Javier Echanobe

840 citations
48 papers · 565 · h-index 15

Impact in

Papers in

Javier Echanobe

47 papers receiving 547 citations

Peers

Javier Echanobe
Comparison fields: 5 of 75
  • Automotive Engineering 118
  • Artificial Intelligence 246
  • Computer Vision and Pattern Recognition 116
  • Control and Systems Engineering 118
  • Hardware and Architecture 28
Replace Koldo Basterretxea with:
Koldo Basterretxea Spain
Guihe Qin China
Mathias Lechner Austria
Fanny Spagnolo Italy
Nur Syazreen Ahmad Malaysia
Muhammad Usman Rafique United States
Mitra Mirhassani Canada
Chang‐Ho Hyun South Korea
Paul Watta United States
Pengfei Li China
Javier Echanobe relative to Koldo Basterretxea Spain Koldo Basterretxea's profile →
Citations per field
00.5×
Koldo Basterretxea · 1×
Citations per year

Countries citing papers authored by Javier Echanobe

Since Specialization
Citations

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

Fields of papers citing papers by Javier Echanobe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Javier Echanobe, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Javier Echanobe Line = papers co-authored together Javier Echanobe links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201457
2 200853
3 201537
4 201332
5 201632
6 201430
7 200830
8 201528
9 201425
10 200724
11 201120
12 200318
13 201817
14 202317
15 202116
16 201613
17 201411
18 202110
19 20147
20 20107

About Javier Echanobe

Javier Echanobe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Automotive Engineering and Control and Systems Engineering, having authored 48 papers that have together received 565 indexed citations. Recurring topics across this work include Neural Networks and Applications (14 papers), Fuzzy Logic and Control Systems (11 papers), Machine Learning and ELM (11 papers), Advanced Memory and Neural Computing (6 papers), Evolutionary Algorithms and Applications (6 papers), Advanced Battery Technologies Research (5 papers), Advanced Neural Network Applications (5 papers) and CCD and CMOS Imaging Sensors (5 papers). The work is most often cited by research in Automotive Engineering (118 citations), Artificial Intelligence (246 citations), Computer Vision and Pattern Recognition (116 citations), Control and Systems Engineering (118 citations) and Hardware and Architecture (28 citations). Javier Echanobe has collaborated with scholars based in Spain, United Kingdom and Germany. Frequent co-authors include I. del Campo, Koldo Basterretxea, Faiyaz Doctor, V. Sanchez Martinez, J. G. Muga, Adolfo del Campo, José Ramón González de Mendívil, José Javier Astráin, Pradyumn Kumar Shukla and A. Ruschhaupt. Their work appears in journals such as Physical Review A, Journal of Systems Architecture, Electronics Letters, Fuzzy Sets and Systems and Applied Soft Computing.

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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