Ignacio Heredia
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
Papers in ⓘ
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- Anomaly Detection Techniques and Applications 2
- Computational Physics and Python Applications 1
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- COVID-19 diagnosis using AI 1
- Co-authors
- Álvaro López García (4 shared papers)Štefan Dlugolinský (1 shared paper)Ladislav Hluchý (1 shared paper)Peter Malík (1 shared paper)Giang Nguyen (2 shared papers)Martin Bobák (1 shared paper)Viet Tran (2 shared papers)L. Lloret Iglesias (8 shared papers)
- Journals
- Scientific Reports (2 papers)Heliyon (2 papers)Artificial Intelligence Review (1 paper)Earth Science Informatics (1 paper)SHILAP Revista de lepidopterología (1 paper)
In The Last Decade
Ignacio Heredia
10 papers receiving 521 citations
Hit Papers
Peers
Comparison fields: 5 of 132
- Health Informatics 12
- Artificial Intelligence 175
- Signal Processing 40
- Computer Vision and Pattern Recognition 69
- Management Information Systems 29
Countries citing papers authored by Ignacio Heredia
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
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 25 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.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey Hit paper breakdown → | 2019 | 516 |
| 2 | 2023 | 8 | |
| 3 | 2019 | 7 | |
| 4 | 2022 | 6 | |
| 5 | 2024 | 2 | |
| 6 | 2018 | 2 | |
| 7 | 2023 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2021 | 1 | |
| 10 | 2018 | 1 | |
| 11 | 2024 | 0 | |
| 12 | 2019 | 0 | |
| 13 | 2018 | 0 | |
| 14 | 2019 | 0 | |
| 15 | 2019 | 0 |
About Ignacio Heredia
Ignacio Heredia is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Food Science, Plant Science and Organic Chemistry, having authored 15 papers that have together received 545 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (2 papers), Fermentation and Sensory Analysis (2 papers), Advanced Chemical Sensor Technologies (1 paper), Horticultural and Viticultural Research (1 paper), COVID-19 diagnosis using AI (1 paper), Particle Detector Development and Performance (1 paper), Plant Pathogens and Fungal Diseases (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Health Informatics (12 citations), Artificial Intelligence (175 citations), Signal Processing (40 citations), Computer Vision and Pattern Recognition (69 citations) and Management Information Systems (29 citations). Ignacio Heredia has collaborated with scholars based in Spain, Slovakia and Germany. Frequent co-authors include Álvaro López García, Štefan Dlugolinský, Ladislav Hluchý, Peter Malík, Giang Nguyen, Martin Bobák, Viet Tran, L. Lloret Iglesias, J. Marco and María Castrillo. Their work appears in journals such as Scientific Reports, Heliyon, Artificial Intelligence Review, Earth Science Informatics and SHILAP Revista de lepidopterología.
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.