Anton Bryl
- Information Systems top 5%
- Artificial Intelligence top 5%
- Computer Networks and Communications top 10%
- Signal Processing
- Computer Vision and Pattern Recognition
- Topics
- Spam and Phishing Detection (6 papers)Network Security and Intrusion Detection (6 papers)Text and Document Classification Technologies (5 papers)
- Journals
- Artificial Intelligence ReviewArrow@dit (Dublin Institute of Technology)Dublin City University Open Access Institutional Repository (Dublin City University)
In The Last Decade
Anton Bryl
9 papers receiving 254 citations
Peers
Comparison fields: 5 of 39
- Information Systems 246
- Artificial Intelligence 232
- Computer Networks and Communications 134
- Signal Processing 40
- Computer Vision and Pattern Recognition 27
Countries citing papers authored by Anton Bryl
This map shows the geographic impact of Anton Bryl'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 Anton Bryl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anton Bryl more than expected).
Fields of papers citing papers by Anton Bryl
This network shows the impact of papers produced by Anton Bryl. 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 Anton Bryl. The network helps show where Anton Bryl may publish in the future.
Co-authorship network of co-authors of Anton Bryl
This figure shows the co-authorship network connecting the top 25 collaborators of Anton Bryl. A scholar is included among the top collaborators of Anton Bryl based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anton Bryl. Anton Bryl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Improving dependency label accuracy using statistical post-editing: A cross-framework study | 1 |
| 2 | f-align: An open-source alignment tool for LFG f-structures | 1 |
| 3 | F-structure transfer-based statistical machine translation | 9 |
| 4 | 1 | |
| 5 | 250 | |
| 6 | E-Mail Spam Filtering with Local SVM Classifiers | 1 |
| 7 | Evaluation of the Highest Probability SVM Nearest Neighbor Classifier with Variable Relative Error Cost | 25 |
| 8 | Instance-based spam filtering using SVM nearest neighbor classifier | 17 |
| 9 | Highest Probability SVM Nearest Neighbor Classifier for Spam Filtering | 2 |
| 10 | Learning-Based Spam Filters: the Influence of the Temporal Distribution of Training Data | 0 |
About Anton Bryl
Anton Bryl is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications, having authored 10 papers that have together received 307 indexed citations. Recurring topics across this work include Spam and Phishing Detection (6 papers), Network Security and Intrusion Detection (6 papers) and Text and Document Classification Technologies (5 papers). The work is most often cited by research in Information Systems (246 citations), Artificial Intelligence (232 citations) and Computer Networks and Communications (134 citations). Anton Bryl has collaborated with scholars based in Italy and Ireland. Frequent co-authors include Enrico Blanzieri, Josef van Genabith, Yvette Graham, Jennifer Foster and Özlem Çetinoğlu. Their work appears in journals such as Artificial Intelligence Review, Arrow@dit (Dublin Institute of Technology) and Dublin City University Open Access Institutional Repository (Dublin City University).
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.