Luis Quesada

813 citations
52 papers · 579 · h-index 8

Impact in

Papers in

Luis Quesada

45 papers receiving 545 citations

Peers

Luis Quesada
Comparison fields: 5 of 92
  • Human-Computer Interaction 162
  • Artificial Intelligence 233
  • Information Systems and Management 37
  • Software 20
  • Computer Vision and Pattern Recognition 95
Replace Munéo Kitajima with:
Munéo Kitajima Japan
Anton Eliëns Netherlands
Željko Obrenović Netherlands
Bruno Dumas Belgium
Yann Riche United States
Nestor Garay-Vitoria Spain
Paolo Busetta Italy
Rieks op den Akker Netherlands
Jason Wu United States
Juan-Miguel López-Gil Spain
Luis Quesada relative to Munéo Kitajima Japan Munéo Kitajima's profile →
Citations per field
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Munéo Kitajima · 1×
Citations per year

Countries citing papers authored by Luis Quesada

Since Specialization
Citations

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

Fields of papers citing papers by Luis Quesada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017212
2 201978
3 201966
4 201753
5 202135
6 201514
7 200513
8 20179
9 20198
10 20227
11 20176
12
Using dominators for solving constrained path problems
19945
13 20125
14 20184
15 20164
16 20114
17 20154
18 20194
19 20164
20 20123

About Luis Quesada

Luis Quesada is a scholar working on Human-Computer Interaction, Software, Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications, having authored 52 papers that have together received 579 indexed citations. Recurring topics across this work include Model-Driven Software Engineering Techniques (9 papers), Natural Language Processing Techniques (6 papers), AI in Service Interactions (6 papers), Hand Gesture Recognition Systems (6 papers), Speech and dialogue systems (4 papers), Context-Aware Activity Recognition Systems (4 papers), Gait Recognition and Analysis (4 papers) and Innovative Human-Technology Interaction (4 papers). The work is most often cited by research in Human-Computer Interaction (162 citations), Artificial Intelligence (233 citations), Information Systems and Management (37 citations), Software (20 citations) and Computer Vision and Pattern Recognition (95 citations). Luis Quesada has collaborated with scholars based in Costa Rica, Spain and Germany. Frequent co-authors include Luis A. Guerrero, Gustavo López, Ignacio Díaz Blanco, Fernando Berzal, Yves Deville, Adrián Lara, Peter Van Roy, Juan-Carlos Cubero, Peter Van Roy and Jose Andres Guevara-Coto. Their work appears in journals such as Journal of Ambient Intelligence and Humanized Computing, International Journal of Neural Systems, Lecture notes in computer science, International Journal of Software Engineering and Knowledge Engineering and Advances in Human-Computer Interaction.

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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