Gustavo Batista

3.8k total citations · 2 hit papers
46 papers, 2.1k citations indexed

About

Gustavo Batista is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Gustavo Batista has authored 46 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 10 papers in Computer Networks and Communications and 8 papers in Signal Processing. Recurrent topics in Gustavo Batista's work include Anomaly Detection Techniques and Applications (13 papers), Imbalanced Data Classification Techniques (8 papers) and Machine Learning and Data Classification (7 papers). Gustavo Batista is often cited by papers focused on Anomaly Detection Techniques and Applications (13 papers), Imbalanced Data Classification Techniques (8 papers) and Machine Learning and Data Classification (7 papers). Gustavo Batista collaborates with scholars based in Brazil, Australia and United States. Gustavo Batista's co-authors include Maria Carolina Monard, Eamonn Keogh, Thanawin Rakthanmanon, Qiang Zhu, Bilson Campana, Abdullah Mueen, Jesin Zakaria, Brandon Westover, Ronaldo C. Prati and Agenor Mafra‐Neto and has published in prestigious journals such as Expert Systems with Applications, Neurocomputing and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Gustavo Batista

36 papers receiving 2.0k citations

Hit Papers

align trajectories sort by overperformance

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.

Searching and mining trillions of time series subsequences under dynamic time warping

2012 677 citations Thanawin Rakthanmanon, Bilson Campana et al. profile →
An analysis of four missing data treatment methods for supervised learning

2003 573 citations Gustavo Batista, Maria Carolina Monard Applied Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gustavo Batista Brazil	17	1.0k	876	269	212	151	46	2.1k
François Petitjean Australia	21	894 0.9×	824 0.9×	221 0.8×	133 0.6×	161 1.1×	57	2.4k
Derya Birant Türkiye	19	777 0.8×	424 0.5×	297 1.1×	295 1.4×	69 0.5×	90	2.3k
Juan J. Rodríguez Spain	25	1.8k 1.8×	393 0.4×	699 2.6×	330 1.6×	76 0.5×	76	3.3k
Shengrui Wang Canada	24	1.0k 1.0×	283 0.3×	501 1.9×	372 1.8×	48 0.3×	136	2.0k
Diego Furtado Silva Brazil	20	786 0.8×	689 0.8×	214 0.8×	73 0.3×	145 1.0×	54	1.5k
Matthias Schubert Germany	19	835 0.8×	394 0.4×	444 1.7×	214 1.0×	51 0.3×	106	2.3k
Carlos J. Alonso Spain	12	911 0.9×	300 0.3×	369 1.4×	178 0.8×	56 0.4×	47	1.9k
Hujun Yin United Kingdom	27	948 0.9×	273 0.3×	1.0k 3.9×	97 0.5×	59 0.4×	164	2.5k
A. Prieto Spain	26	1.2k 1.2×	264 0.3×	310 1.2×	95 0.4×	50 0.3×	160	2.5k
Kuniaki Uehara Japan	16	602 0.6×	342 0.4×	499 1.9×	187 0.9×	119 0.8×	124	1.5k

Countries citing papers authored by Gustavo Batista

Since Specialization

Citations

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

Fields of papers citing papers by Gustavo Batista

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gustavo Batista

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Lyu, Minzhao, et al.. (2025). FastFlow : Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets. 46–48.

Quiles, Marcos G., et al.. (2025). Towards Label Shift Adaptation for Robust IoT Device Identification. 105–110.

Lyu, Minzhao, et al.. (2025). FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 9(2). 1–27.

Batista, Gustavo, et al.. (2025). Pedestrian trajectory prediction using goal-driven and dynamics-based deep learning framework. Expert Systems with Applications. 271. 126557–126557. 3 indexed citations

Wang, Honghui, et al.. (2024). Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning. 15068–15075. 3 indexed citations

Batista, Gustavo, et al.. (2024). Poster: Understanding and Managing Changes in IoT Device Behaviors for Reliable Network Traffic Inference. 25–27. 1 indexed citations

Batista, Gustavo, et al.. (2023). Aa-Dladmm: An Accelerated Admm-Based Framework for Training Deep Neural Networks. SSRN Electronic Journal.

Mishra, Deepak, et al.. (2021). COVID-Safe Spatial Occupancy Monitoring Using OFDM-Based Features and Passive WiFi Samples. ACM Transactions on Management Information Systems. 12(4). 1–24. 22 indexed citations

Batista, Gustavo, et al.. (2020). The Importance of the Test Set Size in Quantification Assessment. 2640–2646. 3 indexed citations

10.

Cherman, Everton Alvares, et al.. (2019). DyS: A Framework for Mixture Models in Quantification. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4552–4560. 14 indexed citations

11.

Flach, Peter, et al.. (2016). Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test. Bristol Research (University of Bristol). 1545–1554. 102 indexed citations

12.

Silva, Diego Furtado, Vinícius M. A. Souza, & Gustavo Batista. (2013). <b>A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English</b> - doi: 10.4025/actascitechnol.v35i4.19825. Acta Scientiarum. Technology. 35(4). 8 indexed citations

13.

Rakthanmanon, Thanawin, Bilson Campana, Abdullah Mueen, et al.. (2013). Addressing Big Data Time Series. ACM Transactions on Knowledge Discovery from Data. 7(3). 1–31. 145 indexed citations

14.

Batista, Gustavo, et al.. (2013). Coping with highly imbalanced datasets: A case study with definition extraction in a multilingual setting. Natural Language Engineering. 20(3). 327–359. 11 indexed citations

15.

Prati, Ronaldo C., Gustavo Batista, & Maria Carolina Monard. (2011). A Survey on Graphical Methods for Classification Predictive Performance Evaluation. IEEE Transactions on Knowledge and Data Engineering. 23(11). 1601–1618. 74 indexed citations

16.

Batista, Gustavo, Bilson Campana, & Eamonn Keogh. (2010). Classification of Live Moths Combining Texture, Color and Shape Primitives. 903–906. 9 indexed citations

17.

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

18.

Giusti, Rafael, Gustavo Batista, & Ronaldo C. Prati. (2008). Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules. 537–542. 8 indexed citations

19.

Batista, Gustavo & Maria Carolina Monard. (2003). An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence. 17(5-6). 519–533. 573 indexed citations breakdown →

20.

Baranauskas, José Augusto, et al.. (2000). A Computational Environment For ExtractingRules From Databases. WIT transactions on information and communication technologies. 25. 2 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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