Tomás Maul
Impact in
- Building and Construction top 10%
- Building Energy and Comfort Optimization
- BIM and Construction Integration
Papers in
-
- Neural Networks and Applications 6
- AI in cancer detection 4
- Anomaly Detection Techniques and Applications 3
- Co-authors
- Andrzej Bargieła (9 shared papers)Tuong Thuy Vu (7 shared papers)Iman Yi Liao (7 shared papers)Amr Ahmed (4 shared papers)Hui Ling Chen (1 shared paper)Chris Gibbins (1 shared paper)Kang‐Nee Ting (1 shared paper)Nan Gao (1 shared paper)
- Journals
- Multimedia Tools and Applications (2 papers)Neural Computing and Applications (2 papers)Automation in Construction (2 papers)Neural Processing Letters (2 papers)Neurocomputing (2 papers)
- Partner nations
- MalaysiaUnited KingdomVietnam
In The Last Decade
Tomás Maul
51 papers receiving 289 citations
Peers
Comparison fields: 5 of 94
- Building and Construction 66
- Medical Laboratory Technology 4
- Architecture 4
- Computer Vision and Pattern Recognition 53
- Media Technology 22
Countries citing papers authored by Tomás Maul
This map shows the geographic impact of Tomás Maul'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 Tomás Maul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomás Maul more than expected).
Fields of papers citing papers by Tomás Maul
This network shows the impact of papers produced by Tomás Maul. 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 Tomás Maul. The network helps show where Tomás Maul may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomás Maul, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 69 | |
| 2 | 2024 | 24 | |
| 3 | 2022 | 22 | |
| 4 | 2019 | 19 | |
| 5 | 2016 | 16 | |
| 6 | 2017 | 12 | |
| 7 | 2012 | 11 | |
| 8 | 2019 | 10 | |
| 9 | 2014 | 9 | |
| 10 | 2013 | 7 | |
| 11 | 2010 | 7 | |
| 12 | 2023 | 7 | |
| 13 | 2015 | 5 | |
| 14 | 2014 | 5 | |
| 15 | 2009 | 4 | |
| 16 | 2010 | 4 | |
| 17 | 2022 | 4 | |
| 18 | 2023 | 3 | |
| 19 | 2024 | 3 | |
| 20 | 2021 | 3 |
About Tomás Maul
Tomás Maul is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Media Technology and Cognitive Neuroscience, having authored 54 papers that have together received 301 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Advanced Memory and Neural Computing (5 papers), AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), CCD and CMOS Imaging Sensors (4 papers), Anomaly Detection Techniques and Applications (3 papers), Neuroscience and Neural Engineering (3 papers) and Remote-Sensing Image Classification (3 papers). The work is most often cited by research in Building and Construction (66 citations), Medical Laboratory Technology (4 citations), Architecture (4 citations), Computer Vision and Pattern Recognition (53 citations) and Media Technology (22 citations). Tomás Maul has collaborated with scholars based in Malaysia, United Kingdom and Vietnam. Frequent co-authors include Andrzej Bargieła, Tuong Thuy Vu, Iman Yi Liao, Amr Ahmed, Hui Ling Chen, Chris Gibbins, Kang‐Nee Ting, Nan Gao, A. Foss and Gustavo Henrique Paetzold. Their work appears in journals such as Multimedia Tools and Applications, Neural Computing and Applications, Automation in Construction, Neural Processing Letters and Neurocomputing.
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.