Leonardo Rocha

2.2k total citations
139 papers, 1.3k citations indexed

About

Leonardo Rocha is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Leonardo Rocha has authored 139 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Artificial Intelligence, 60 papers in Information Systems and 24 papers in Management Science and Operations Research. Recurrent topics in Leonardo Rocha's work include Text and Document Classification Technologies (34 papers), Recommender Systems and Techniques (31 papers) and Topic Modeling (27 papers). Leonardo Rocha is often cited by papers focused on Text and Document Classification Technologies (34 papers), Recommender Systems and Techniques (31 papers) and Topic Modeling (27 papers). Leonardo Rocha collaborates with scholars based in Brazil, United States and Portugal. Leonardo Rocha's co-authors include Marcos André Gonçalves, Fernando Mourão, Thiago Salles, Felipe Viegas, Adriano C. M. Pereira, Wagner Meira, Thierson Couto Rosa, Sérgio Canuto, Renato Ferreira and Christian Gomes and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Leonardo Rocha

111 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Leonardo Rocha Brazil 21 761 468 154 137 119 139 1.3k
Yue Wang China 22 1.1k 1.4× 336 0.7× 186 1.2× 152 1.1× 118 1.0× 156 1.8k
Enrique Costa‐Montenegro Spain 15 399 0.5× 420 0.9× 174 1.1× 217 1.6× 59 0.5× 56 991
Jie Yang Netherlands 19 1.0k 1.3× 711 1.5× 246 1.6× 217 1.6× 144 1.2× 134 1.8k
Juan F. Huete Spain 16 631 0.8× 599 1.3× 186 1.2× 173 1.3× 228 1.9× 79 1.3k
Mi Zhang China 11 458 0.6× 621 1.3× 179 1.2× 213 1.6× 240 2.0× 48 1.1k
Gisele L. Pappa Brazil 22 700 0.9× 232 0.5× 78 0.5× 108 0.8× 133 1.1× 132 1.5k
İsmail Hakkı Toroslu Türkiye 18 315 0.4× 307 0.7× 141 0.9× 156 1.1× 208 1.7× 86 1.1k
Yudian Zheng China 17 874 1.1× 285 0.6× 128 0.8× 171 1.2× 368 3.1× 29 1.4k
Xiang Ao China 23 1.5k 1.9× 664 1.4× 292 1.9× 152 1.1× 144 1.2× 93 2.0k

Countries citing papers authored by Leonardo Rocha

Since Specialization
Citations

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

Fields of papers citing papers by Leonardo Rocha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonardo Rocha

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Rocha. A scholar is included among the top collaborators of Leonardo Rocha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Leonardo Rocha. Leonardo Rocha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rocha, Leonardo, et al.. (2025). Enhanced LLM-supported instructions for medication use through retrieval-augmented generation. Computers in Biology and Medicine. 198(Pt A). 111135–111135.
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Vassio, Luca, et al.. (2025). Tracing the 2024 U.S. election debate on Telegram with LLMs and graph analysis. Social Network Analysis and Mining. 15(1).
5.
Reis, Zilma Silveira Nogueira, et al.. (2025). Instruções de Uso de Medicamentos Suportadas por RAG em Grandes Modelos de Linguagem. 23. 143–149.
6.
Melo, Pedro O. S. Vaz de, Berthier Ribeiro‐Neto, Leonardo Rocha, et al.. (2024). On Representation Learning-based Methods for Effective, Efficient, and Scalable Code Retrieval. Neurocomputing. 600. 128172–128172. 2 indexed citations
7.
Pagano, A., et al.. (2024). Explaining the Hardest Errors of Contextual Embedding Based Classifiers. 419–434. 1 indexed citations
8.
Magalhães, Ana, et al.. (2024). Prevalence of subclinical infectious agents in a blood donor population tested on every donation. Journal of Small Animal Practice. 65(3). 176–180. 1 indexed citations
9.
Viegas, Felipe, et al.. (2024). Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics. Computational Linguistics. 51(3). 843–883. 1 indexed citations
10.
Reis, Zilma Silveira Nogueira, et al.. (2024). Evaluating Large Language Model–Supported Instructions for Medication Use: First Steps Toward a Comprehensive Model. PubMed. 2(4). 632–644. 4 indexed citations
11.
Rocha, Leonardo, et al.. (2024). Análise Comparativa de Métodos de Undersampling em Classificação Automática de Texto Baseada em Transformers. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 22(1). 1–10. 1 indexed citations
12.
Belém, Fabiano, et al.. (2023). On the class separability of contextual embeddings representations – or “The classifier does not matter when the (text) representation is so good!”. Information Processing & Management. 60(4). 103336–103336. 18 indexed citations
13.
Pereira, Adriano C. M., et al.. (2023). A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation Systems. 193–197. 1 indexed citations
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Pereira, Adriano C. M., et al.. (2022). Multi-Armed Bandits in Recommendation Systems: A survey of the state-of-the-art and future directions. Expert Systems with Applications. 197. 116669–116669. 44 indexed citations
16.
Dias, Diego Roberto Colombo, et al.. (2022). Supervised Classification of Motor-Rehabilitation Body Movements with RGB Cameras and Pose Tracking Data. SHILAP Revista de lepidopterología. 13(1). 221–231. 3 indexed citations
17.
Vieira, Rafael P., Leonardo Rocha, Letícia R. Teixeira, et al.. (2010). Benzaldeído Semicarbazona: Um Perfil de Atividades Candidato a Fármaco que Alia Simplicidade Estrutural a um Amplo Perfil de Atividades. Revista Virtual de Química. 2(1). 2–9. 1 indexed citations
18.
Salles, Thiago, Leonardo Rocha, Fernando Mourão, et al.. (2010). Automatic Document Classification Temporally Robust. Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais). 1(2). 199–212. 9 indexed citations
19.
Rocha, Leonardo, et al.. (2010). Partial discharge signal denoising with spatially adaptive wavelet thresholding and support vector machines. Electric Power Systems Research. 81(2). 644–659. 57 indexed citations
20.
Salles, Thiago, Leonardo Rocha, Gisele L. Pappa, et al.. (2009). Classificação Automática de Documentos Robusta Temporalmente.. 106–119. 1 indexed citations

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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