Ronaldo C. Prati

7.5k total citations · 2 hit papers
67 papers, 4.4k citations indexed

About

Ronaldo C. Prati is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ronaldo C. Prati has authored 67 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 18 papers in Information Systems and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ronaldo C. Prati's work include Imbalanced Data Classification Techniques (16 papers), Data Mining Algorithms and Applications (13 papers) and Machine Learning and Data Classification (13 papers). Ronaldo C. Prati is often cited by papers focused on Imbalanced Data Classification Techniques (16 papers), Data Mining Algorithms and Applications (13 papers) and Machine Learning and Data Classification (13 papers). Ronaldo C. Prati collaborates with scholars based in Brazil, Spain and Finland. Ronaldo C. Prati's co-authors include Gustavo E. A. P. A. Batista, Maria Carolina Monard, Francisco Herrera, Alberto Fernández, Bartosz Krawczyk, Salvador García, Mikel Galar, Diego Furtado Silva, Gustavo Batista and Carlos Kamienski and has published in prestigious journals such as SHILAP Revista de lepidopterología, Physical Chemistry Chemical Physics and Expert Systems with Applications.

In The Last Decade

Ronaldo C. Prati

62 papers receiving 4.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A study of the behavior of several methods for balancing machine learning training data

2004 2.7k citations Gustavo E. A. P. A. Batista, Ronaldo C. Prati et al. profile →
Learning from Imbalanced Data Sets

2018 834 citations Ronaldo C. Prati, Francisco Herrera et al. profile →

Peers

Ronaldo C. Prati

AI

EEE

IS

CVPR

HIM

Victoria López Spain

Maria Carolina Monard Brazil

Jason Van Hulse United States

Amri Napolitano United States

Gustavo E. A. P. A. Batista Brazil

Michał Woźniak Poland

Bartosz Krawczyk Poland

Mikel Galar Spain

Vasile Palade United Kingdom

Victoria López Spain

Ronaldo C. Prati

2.5k ×0.9

AI

567 ×0.7

EEE

637 ×1.1

IS

381 ×1.1

CVPR

233 ×0.7

HIM

Citations per year, relative to Ronaldo C. Prati Ronaldo C. Prati (= 1×) peers Victoria López

Countries citing papers authored by Ronaldo C. Prati

Since Specialization

Citations

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

Fields of papers citing papers by Ronaldo C. Prati

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ronaldo C. Prati. 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 Ronaldo C. Prati. The network helps show where Ronaldo C. Prati may publish in the future.

Co-authorship network of co-authors of Ronaldo C. Prati

This figure shows the co-authorship network connecting the top 25 collaborators of Ronaldo C. Prati. A scholar is included among the top collaborators of Ronaldo C. Prati 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 Ronaldo C. Prati. Ronaldo C. Prati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Prati, Ronaldo C., et al.. (2025). Profiling the needs of dementia caregivers in a Brazilian cross‐sectional study. Alzheimer s & Dementia Translational Research & Clinical Interventions. 11(2).

2.

Dantas, Ramide, et al.. (2025). A review of Smart Irrigation. Biosystems Engineering. 257. 104220–104220.

3.

Prati, Ronaldo C., et al.. (2024). Feature engineering and machine learning for electrochemical detection of rabies virus in graphene-based biosensors. Microchemical Journal. 204. 111074–111074. 8 indexed citations

4.

Prati, Ronaldo C., et al.. (2024). The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling. Journal of Chemical Information and Modeling. 64(4). 1107–1111. 2 indexed citations

5.

Prati, Ronaldo C., et al.. (2023). Data-driven water need estimation for IoT-based smart irrigation: A survey. Expert Systems with Applications. 225. 120194–120194. 28 indexed citations

6.

Prati, Ronaldo C., et al.. (2023). Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training. Journal of Chemical Information and Modeling. 63(17). 5446–5456.

7.

Bianchi, Reinaldo A. C., et al.. (2022). Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture. Biosystems Engineering. 223. 142–158. 35 indexed citations

8.

Quiles, Marcos G., et al.. (2021). Correlation-Based Framework for Extraction of Insights from Quantum Chemistry Databases: Applications for Nanoclusters. Journal of Chemical Information and Modeling. 61(3). 1125–1135. 3 indexed citations

9.

Quiles, Marcos G., et al.. (2021). Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition. Journal of Chemical Information and Modeling. 61(9). 4210–4223. 7 indexed citations

10.

Prati, Ronaldo C., et al.. (2020). Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset. The Journal of Physical Chemistry A. 124(47). 9854–9866. 81 indexed citations

11.

Prati, Ronaldo C., et al.. (2019). Ab initio insights into the structural, energetic, electronic, and stability properties of mixed Ce_nZr_15−nO₃₀ nanoclusters. Physical Chemistry Chemical Physics. 21(48). 26637–26646. 5 indexed citations

12.

Luengo, Julián, et al.. (2018). A First Study on the Use of Noise Filtering to Clean the Bags in Multi-Instance Classification. 1–6. 1 indexed citations

13.

Prati, Ronaldo C., et al.. (2015). Setting Parameters for Support Vector Machines using Transfer Learning. Journal of Intelligent & Robotic Systems. 80(S1). 295–311. 8 indexed citations

14.

Prati, Ronaldo C., Gustavo Batista, & Maria Carolina Monard. (2008). Evaluating Classifiers Using ROC Curves. IEEE Latin America Transactions. 6(2). 215–222. 30 indexed citations

15.

Giusti, Rafael, et al.. (2006). A Multi-Objective Evolutionary Algorithm to Build Knowledge Classification Rules with Specific Properties. 41–41. 3 indexed citations

16.

Batista, Gustavo E. A. P. A., et al.. (2006). A Comparison of Methods for Rule Subset Selection Applied to Associative Classification.. INTELIGENCIA ARTIFICIAL. 10(32). 1 indexed citations

17.

Lee, Huei, et al.. (2006). A Simple Evaluation Model for Feature Subset Selection Algorithms.. INTELIGENCIA ARTIFICIAL. 10(32). 5 indexed citations

18.

Prati, Ronaldo C. & Peter Flach. (2004). ROCCER: A ROC convex hull rule learning algorithm. 7 indexed citations

19.

Prati, Ronaldo C., et al.. (2003). An Integrated Environment for Data Mining. 1 indexed citations

20.

Prati, Ronaldo C., José Augusto Baranauskas, & Maria Carolina Monard. (1999). Aquisição automática de conhecimento utilizando a biblioteca de aprendizado de máquina MLC. Anais. 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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