Diego Mesquita

455 total citations
24 papers, 227 citations indexed

About

Diego Mesquita is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Diego Mesquita has authored 24 papers receiving a total of 227 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 4 papers in Information Systems. Recurrent topics in Diego Mesquita's work include Machine Learning and ELM (9 papers), Face and Expression Recognition (7 papers) and Neural Networks and Applications (6 papers). Diego Mesquita is often cited by papers focused on Machine Learning and ELM (9 papers), Face and Expression Recognition (7 papers) and Neural Networks and Applications (6 papers). Diego Mesquita collaborates with scholars based in Brazil, Finland and Sweden. Diego Mesquita's co-authors include João P. P. Gomes, Amauri H. Souza, Juvêncio S. Nobre, Leonardo Ramos Rodrigues, Roberto Kawakami Harrop Galvão, Lincoln S. Rocha, Ajalmar R. Rocha Neto, Samuel Kaski, Francesco Corona and César Mattos and has published in prestigious journals such as Neurocomputing, Applied Soft Computing and Electronics Letters.

In The Last Decade

Diego Mesquita

20 papers receiving 223 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diego Mesquita Brazil 8 124 41 35 22 21 24 227
Alexander Borisov United States 6 123 1.0× 48 1.2× 28 0.8× 16 0.7× 12 0.6× 12 278
Gerhard Goos Germany 5 102 0.8× 25 0.6× 23 0.7× 12 0.5× 6 0.3× 8 227
Manal A. Ismail Egypt 8 87 0.7× 36 0.9× 39 1.1× 12 0.5× 9 0.4× 30 278
Md. Nasim Adnan Australia 9 106 0.9× 28 0.7× 49 1.4× 14 0.6× 28 1.3× 23 248
Serafín Moral‐García Spain 9 136 1.1× 20 0.5× 40 1.1× 15 0.7× 11 0.5× 22 264
Charlie Obimbo Canada 9 120 1.0× 73 1.8× 67 1.9× 31 1.4× 29 1.4× 32 294
Xiajiong Shen China 7 102 0.8× 34 0.8× 51 1.5× 12 0.5× 7 0.3× 49 271
Tianyu Xu China 8 169 1.4× 85 2.1× 25 0.7× 13 0.6× 15 0.7× 15 294

Countries citing papers authored by Diego Mesquita

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Mesquita, Diego, et al.. (2025). Differentially Private Selection Using Smooth Sensitivity. 3969–3987.
2.
Mesquita, Diego, et al.. (2024). Towards automatic labeling of exception handling bugs: A case study of 10 years bug-fixing in Apache Hadoop. Empirical Software Engineering. 29(4).
3.
Mesquita, Diego, et al.. (2022). Bayesian Analysis of Bug-Fixing Time using Report Data. KTH Publication Database DiVA (KTH Royal Institute of Technology). 57–68. 3 indexed citations
4.
Mattos, César, et al.. (2022). Bayesian Multilateration. IEEE Signal Processing Letters. 29. 962–966. 3 indexed citations
5.
Mesquita, Diego, et al.. (2021). Classificando Graus de Pterígio Utilizando Aprendizado de Máquina. 12. 2 indexed citations
6.
7.
Mesquita, Diego, Amauri H. Souza, & Samuel Kaski. (2020). Rethinking pooling in graph neural networks. Aaltodoc (Aalto University). 33. 2220–2231. 8 indexed citations
8.
Mesquita, Diego, Paul Blomstedt, & Samuel Kaski. (2019). Embarrassingly Parallel MCMC using Deep Invertible Transformations.. Research Explorer (The University of Manchester). 1244–1252. 1 indexed citations
9.
Mesquita, Diego, João P. P. Gomes, & Leonardo Ramos Rodrigues. (2019). Artificial Neural Networks with Random Weights for Incomplete Datasets. Neural Processing Letters. 50(3). 2345–2372. 12 indexed citations
10.
Mesquita, Diego, et al.. (2019). A sparse linear regression model for incomplete datasets. Pattern Analysis and Applications. 23(3). 1293–1303. 3 indexed citations
11.
Mesquita, Diego, João P. P. Gomes, Francesco Corona, Amauri H. Souza, & Juvêncio S. Nobre. (2019). Gaussian kernels for incomplete data. Applied Soft Computing. 77. 356–365. 17 indexed citations
12.
Kärkkäinen, Tommi, et al.. (2017). A Robust Minimal Learning Machine based on the M-Estimator.. Jyväskylä University Digital Archive (University of Jyväskylä). 3 indexed citations
13.
Mesquita, Diego, João P. P. Gomes, Amauri H. Souza, & Juvêncio S. Nobre. (2017). Euclidean distance estimation in incomplete datasets. Neurocomputing. 248. 11–18. 82 indexed citations
14.
Mesquita, Diego, et al.. (2017). Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm. Applied Soft Computing. 70. 1135–1145. 25 indexed citations
15.
Mesquita, Diego, et al.. (2016). Using Robust Extreme Learning Machines to Predict Cotton Yarn Strength and Hairiness.. The European Symposium on Artificial Neural Networks. 1 indexed citations
16.
Mesquita, Diego, João P. P. Gomes, & Leonardo Ramos Rodrigues. (2016). K-means for Datasets with Missing Attributes: Building Soft Constraints with Observed and Imputed Values.. The European Symposium on Artificial Neural Networks. 2 indexed citations
17.
Gomes, João P. P., et al.. (2016). Co-MLM: A SSL Algorithm Based on the Minimal Learning Machine. 9. 97–102. 3 indexed citations
18.
Mesquita, Diego, Lincoln S. Rocha, João P. P. Gomes, & Ajalmar R. Rocha Neto. (2016). Classification with reject option for software defect prediction. Applied Soft Computing. 49. 1085–1093. 27 indexed citations
19.
Mesquita, Diego, et al.. (2016). Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator. 4. 433–437. 3 indexed citations
20.
Mesquita, Diego, João P. P. Gomes, Leonardo Ramos Rodrigues, & Roberto Kawakami Harrop Galvão. (2015). Pruning Extreme Learning Machines Using the Successive Projections Algorithm. IEEE Latin America Transactions. 13(12). 3974–3979. 11 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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