Javier Echanobe

822 citations
48 papers · 552 · h-index 15

Impact in

Papers in

Javier Echanobe

47 papers receiving 532 citations

Peers

Javier Echanobe
Comparison fields: 5 of 74
  • Automotive Engineering 117
  • Artificial Intelligence 244
  • Computer Vision and Pattern Recognition 113
  • Control and Systems Engineering 117
  • Hardware and Architecture 28
Replace Koldo Basterretxea with:
Koldo Basterretxea Spain
Fanny Spagnolo Italy
Mathias Lechner Austria
Nur Syazreen Ahmad Malaysia
Mitra Mirhassani Canada
Muhammad Usman Rafique United States
Fatih Erden United States
Paul Watta United States
Chang‐Ho Hyun South Korea
Damian Grzechca Poland
Javier Echanobe relative to Koldo Basterretxea Spain Koldo Basterretxea's profile →
Citations per field
00.5×2.9×
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 18 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 201536
4 201632
5 201332
6 201429
7 200828
8 201528
9 201425
10 200724
11 201120
12 200318
13 201816
14 202315
15 202114
16 201613
17 201411
18 202110
19 20107
20 20147

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 552 indexed citations. Recurring topics across this work include Neural Networks and Applications (14 papers), Machine Learning and ELM (11 papers), Fuzzy Logic and Control Systems (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 (117 citations), Artificial Intelligence (244 citations), Computer Vision and Pattern Recognition (113 citations), Control and Systems Engineering (117 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, Estibaliz Asua, José Javier Astráin and Hartmut Schmeck. Their work appears in journals such as Physical Review A, Fuzzy Sets and Systems, Journal of Systems Architecture, IEEE Transactions on Fuzzy 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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