Roberto Henriques

63 papers receiving 982 citations

Roberto Henriques's Hit Papers

Generative AI for Customizable Learning Experiences 2024 · 103 citations
1030+1Years since publication255075100

Peers

Roberto Henriques
Comparison fields: 5 of 142
  • Health Informatics 32
  • Computer Science Applications 77
  • Accounting 121
  • Artificial Intelligence 318
  • Health Information Management 40
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Claudia Perlich United States
David Mauricio Peru
Bo Dong China
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Dries F. Benoit Belgium
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Citations per year

Countries citing papers authored by Roberto Henriques

Since Specialization
Citations

This map shows the geographic impact of Roberto Henriques'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 Roberto Henriques with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Henriques more than expected).

Fields of papers citing papers by Roberto Henriques

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roberto Henriques. 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 Roberto Henriques. The network helps show where Roberto Henriques may publish in the future.

Co-authors

The 25 scholars most cited alongside Roberto Henriques, 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 Roberto Henriques Line = papers co-authored together Roberto Henriques links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017228
2
Generative AI for Customizable Learning Experiences
Hit paper breakdown →
2024103
3 201772
4 201352
5 201846
6 202338
7 202034
8 201833
9 201131
10 201729
11 201726
12 200825
13 202220
14 202120
15 201820
16 202417
17 201817
18 201816
19 202112
20 201511

About Roberto Henriques

Roberto Henriques is a scholar working on Artificial Intelligence, Computer Science Applications, Education, Signal Processing and Economics and Econometrics, having authored 70 papers that have together received 1.0k indexed citations. Recurring topics across this work include Online Learning and Analytics (11 papers), Data Management and Algorithms (6 papers), Advanced Clustering Algorithms Research (4 papers), Data Mining Algorithms and Applications (4 papers), E-Learning and Knowledge Management (4 papers), Geographic Information Systems Studies (4 papers), Forecasting Techniques and Applications (3 papers) and Education during COVID-19 pandemic (3 papers). The work is most often cited by research in Health Informatics (32 citations), Computer Science Applications (77 citations), Accounting (121 citations), Artificial Intelligence (318 citations) and Health Information Management (40 citations). Roberto Henriques has collaborated with scholars based in Portugal, Brazil and Czechia. Frequent co-authors include Petr Hájek, Mauro Castelli, Vladimir Trajkovik, Victor Lobo, Fernando Bação, Ana Cristina Costa, Jorge Mateu, Leonardo Vanneschi, João Guerreiro and Sérgio Moro. Their work appears in journals such as Sustainability, Emerging Science Journal, Technological Forecasting and Social Change, Complexity and PLoS ONE.

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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