Jose J. Valero-Mas

61 total papers · 683 total citations
37 papers, 429 citations indexed

About

Jose J. Valero-Mas is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Jose J. Valero-Mas has authored 37 papers receiving a total of 429 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 18 papers in Signal Processing and 18 papers in Artificial Intelligence. Recurrent topics in Jose J. Valero-Mas's work include Music and Audio Processing (18 papers), Machine Learning and Data Classification (13 papers) and Music Technology and Sound Studies (11 papers). Jose J. Valero-Mas is often cited by papers focused on Music and Audio Processing (18 papers), Machine Learning and Data Classification (13 papers) and Music Technology and Sound Studies (11 papers). Jose J. Valero-Mas collaborates with scholars based in Spain, South Korea and Canada. Jose J. Valero-Mas's co-authors include Juan Ramón Rico-Juan, Jorge Calvo-Zaragoza, Antonio‐Javier Gallego, Francisco J. Castellanos, José M. Iñesta, Antonio Pertusa, Xavier Serra, Emmanouil Benetos, Víctor M. Sánchez-Cartagena and Carlos De la Fuente and has published in prestigious journals such as Expert Systems with Applications, Computers & Education and Pattern Recognition.

In The Last Decade

Jose J. Valero-Mas

33 papers receiving 415 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Jose J. Valero-Mas	247	166	80	50	25	37	429
Fazeel Abid	204 0.8×	59 0.4×	56 0.7×	47 0.9×	13 0.5×	26	396
Thilo Stadelmann	248 1.0×	111 0.7×	184 2.3×	33 0.7×	3 0.1×	62	469
M. Julia Flores	211 0.9×	75 0.5×	85 1.1×	48 1.0×	11 0.4×	35	483
Nacer Farajzadeh	131 0.5×	289 1.7×	34 0.4×	16 0.3×	7 0.3×	30	494
Vivian F. López	120 0.5×	118 0.7×	47 0.6×	175 3.5×	15 0.6×	33	380
Mohamed Ettaouil	213 0.9×	114 0.7×	40 0.5×	46 0.9×	8 0.3×	57	407
J. Arturo Olvera-López	324 1.3×	137 0.8×	55 0.7×	55 1.1×	8 0.3×	29	488
Radwa Marzouk	133 0.5×	129 0.8×	59 0.7×	39 0.8×	11 0.4×	57	449
Haojun Sun	231 0.9×	105 0.6×	90 1.1×	62 1.2×	7 0.3×	44	451
Dongyang Li	172 0.7×	79 0.5×	54 0.7×	50 1.0×	3 0.1×	64	398

Countries citing papers authored by Jose J. Valero-Mas

Since Specialization

Citations

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

Fields of papers citing papers by Jose J. Valero-Mas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jose J. Valero-Mas. 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 Jose J. Valero-Mas. The network helps show where Jose J. Valero-Mas may publish in the future.

Co-authorship network of co-authors of Jose J. Valero-Mas

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

All Works

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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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