Ivan Donadello

590 citations
22 papers · 261 indexed · h-index 10

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Explainable Artificial Intelligence (XAI)
    • Topic Modeling
    • Sentiment Analysis and Opinion Mining
    • Machine Learning in Healthcare
    • Adversarial Robustness in Machine Learning

Papers in

Ivan Donadello

18 papers receiving 254 citations

Peers

Ivan Donadello
Comparison fields: 5 of 74
  • Health Informatics 28
  • Artificial Intelligence 181
  • Safety Research 15
  • Applied Psychology 9
  • Management Information Systems 15
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Citations per year

Countries citing papers authored by Ivan Donadello

Since Specialization
Citations

This map shows the geographic impact of Ivan Donadello's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ivan Donadello with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Donadello more than expected).

Fields of papers citing papers by Ivan Donadello

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivan Donadello. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ivan Donadello. The network helps show where Ivan Donadello may publish in the future.

Co-authors

The 25 scholars most cited alongside Ivan Donadello, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ivan Donadello Line = papers co-authored together Ivan Donadello links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202262
2 202039
3 202237
4 202429
5 201718
6 201611
7
Persuasive Explanation of Reasoning Inferences on Dietary Data.
201910
8 20199
9 20239
10 20169
11 20226
12 20225
13 20225
14 20184
15 20204
16 20232
17
SeXAI: Introducing Concepts into Black Boxes for Explainable Artificial Intelligence.
20201
18
Ontology Based Semantic Image Interpretation.
20151
19 20250
20 20210

About Ivan Donadello

Ivan Donadello is a scholar working on General Decision Sciences, Artificial Intelligence, Applied Psychology, Management Information Systems and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 261 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (6 papers), Topic Modeling (5 papers), Multimodal Machine Learning Applications (3 papers), Service-Oriented Architecture and Web Services (3 papers), Business Process Modeling and Analysis (3 papers), Mobile Health and mHealth Applications (2 papers), Advanced Text Analysis Techniques (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Health Informatics (28 citations), Artificial Intelligence (181 citations), Safety Research (15 citations), Applied Psychology (9 citations) and Management Information Systems (15 citations). Ivan Donadello has collaborated with scholars based in Italy, United Kingdom and France. Frequent co-authors include Mauro Dragoni, Claudio Eccher, Luciano Serafini, Erik Cambria, Artur d’Avila Garcez, Natalia Díaz-Rodríguez, A. Lamas, Siham Tabik, David Filliat and Gianni Franchi. Their work appears in journals such as Artificial Intelligence in Medicine, Business & Information Systems Engineering, Engineering Applications of Artificial Intelligence, Frontiers in Psychology and IEEE Intelligent Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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