Diego Mesquita
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Electrical and Electronic Engineering
- Global and Planetary Change
- Co-authors
- João P. P. GomesAmauri H. SouzaJuvêncio S. NobreLeonardo Ramos RodriguesRoberto Kawakami Harrop GalvãoAjalmar R. Rocha NetoLincoln S. RochaFrancesco Corona
- Topics
- Machine Learning and ELM (9 papers)Face and Expression Recognition (7 papers)Neural Networks and Applications (6 papers)
In The Last Decade
Diego Mesquita
20 papers receiving 223 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 124
- Computer Vision and Pattern Recognition 41
- Information Systems 35
- Electrical and Electronic Engineering 22
- Global and Planetary Change 21
Countries citing papers authored by Diego Mesquita
This map shows the geographic impact of Diego Mesquita'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 Diego Mesquita with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Mesquita more than expected).
Fields of papers citing papers by Diego Mesquita
This network shows the impact of papers produced by Diego Mesquita. 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 Diego Mesquita. The network helps show where Diego Mesquita may publish in the future.
Co-authorship network of co-authors of Diego Mesquita
This figure shows the co-authorship network connecting the top 25 collaborators of Diego Mesquita. A scholar is included among the top collaborators of Diego Mesquita 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 Diego Mesquita. Diego Mesquita is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 3 | |
| 5 | Classificando Graus de Pterígio Utilizando Aprendizado de Máquina | 2 |
| 6 | 0 | |
| 7 | Rethinking pooling in graph neural networks | 8 |
| 8 | 17 | |
| 9 | 12 | |
| 10 | 3 | |
| 11 | Embarrassingly Parallel MCMC using Deep Invertible Transformations. | 1 |
| 12 | 82 | |
| 13 | A Robust Minimal Learning Machine based on the M-Estimator. | 3 |
| 14 | 25 | |
| 15 | Using Robust Extreme Learning Machines to Predict Cotton Yarn Strength and Hairiness. | 1 |
| 16 | K-means for Datasets with Missing Attributes: Building Soft Constraints with Observed and Imputed Values. | 2 |
| 17 | 27 | |
| 18 | 3 | |
| 19 | 3 | |
| 20 | 11 |
About Diego Mesquita
Diego Mesquita is a scholar working on Software, Artificial Intelligence and Statistics and Probability, having authored 24 papers that have together received 227 indexed citations. Recurring topics across this work include Machine Learning and ELM (9 papers), Face and Expression Recognition (7 papers) and Neural Networks and Applications (6 papers). The work is most often cited by research in Software (17 citations), Artificial Intelligence (124 citations) and Computer Vision and Pattern Recognition (41 citations). Diego Mesquita has collaborated with scholars based in Brazil, Finland and Sweden. Frequent co-authors include João P. P. Gomes, Amauri H. Souza, Juvêncio S. Nobre, Leonardo Ramos Rodrigues, Roberto Kawakami Harrop Galvão, Ajalmar R. Rocha Neto, Lincoln S. Rocha, Francesco Corona, Samuel Kaski and César Mattos. Their work appears in journals such as Neurocomputing, Applied Soft Computing and Electronics Letters.
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.