David Macêdo

447 total citations
22 papers, 247 citations indexed

About

David Macêdo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, David Macêdo has authored 22 papers receiving a total of 247 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 4 papers in Electrical and Electronic Engineering. Recurrent topics in David Macêdo's work include Advanced Neural Network Applications (7 papers), Adversarial Robustness in Machine Learning (3 papers) and Advanced Image and Video Retrieval Techniques (3 papers). David Macêdo is often cited by papers focused on Advanced Neural Network Applications (7 papers), Adversarial Robustness in Machine Learning (3 papers) and Advanced Image and Video Retrieval Techniques (3 papers). David Macêdo collaborates with scholars based in Brazil, Canada and United States. David Macêdo's co-authors include Cleber Zanchettin, Adriano L. I. Oliveira, Divanilson R. Campelo, Paulo Freitas de Araujo-Filho, Georges Kaddoum, Teresa B. Ludermir, Paulo S. G. de Mattos Neto, Luiz Antônio de Oliveira, Tsang Ing Ren and Byron Leite Dantas Bezerra and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

David Macêdo

19 papers receiving 237 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Macêdo Brazil	8	136	100	80	55	26	22	247
Jiabao Wang China	11	112 0.8×	39 0.4×	219 2.7×	50 0.9×	11 0.4×	72	398
Muhammad Usman Yaseen Pakistan	9	50 0.4×	80 0.8×	114 1.4×	30 0.5×	12 0.5×	20	226
Belkacem Fergani Algeria	9	129 0.9×	110 1.1×	168 2.1×	45 0.8×	11 0.4×	31	300
Zhongtang Zhao China	10	193 1.4×	64 0.6×	187 2.3×	28 0.5×	25 1.0×	15	367
Zhaomin Chen Singapore	7	265 1.9×	201 2.0×	31 0.4×	75 1.4×	61 2.3×	11	373
Chee Chong United States	6	197 1.4×	66 0.7×	51 0.6×	29 0.5×	51 2.0×	12	307
K. Deepak India	9	190 1.4×	119 1.2×	134 1.7×	20 0.4×	6 0.2×	24	294
Krasimir Tonchev Bulgaria	10	37 0.3×	113 1.1×	107 1.3×	34 0.6×	19 0.7×	72	323
Yuanwang Wei China	8	185 1.4×	86 0.9×	226 2.8×	18 0.3×	8 0.3×	14	333
B. Perumal India	10	62 0.5×	69 0.7×	98 1.2×	23 0.4×	7 0.3×	50	247

Countries citing papers authored by David Macêdo

Since Specialization

Citations

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

Fields of papers citing papers by David Macêdo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Macêdo

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

Macêdo, David, Cleber Zanchettin, & Teresa B. Ludermir. (2024). Sigmoidal learning rate optimizer for deep neural network training using a two-phase adaptation approach. Applied Soft Computing. 167. 112264–112264. 1 indexed citations

Macêdo, David, et al.. (2023). Learning What, Where and Which to Transfer. 1 indexed citations

Macêdo, David, et al.. (2023). Improving small object detection with DETRAug. 1–8. 1 indexed citations

Macêdo, David, et al.. (2022). PictoBERT: Transformers for next pictogram prediction. Expert Systems with Applications. 202. 117231–117231. 7 indexed citations

Fernandes, Bruno, et al.. (2022). Convolution Optimization in Fire Classification. IEEE Access. 10. 23642–23658. 5 indexed citations

Macêdo, David, et al.. (2022). A Fast Multiple Cue Fusing Approach for Human Emotion Recognition. SSRN Electronic Journal. 4 indexed citations

Macêdo, David, Tsang Ing Ren, Cleber Zanchettin, Adriano L. I. Oliveira, & Teresa B. Ludermir. (2021). Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples. IEEE Transactions on Neural Networks and Learning Systems. 33(6). 2350–2364. 17 indexed citations

Macêdo, David, et al.. (2021). KutralNext: An Efficient Multi-label Fire and Smoke Image Recognition Model. 7–13. 1 indexed citations

Macêdo, David, et al.. (2021). Multi-Class Mobile Money Service Financial Fraud Detection by Integrating Supervised Learning with Adversarial Autoencoders. 1–7. 1 indexed citations

10.

Araujo-Filho, Paulo Freitas de, et al.. (2020). Intrusion Detection for Cyber–Physical Systems Using Generative Adversarial Networks in Fog Environment. IEEE Internet of Things Journal. 8(8). 6247–6256. 107 indexed citations

11.

Macêdo, David, Cleber Zanchettin, Adriano L. I. Oliveira, & Teresa B. Ludermir. (2019). Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study. Expert Systems with Applications. 124. 271–281. 28 indexed citations

12.

Macêdo, David, Tsang Ing Ren, Cleber Zanchettin, Adriano L. I. Oliveira, & Teresa B. Ludermir. (2019). Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum Entropy. arXiv (Cornell University).

13.

Macêdo, David, et al.. (2019). Improving Universal Language Model Fine-Tuning using Attention Mechanism. 521. 1–7. 1 indexed citations

14.

Macêdo, David, et al.. (2019). Otimização do Consumo de Energia em Redes Ad Hoc Aloha Empregando Deep Learning.

15.

Zanchettin, Cleber, et al.. (2019). Towards Optimizing Convolutional Neural Networks for Robotic Surgery Skill Evaluation. 1–8. 9 indexed citations

16.

Macêdo, David, et al.. (2019). Additive Margin SincNet for Speaker Recognition. arXiv (Cornell University). 1–5. 6 indexed citations

17.

Oliveira, Luiz Antônio de, et al.. (2018). SegNetRes-CRF: A Deep Convolutional Encoder-Decoder Architecture for Semantic Image Segmentation. 1–6. 16 indexed citations

18.

Macêdo, David, Cleber Zanchettin, & Teresa B. Ludermir. (2018). Simple Fast Convolutional Feature Learning. 1 indexed citations

19.

Macêdo, David, et al.. (1999). A secret-key cipher based on a non-linear structured code. Computer Communications. 22(8). 758–761. 2 indexed citations

20.

Macêdo, David, et al.. (1996). Cryptanalysis of Krouk's public-key cipher. Electronics Letters. 32(14). 1279–1280. 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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