for Explainable Machine Learning Systems: State of the Art
Saikat Das, Mazharul Hossain, Sajjan G. Shiva
(2023), pp. 561-566
Closed Access | Times Cited: 1
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