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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:

Explainable AI Methods - A Brief Overview
Andreas Holzinger, Anna Saranti, Christoph Molnar, et al.
Lecture notes in computer science (2022), pp. 13-38
Open Access | Times Cited: 232

Showing 76-100 of 232 citing articles:

Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning
Andreas Wünsch, Tanja Liesch, Nico Goldscheider
Hydrology and earth system sciences (2024) Vol. 28, Iss. 9, pp. 2167-2178
Open Access | Times Cited: 3
Theoretical and Practical Principles for Generative AI in Communities of Practice and Social Learning
Darren Cambridge, Etienne Wenger-Trayner, Per Hammer, et al.
Postdigital science and education (2024), pp. 229-239
Closed Access | Times Cited: 3
Emerging Technologies for Sustainable Agriculture: The Power of Humans and the Way Ahead
Malamati Louta, Konstantina Banti, Ioanna Karampelia
IEEE Access (2024) Vol. 12, pp. 98492-98529
Open Access | Times Cited: 3
Secure Explainable-AI Approach for Brake Faults Prediction in Heavy Transport
Muhammad Ahmad Khan, Maqbool Khan, Hussain Dawood, et al.
IEEE Access (2024) Vol. 12, pp. 114940-114950
Open Access | Times Cited: 3
Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives
Stefano Cairone, Shadi W. Hasan, Kwang‐Ho Choo, et al.
Euro-Mediterranean Journal for Environmental Integration (2024)
Open Access | Times Cited: 3
Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens
Izegbua E. Ihongbe, Shereen Fouad, Taha F. Mahmoud, et al.
PLoS ONE (2024) Vol. 19, Iss. 10, pp. e0308758-e0308758
Open Access | Times Cited: 3
Interaction of Thoughts: Towards Mediating Task Assignment in Human-AI Cooperation with a Capability-Aware Shared Mental Model
Ziyao He, Yunpeng Song, Shurui Zhou, et al.
(2023), pp. 1-18
Closed Access | Times Cited: 9
Interpretable Skin Cancer Classification based on Incremental Domain Knowledge Learning
Eman Rezk, Mohamed Eltorki, Wael El‐Dakhakhni
Journal of Healthcare Informatics Research (2023) Vol. 7, Iss. 1, pp. 59-83
Closed Access | Times Cited: 8
Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system
Andrew K. C. Wong, Pei-Yuan Zhou, A. Lee
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 7
Validity evidence supporting clinical skills assessment by artificial intelligence compared with trained clinician raters
Vilma Johnsson, Morten Søndergaard, Kulamakan Kulasegaram, et al.
Medical Education (2023) Vol. 58, Iss. 1, pp. 105-117
Open Access | Times Cited: 7
Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning
Federico Cabitza, Andrea Campagner, Lorenzo Famiglini, et al.
Lecture notes in computer science (2022), pp. 31-50
Closed Access | Times Cited: 12
Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs
Bettina Finzel, Anna Saranti, Alessa Angerschmid, et al.
KI - Künstliche Intelligenz (2022) Vol. 36, Iss. 3-4, pp. 271-285
Open Access | Times Cited: 12
Who decides what online and beyond: freedom of choice in predictive machine-learning algorithms
Simona Tiribelli
Elsevier eBooks (2024), pp. 299-321
Closed Access | Times Cited: 2
Unveiling the factors of aesthetic preferences with explainable AI
Derya Soydaner, Johan Wagemans
British Journal of Psychology (2024)
Open Access | Times Cited: 2
Question Answering Over Knowledge Graphs for Explainable Satellite Scheduling
Cheyenne Powell, Annalisa Riccardi
(2024)
Open Access | Times Cited: 2
Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective
Parniyan Sadeghi, Hanie Karimi, Atiye Lavafian, et al.
Expert Review of Clinical Immunology (2024) Vol. 20, Iss. 10, pp. 1219-1236
Closed Access | Times Cited: 2
Study on Empowering Cyber Security by Using Adaptive Machine Learning Methods
Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pawar, et al.
(2024), pp. 166-171
Closed Access | Times Cited: 2
AUTOLYCUS: Exploiting Explainable Artificial Intelligence (XAI) for Model Extraction Attacks against Interpretable Models
Abdullah Çağlar Öksüz, Anisa Halimi, Erman Ayday
Proceedings on Privacy Enhancing Technologies (2024) Vol. 2024, Iss. 4, pp. 684-699
Open Access | Times Cited: 2
The role of artificial intelligence in disease prediction: using ensemble model to predict disease mellitus
Qinyuan Du, Dongli Wang, Yimin Zhang
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 2
Understanding Student Perceptions of Artificial Intelligence as a Teammate
Rebecca Marrone, Andrew Zamecnik, Srécko Joksimovíc, et al.
Technology Knowledge and Learning (2024)
Open Access | Times Cited: 2
Beyond model interpretability: socio-structural explanations in machine learning
Andrew Smart, Atoosa Kasirzadeh
AI & Society (2024)
Open Access | Times Cited: 2
Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics
Ladislav Hodač, Kevin Karbstein, Lara M. Kösters, et al.
The Plant Journal (2024) Vol. 120, Iss. 4, pp. 1343-1357
Open Access | Times Cited: 2
CVD22: Explainable artificial intelligence determination of the relationship of troponin to D-Dimer, mortality, and CK-MB in COVID-19 patients
Kevser Kübra Kırboğa, Ecir Uğur Küçüksille, Muhammet Emin Naldan, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 233, pp. 107492-107492
Open Access | Times Cited: 6
Commentary on explainable artificial intelligence methods: SHAP and LIME
Ahmed Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 6
Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy
Sun Ju Lee, Gian-Gabriel P. Garcia, Kaitlyn K. Stanhope, et al.
American Journal of Obstetrics & Gynecology MFM (2023) Vol. 5, Iss. 10, pp. 101096-101096
Closed Access | Times Cited: 6

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