Newton Spolaôr

1.8k total citations
31 papers, 865 citations indexed

About

Newton Spolaôr is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Newton Spolaôr has authored 31 papers receiving a total of 865 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 10 papers in Molecular Biology and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Newton Spolaôr's work include Text and Document Classification Technologies (11 papers), Machine Learning in Bioinformatics (7 papers) and Image Retrieval and Classification Techniques (5 papers). Newton Spolaôr is often cited by papers focused on Text and Document Classification Technologies (11 papers), Machine Learning in Bioinformatics (7 papers) and Image Retrieval and Classification Techniques (5 papers). Newton Spolaôr collaborates with scholars based in Brazil, Moldova and Greece. Newton Spolaôr's co-authors include Huei Lee, Maria Carolina Monard, Everton Alvares Cherman, Fabiane Barreto Vavassori Benitti, Ana Carolina Lorena, Feng Chung Wu, Grigorios Tsoumakas, Cláudio Saddy Rodrigues Coy, Ivan G. Costa and Marcílio C. P. de Souto and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Computers & Education.

In The Last Decade

Newton Spolaôr

29 papers receiving 839 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Newton Spolaôr Brazil 15 522 276 163 132 72 31 865
Karl Moritz Hermann United Kingdom 11 1.1k 2.2× 272 1.0× 169 1.0× 66 0.5× 31 0.4× 16 1.5k
Phil Blunsom United Kingdom 14 966 1.9× 235 0.9× 70 0.4× 59 0.4× 29 0.4× 29 1.4k
Selma Ayşe Özel Türkiye 15 700 1.3× 118 0.4× 216 1.3× 53 0.4× 50 0.7× 49 938
Alexander Ratner United States 13 653 1.3× 132 0.5× 131 0.8× 148 1.1× 47 0.7× 22 1.1k
Marion Neumann Germany 10 746 1.4× 293 1.1× 144 0.9× 133 1.0× 10 0.1× 18 1.1k
Junzhong Ji China 19 502 1.0× 192 0.7× 119 0.7× 146 1.1× 24 0.3× 128 1.2k
Aimilia Tzanavari Cyprus 8 254 0.5× 148 0.5× 144 0.9× 41 0.3× 22 0.3× 16 815
Lixin Han China 16 389 0.7× 224 0.8× 308 1.9× 31 0.2× 20 0.3× 74 890
Pengfei Liu China 23 2.0k 3.8× 316 1.1× 236 1.4× 74 0.6× 24 0.3× 80 2.3k
Xiaodan Zhang China 19 423 0.8× 423 1.5× 144 0.9× 70 0.5× 12 0.2× 83 1.2k

Countries citing papers authored by Newton Spolaôr

Since Specialization
Citations

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

Fields of papers citing papers by Newton Spolaôr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Newton Spolaôr

This figure shows the co-authorship network connecting the top 25 collaborators of Newton Spolaôr. A scholar is included among the top collaborators of Newton Spolaôr 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 Newton Spolaôr. Newton Spolaôr 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.
Spolaôr, Newton, et al.. (2024). Avaliação de variações da rede profunda EfficientNet em bases dermoscópicas. Journal of Health Informatics. 16(Especial).
2.
Spolaôr, Newton, Huei Lee, Ana Isabel Mendes, et al.. (2023). Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets. Multimedia Tools and Applications. 83(9). 27305–27329. 18 indexed citations
3.
Spolaôr, Newton, et al.. (2023). Caracterização e Classificação de Conjuntos Desbalanceados de Dermoscopias. Journal of Health Informatics. 15(Especial).
4.
Parmezan, Antonio Rafael Sabino, Huei Lee, Newton Spolaôr, & Feng Chung Wu. (2021). Automatic recommendation of feature selection algorithms based on dataset characteristics. Expert Systems with Applications. 185. 115589–115589. 23 indexed citations
5.
Lee, Huei, et al.. (2019). Heuristics-based Responsiveness Evaluation of a Telemedicine Computational Web System. IEEE Latin America Transactions. 17(3). 444–452. 5 indexed citations
6.
Lee, Huei, et al.. (2018). Web System Prototype based on speech recognition to construct medical reports in Brazilian Portuguese. International Journal of Medical Informatics. 121. 39–52. 10 indexed citations
7.
Spolaôr, Newton, Ana Carolina Lorena, & Huei Lee. (2017). Feature Selection via Pareto Multi-objective Genetic Algorithms. Applied Artificial Intelligence. 31(9-10). 764–791. 10 indexed citations
8.
Trambaiolli, Lucas R., Newton Spolaôr, Ana Carolina Lorena, Renato Anghinah, & João Ricardo Sato. (2017). Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease. Clinical Neurophysiology. 128(10). 2058–2067. 64 indexed citations
9.
Spolaôr, Newton & Fabiane Barreto Vavassori Benitti. (2017). Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers & Education. 112. 97–107. 101 indexed citations
10.
Oliva, Jefferson Tales, Huei Lee, Newton Spolaôr, Cláudio Saddy Rodrigues Coy, & Feng Chung Wu. (2016). Prototype system for feature extraction, classification and study of medical images. Expert Systems with Applications. 63. 267–283. 22 indexed citations
11.
Spolaôr, Newton, Maria Carolina Monard, Grigorios Tsoumakas, & Huei Lee. (2015). A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing. 180. 3–15. 70 indexed citations
12.
Spolaôr, Newton, et al.. (2015). Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations. International Journal of Computational Intelligence Systems. 8(Supplement 2). 3–3. 8 indexed citations
13.
Spolaôr, Newton, Maria Carolina Monard, Grigorios Tsoumakas, & Huei Lee. (2014). Label Construction for Multi-label Feature Selection. Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico). 7. 247–252. 9 indexed citations
14.
Spolaôr, Newton, et al.. (2014). A framework for multi-label exploratory data analysis: ML-EDA. Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico). 1. 1–12. 1 indexed citations
15.
Spolaôr, Newton, et al.. (2014). A Framework to Generate Synthetic Multi-label Datasets. Electronic Notes in Theoretical Computer Science. 302. 155–176. 28 indexed citations
16.
Spolaôr, Newton, Everton Alvares Cherman, Maria Carolina Monard, & Huei Lee. (2013). ReliefF for Multi-label Feature Selection. 6–11. 143 indexed citations
17.
Spolaôr, Newton, et al.. (2013). A systematic review on experimental multi-label learning. 3 indexed citations
18.
Spolaôr, Newton, Everton Alvares Cherman, Maria Carolina Monard, & Huei Lee. (2013). A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach. Electronic Notes in Theoretical Computer Science. 292. 135–151. 164 indexed citations
19.
Spolaôr, Newton & Grigorios Tsoumakas. (2013). Evaluating Feature Selection Methods for Multi-Label Text Classication.. 15 indexed citations
20.
Lorena, Ana Carolina, Ivan G. Costa, Newton Spolaôr, & Marcílio C. P. de Souto. (2011). Analysis of complexity indices for classification problems: Cancer gene expression data. Neurocomputing. 75(1). 33–42. 42 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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