Ignacio Heredia

1.0k citations
13 papers · 613 · 1 hit paper · h-index 3

Impact in

Papers in

Ignacio Heredia

7 papers receiving 586 citations

Ignacio Heredia's Hit Papers

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey 2019 · 598 citations
5980+2+4Years since publication100200300400500

Peers

Ignacio Heredia
Comparison fields: 5 of 132
  • Health Informatics 12
  • Artificial Intelligence 207
  • Health Information Management 17
  • Signal Processing 44
  • Computer Vision and Pattern Recognition 79
Replace Martin Bobák with:
Martin Bobák Slovakia
Peter Malík Slovakia
Štefan Dlugolinský Slovakia
Alankrita Aggarwal India
Nisreen Innab Saudi Arabia
Geon Heo South Korea
Ramzan Talib Pakistan
Yuji Roh South Korea
Christopher Ifeanyi Eke Nigeria
Roheet Bhatnagar India
Ignacio Heredia relative to Martin Bobák Slovakia Martin Bobák's profile →
Citations per field
00.5×1.5×
Martin Bobák · 1×
Citations per year

Countries citing papers authored by Ignacio Heredia

Since Specialization
Citations

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

Fields of papers citing papers by Ignacio Heredia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Ignacio Heredia, 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 Ignacio Heredia Line = papers co-authored together Ignacio Heredia links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
Hit paper breakdown →
2019598
2 20227
3 20232
4 20242
5 20182
6 20181
7 20211
8 20250
9 20240
10 20190
11 20190
12 20190
13 20180

About Ignacio Heredia

Ignacio Heredia is a scholar working on Artificial Intelligence, Food Science, Plant Science, Organic Chemistry and Molecular Biology, having authored 13 papers that have together received 613 indexed citations. Recurring topics across this work include Fermentation and Sensory Analysis (2 papers), Isotope Analysis in Ecology (1 paper), Horticultural and Viticultural Research (1 paper), Cardiac Imaging and Diagnostics (1 paper), Plant Pathogens and Fungal Diseases (1 paper), Cryptography and Data Security (1 paper), Blockchain Technology Applications and Security (1 paper) and Marine and coastal ecosystems (1 paper). The work is most often cited by research in Health Informatics (12 citations), Artificial Intelligence (207 citations), Health Information Management (17 citations), Signal Processing (44 citations) and Computer Vision and Pattern Recognition (79 citations). Ignacio Heredia has collaborated with scholars based in Spain, Belgium and Slovakia. Frequent co-authors include Peter Malík, Viet Tran, Štefan Dlugolinský, Martin Bobák, Álvaro López García, Ladislav Hluchý, Giang Nguyen, Begoña Bartolomé, L. Lloret Iglesias and María‐José Motilva. Their work appears in journals such as Heliyon, Artificial Intelligence Review, Frontiers in Marine Science, Scientific Reports and Biodiversity Information Science and Standards.

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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