Fábio Alexandre Spanhol

3.6k total citations · 2 hit papers
16 papers, 2.3k citations indexed

About

Fábio Alexandre Spanhol is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Fábio Alexandre Spanhol has authored 16 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Networks and Communications, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Artificial Intelligence. Recurrent topics in Fábio Alexandre Spanhol's work include Air Quality Monitoring and Forecasting (4 papers), AI in cancer detection (4 papers) and IoT and Edge/Fog Computing (3 papers). Fábio Alexandre Spanhol is often cited by papers focused on Air Quality Monitoring and Forecasting (4 papers), AI in cancer detection (4 papers) and IoT and Edge/Fog Computing (3 papers). Fábio Alexandre Spanhol collaborates with scholars based in Brazil, France and Portugal. Fábio Alexandre Spanhol's co-authors include Caroline Petitjean, Laurent Heutte, Luiz S. Oliveira, Paulo Cavalin, Paul Honeiné, Leila Droprinchinski Martins, Cléber Antônio Lindino, Álvaro Largura, Fabiano Sandrini and Leonor Gusmão and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Biomedical Engineering and Sensors.

In The Last Decade

Fábio Alexandre Spanhol

13 papers receiving 2.2k citations

Hit Papers

align trajectories

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.

A Dataset for Breast Cancer Histopathological Image Classification

2015 1.1k citations Fábio Alexandre Spanhol, Luiz S. Oliveira et al. IEEE Transactions on Biomedical Engineering profile →
Breast cancer histopathological image classification using Convolutional Neural Networks

2016 617 citations Fábio Alexandre Spanhol, Luiz S. Oliveira et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fábio Alexandre Spanhol Brazil	8	2.0k	1.4k	1.2k	315	129	16	2.3k
Ángel Cruz-Roa Colombia	13	1.4k 0.7×	852 0.6×	776 0.6×	142 0.5×	206 1.6×	40	1.8k
Ajay Basavanhally United States	19	1.8k 0.9×	1.1k 0.8×	1.0k 0.9×	135 0.4×	395 3.1×	28	2.3k
Yuanpu Xie United States	13	901 0.5×	488 0.4×	598 0.5×	93 0.3×	309 2.4×	21	1.4k
José Rouco Spain	18	651 0.3×	899 0.7×	524 0.4×	101 0.3×	68 0.5×	49	1.5k
Inês Domingues Portugal	13	993 0.5×	812 0.6×	437 0.4×	176 0.6×	24 0.2×	45	1.5k
Humayun Irshad United States	13	999 0.5×	499 0.4×	728 0.6×	89 0.3×	437 3.4×	20	1.5k
Zizhao Zhang United States	19	824 0.4×	421 0.3×	789 0.7×	67 0.2×	63 0.5×	42	1.6k
J. E. Leo Desautels Canada	20	1.8k 0.9×	847 0.6×	1.5k 1.2×	84 0.3×	43 0.3×	53	2.4k
John Arévalo Colombia	13	705 0.4×	483 0.3×	367 0.3×	93 0.3×	72 0.6×	30	1.0k
Changhao Sun China	13	639 0.3×	423 0.3×	405 0.3×	96 0.3×	96 0.7×	23	1.0k

Countries citing papers authored by Fábio Alexandre Spanhol

Since Specialization

Citations

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

Fields of papers citing papers by Fábio Alexandre Spanhol

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fábio Alexandre Spanhol. 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 Fábio Alexandre Spanhol. The network helps show where Fábio Alexandre Spanhol may publish in the future.

Co-authorship network of co-authors of Fábio Alexandre Spanhol

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

All Works

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16 of 16 papers shown

Lindino, Cléber Antônio, et al.. (2024). Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements. Environmental Monitoring and Assessment. 196(8). 716–716.

Spanhol, Fábio Alexandre, et al.. (2023). Low-Cost Water Quality Sensors for IoT: A Systematic Review. Sensors. 23(9). 4424–4424. 48 indexed citations

Martins, Leila Droprinchinski, et al.. (2022). Evaluation of low-cost sensors for real-time water quality monitoring. 56–61. 1 indexed citations

Spanhol, Fábio Alexandre, et al.. (2022). Avaliação do Impacto da Temperatura e Umidade em Sensores de Baixo Custo de Material Particulado. 168–181. 1 indexed citations

Spanhol, Fábio Alexandre, et al.. (2022). A Machine Learning-Based Approach to Calibrate Low-Cost Particulate Matter Sensors. 1–8. 2 indexed citations

Spanhol, Fábio Alexandre, et al.. (2021). Em Busca de um Algoritmo de Retransmissão em Redes LoRaWAN. 70–83.

Spanhol, Fábio Alexandre, et al.. (2021). Deployment of a LoRaWAN network and evaluation of tracking devices in the context of smart cities. Journal of Internet Services and Applications. 12(1). 20 indexed citations

Spanhol, Fábio Alexandre, et al.. (2020). Implantação e Avaliação de uma Rede Sem-Fio de Longo Alcance e Baixa Potência para Cidades Inteligentes. 192–205.

Petitjean, Caroline, et al.. (2018). Multiple instance learning for histopathological breast cancer image classification. Expert Systems with Applications. 117. 103–111. 256 indexed citations

10.

Spanhol, Fábio Alexandre. (2018). Automatic breast cancer classification from histopathological images : a hybrid approach. UFPR Digital Collection (Federal University of Paraná). 7 indexed citations

11.

Spanhol, Fábio Alexandre, Luiz S. Oliveira, Paulo Cavalin, Caroline Petitjean, & Laurent Heutte. (2017). Deep features for breast cancer histopathological image classification. 1868–1873. 225 indexed citations

12.

Spanhol, Fábio Alexandre, Luiz S. Oliveira, Caroline Petitjean, & Laurent Heutte. (2016). Breast cancer histopathological image classification using Convolutional Neural Networks. 2560–2567. 617 indexed citations breakdown →

13.

Spanhol, Fábio Alexandre, Luiz S. Oliveira, Caroline Petitjean, & Laurent Heutte. (2015). A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering. 63(7). 1455–1462. 1104 indexed citations breakdown →

14.

Spanhol, Fábio Alexandre, et al.. (2012). Gerenciamento de projetos. 18 indexed citations

15.

Spanhol, Fábio Alexandre, et al.. (2009). Allele frequencies of 15 STRs in a representative sample of the Brazilian population. Forensic Science International Genetics. 4(2). e61–e63. 13 indexed citations

16.

Spanhol, Fábio Alexandre, Cláudia C. Branco, Paula Pacheco, et al.. (2009). 15 STR loci frequencies in the population from Paraná, Southern Brazil. Forensic Science International Genetics. 4(1). e23–e24. 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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