Gerson Zaverucha

1.1k total citations
49 papers, 403 citations indexed

About

Gerson Zaverucha is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Gerson Zaverucha has authored 49 papers receiving a total of 403 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 10 papers in Information Systems and 6 papers in Computer Networks and Communications. Recurrent topics in Gerson Zaverucha's work include Neural Networks and Applications (11 papers), Logic, Reasoning, and Knowledge (10 papers) and Bayesian Modeling and Causal Inference (9 papers). Gerson Zaverucha is often cited by papers focused on Neural Networks and Applications (11 papers), Logic, Reasoning, and Knowledge (10 papers) and Bayesian Modeling and Causal Inference (9 papers). Gerson Zaverucha collaborates with scholars based in Brazil, United Kingdom and United States. Gerson Zaverucha's co-authors include Artur d’Avila Garcez, Juliana Bernardes, Alessandra Carbone, Aline Paes, Marta Mattoso, Marcelo Teixeira, Fernanda Baião, Vı́tor Santos Costa, Catherine Vaquero and Fabio Rocha Jimenez Vieira and has published in prestigious journals such as Bioinformatics, BMC Bioinformatics and Information Sciences.

In The Last Decade

Gerson Zaverucha

45 papers receiving 372 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gerson Zaverucha Brazil	10	237	80	57	46	45	49	403
Mark Shackleton United Kingdom	11	194 0.8×	45 0.6×	77 1.4×	118 2.6×	36 0.8×	20	383
Ignacio Arnaldo United States	7	254 1.1×	44 0.6×	50 0.9×	113 2.5×	60 1.3×	14	343
Miguel Nicolau Ireland	13	484 2.0×	89 1.1×	20 0.4×	99 2.2×	48 1.1×	50	561
Gwang S. Jung United States	5	136 0.6×	52 0.7×	89 1.6×	44 1.0×	39 0.9×	11	327
Xiaodong Zhu China	7	187 0.8×	28 0.3×	61 1.1×	17 0.4×	70 1.6×	12	325
Priscila M. V. Lima Brazil	10	244 1.0×	19 0.2×	42 0.7×	60 1.3×	20 0.4×	57	378
Daokun Zhang Australia	3	343 1.4×	98 1.2×	80 1.4×	46 1.0×	13 0.3×	5	484
Mostafa Haghir Chehreghani Iran	10	178 0.8×	22 0.3×	79 1.4×	35 0.8×	50 1.1×	40	294
Huan Yee Koh Australia	8	180 0.8×	53 0.7×	13 0.2×	54 1.2×	91 2.0×	10	329

Countries citing papers authored by Gerson Zaverucha

Since Specialization

Citations

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

Fields of papers citing papers by Gerson Zaverucha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gerson Zaverucha

This figure shows the co-authorship network connecting the top 25 collaborators of Gerson Zaverucha. A scholar is included among the top collaborators of Gerson Zaverucha 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 Gerson Zaverucha. Gerson Zaverucha 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

Paes, Aline, et al.. (2025). Towards Robust Neurosymbolic Relational Learning. City Research Online (City University London). 1–8.

Paes, Aline, et al.. (2023). Word embeddings-based transfer learning for boosted relational dependency networks. Machine Learning. 113(3). 1269–1302. 4 indexed citations

Vieira, Daniel, et al.. (2023). A Statistical Relational Learning Approach Towards Products, Software Vulnerabilities and Exploits. IEEE Transactions on Network and Service Management. 20(3). 3782–3802. 4 indexed citations

Zaverucha, Gerson, et al.. (2018). Using OpenWordnet-PT for Question Answering on Legal Domain. 105–112. 1 indexed citations

Bernardes, Juliana, Gerson Zaverucha, Catherine Vaquero, & Alessandra Carbone. (2016). Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence. PLoS Computational Biology. 12(7). e1005038–e1005038. 22 indexed citations

Zaverucha, Gerson, et al.. (2015). Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses. City Research Online (City University London). 3 indexed citations

Garcez, Artur d’Avila, et al.. (2015). Relational knowledge extraction from neural networks. Neural Information Processing Systems. 146–154. 3 indexed citations

Bernardes, Juliana, et al.. (2015). Evaluation and improvements of clustering algorithms for detecting remote homologous protein families. BMC Bioinformatics. 16(1). 34–34. 14 indexed citations

Zaverucha, Gerson, Vı́tor Santos Costa, & Aline Paes. (2014). Inductive Logic Programming. Lecture notes in computer science. 2 indexed citations

10.

Bernardes, Juliana, Alessandra Carbone, & Gerson Zaverucha. (2011). A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models. BMC Bioinformatics. 12(1). 83–83. 7 indexed citations

11.

Bernardes, Juliana, Alberto M. R. Dávila, Vı́tor Santos Costa, & Gerson Zaverucha. (2007). Improving model construction of profile HMMs for remote homology detection through structural alignment. BMC Bioinformatics. 8(1). 435–435. 16 indexed citations

12.

Zaverucha, Gerson, et al.. (2006). Genetic Based Machine Learning: Merging Pittsburgh and Michigan, an Implicit Feature Selection Mechanism and a New Crossover Operator. 58–58. 5 indexed citations

13.

Zaverucha, Gerson, et al.. (2006). Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks.. The European Symposium on Artificial Neural Networks. 425–430. 1 indexed citations

14.

Teixeira, Marcelo, et al.. (2003). Recurrent neural gas in electric load forecasting. 5. 3468–3473. 1 indexed citations

15.

Teixeira, Marcelo & Gerson Zaverucha. (2002). Fuzzy Bayes and Fuzzy Markov Predictors. Journal of Intelligent & Fuzzy Systems. 13(2). 155–165. 7 indexed citations

16.

Zaverucha, Gerson, et al.. (2002). An integration of neural networks and nonmonotonic reasoning for power system diagnosis. 3. 1409–1413. 2 indexed citations

17.

Zaverucha, Gerson, et al.. (1998). A Penalty-Function Approach to Rule Extraction from Knowledge-Based Neural Networks. International Conference on Neural Information Processing. 27(6). 1497–1500. 1 indexed citations

18.

Mattoso, Marta, et al.. (1998). Towards an inductive design of distributed object oriented databases. 3. 188–197. 6 indexed citations

19.

Silva, Andréa Lago da, et al.. (1994). Artificial neural networks for power systems diagnosis. 3738–3743 vol.6. 17 indexed citations

20.

Zaverucha, Gerson. (1992). Logical foundations of a modal defeasible relevant logic of belief. European Conference on Artificial Intelligence. 615–619. 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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