R. Chaves

1.5k total citations
29 papers, 1.0k citations indexed

About

R. Chaves is a scholar working on Computer Vision and Pattern Recognition, Neurology and Physiology. According to data from OpenAlex, R. Chaves has authored 29 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 12 papers in Neurology and 10 papers in Physiology. Recurrent topics in R. Chaves's work include Image Retrieval and Classification Techniques (13 papers), Brain Tumor Detection and Classification (10 papers) and Alzheimer's disease research and treatments (9 papers). R. Chaves is often cited by papers focused on Image Retrieval and Classification Techniques (13 papers), Brain Tumor Detection and Classification (10 papers) and Alzheimer's disease research and treatments (9 papers). R. Chaves collaborates with scholars based in Spain, Brazil and Germany. R. Chaves's co-authors include Javier Ramı́rez, J. M. Górriz, I. Álvarez, D. Salas-González, M. López, F. Segovia, Carlos G. Puntonet, Pablo Padilla, Manuel Gómez-Río and Elmar W. Lang and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and Physics in Medicine and Biology.

In The Last Decade

R. Chaves

28 papers receiving 994 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R. Chaves Spain 15 400 369 280 246 198 29 1.0k
Francisco J. Martínez-Murcia Spain 21 314 0.8× 238 0.6× 264 0.9× 183 0.7× 304 1.5× 62 1.3k
M. López Spain 22 591 1.5× 562 1.5× 387 1.4× 334 1.4× 367 1.9× 42 1.7k
F. Segovia Spain 26 668 1.7× 621 1.7× 459 1.6× 435 1.8× 461 2.3× 82 2.0k
Chris Hinrichs United States 8 224 0.6× 158 0.4× 153 0.5× 315 1.3× 188 0.9× 11 717
Ninon Burgos France 20 358 0.9× 291 0.8× 396 1.4× 328 1.3× 205 1.0× 56 1.7k
Lauge Sørensen Denmark 16 217 0.5× 139 0.4× 209 0.7× 272 1.1× 140 0.7× 36 897
Biao Jie China 22 248 0.6× 192 0.5× 248 0.9× 274 1.1× 835 4.2× 62 1.4k
Xiaoke Hao China 16 167 0.4× 151 0.4× 163 0.6× 135 0.5× 202 1.0× 45 811
Bharat Richhariya India 14 181 0.5× 420 1.1× 538 1.9× 79 0.3× 159 0.8× 19 992
Junhao Wen United States 11 301 0.8× 115 0.3× 268 1.0× 311 1.3× 196 1.0× 30 883

Countries citing papers authored by R. Chaves

Since Specialization
Citations

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

Fields of papers citing papers by R. Chaves

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R. Chaves

This figure shows the co-authorship network connecting the top 25 collaborators of R. Chaves. A scholar is included among the top collaborators of R. Chaves 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 R. Chaves. R. Chaves 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.
Chaves, R., et al.. (2025). Assessment of body composition, sarcopenia and protein intake in mild to moderate Parkinson’s disease. Frontiers in Nutrition. 12. 1507545–1507545. 1 indexed citations
2.
Chaves, R., André Luis Debiaso Rossi, & Luís P. F. Garcia. (2023). A Financial Distress Prediction using a Non-stationary Dataset. 300–314.
3.
Chaves, R., Javier Ramı́rez, J. M. Górriz, I. Álvarez, & D. Salas-González. (2012). FDG and PIB biomarker PET analysis for the Alzheimer's disease detection using Association Rules. 2576–2579. 11 indexed citations
4.
Salas-González, D., J. M. Górriz, Javier Ramı́rez, et al.. (2012). Intensity normalization of FP-CIT SPECT in patients with Parkinsonism using the α-stable distribution. 3944–3946. 1 indexed citations
5.
Chaves, R., Javier Ramı́rez, J. M. Górriz, & Carlos G. Puntonet. (2012). Association rule-based feature selection method for Alzheimer’s disease diagnosis. Expert Systems with Applications. 39(14). 11766–11774. 53 indexed citations
6.
Chaves, R., J. M. Górriz, Javier Ramı́rez, et al.. (2011). Efficient mining of association rules for the early diagnosis of Alzheimer's disease. Physics in Medicine and Biology. 56(18). 6047–6063. 29 indexed citations
7.
Ramı́rez, Javier, J. M. Górriz, F. Segovia, et al.. (2010). Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters. 472(2). 99–103. 98 indexed citations
8.
Padilla, Pablo, J. M. Górriz, Javier Ramı́rez, et al.. (2010). Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction. Neuroscience Letters. 479(3). 192–196. 21 indexed citations
9.
Salas-González, D., J. M. Górriz, Javier Ramı́rez, et al.. (2010). Feature selection using factor analysis for Alzheimer's diagnosis using PET images. Medical Physics. 37(11). 6084–6095. 60 indexed citations
10.
Segovia, F., J. M. Górriz, Javier Ramı́rez, et al.. (2010). Classification of functional brain images using a GMM-based multi-variate approach. Neuroscience Letters. 474(1). 58–62. 33 indexed citations
11.
Salas-González, D., J. M. Górriz, Javier Ramı́rez, et al.. (2010). Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees. Physics in Medicine and Biology. 55(10). 2807–2817. 44 indexed citations
12.
Álvarez, I., J. M. Górriz, Javier Ramı́rez, et al.. (2010). 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Information Sciences. 181(4). 903–916. 104 indexed citations
13.
Ramı́rez, Javier, J. M. Górriz, F. Segovia, et al.. (2010). Early Alzheimer's disease diagnosis using partial least squares and random forests. 3217. 81–84. 5 indexed citations
14.
Padilla, Pablo, J. M. Górriz, Javier Ramı́rez, et al.. (2010). Alzheimer's disease detection in functional images using 2D Gabor wavelet analysis. Electronics Letters. 46(8). 556–558. 13 indexed citations
15.
Álvarez, I., J. M. Górriz, M. López, et al.. (2010). Computer aided diagnosis of Alzheimer’s disease using component based SVM. Applied Soft Computing. 11(2). 2376–2382. 61 indexed citations
16.
López, M., Javier Ramı́rez, J. M. Górriz, et al.. (2009). SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA. Neuroscience Letters. 464(3). 233–238. 97 indexed citations
17.
Segovia, F., J. M. Górriz, Javier Ramı́rez, et al.. (2009). fMRI data analysis using a novel clustering technique. 45. 3399–3403. 2 indexed citations
18.
López, M., Javier Ramı́rez, J. M. Górriz, et al.. (2009). Neurological image classification for the Alzheimer's Disease diagnosis using Kernel PCA and Support Vector Machines. 1496. 2486–2489. 9 indexed citations
19.
Chaves, R., Javier Ramı́rez, J. M. Górriz, et al.. (2009). SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting. Neuroscience Letters. 461(3). 293–297. 107 indexed citations
20.
López, M., Javier Ramı́rez, J. M. Górriz, et al.. (2009). Multivariate approaches for Alzheimer's disease diagnosis using Bayesian classifiers. 41. 3190–3193. 7 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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