Alexander K. Seewald
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Molecular Biology
- Co-authors
- Wilfried N. GanstererJohann PetrakGerhard WidmerJames R. CypserAndreas HeindlThomas E. JohnsonAlexander MendenhallIsabella Ellinger
- Topics
- Machine Learning and Data Classification (4 papers)Spam and Phishing Detection (3 papers)Network Security and Intrusion Detection (3 papers)
- Partner nations
- AustriaUnited KingdomUnited States
In The Last Decade
Alexander K. Seewald
20 papers receiving 231 citations
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 153
- Information Systems 60
- Computer Vision and Pattern Recognition 55
- Computer Networks and Communications 41
- Molecular Biology 27
Countries citing papers authored by Alexander K. Seewald
This map shows the geographic impact of Alexander K. Seewald'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 Alexander K. Seewald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander K. Seewald more than expected).
Fields of papers citing papers by Alexander K. Seewald
This network shows the impact of papers produced by Alexander K. Seewald. 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 Alexander K. Seewald. The network helps show where Alexander K. Seewald may publish in the future.
Co-authorship network of co-authors of Alexander K. Seewald
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander K. Seewald. A scholar is included among the top collaborators of Alexander K. Seewald 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 Alexander K. Seewald. Alexander K. Seewald is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 11 | |
| 9 | 0 | |
| 10 | 3 | |
| 11 | 18 | |
| 12 | Towards Autmating Malware Classification and Characterization. | 3 |
| 13 | 33 | |
| 14 | 7 | |
| 15 | 1 | |
| 16 | Towards a theoretical framework for ensemble classification | 10 |
| 17 | 4 | |
| 18 | How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness | 103 |
| 19 | 1 | |
| 20 | Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study | 11 |
About Alexander K. Seewald
Alexander K. Seewald is a scholar working on Aging, Biophysics and Artificial Intelligence, having authored 23 papers that have together received 251 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Spam and Phishing Detection (3 papers) and Network Security and Intrusion Detection (3 papers). The work is most often cited by research in Artificial Intelligence (153 citations), Aging (7 citations) and Computer Vision and Pattern Recognition (55 citations). Alexander K. Seewald has collaborated with scholars based in Austria, United Kingdom and United States. Frequent co-authors include Wilfried N. Gansterer, Johann Petrak, Gerhard Widmer, James R. Cypser, Andreas Heindl, Thomas E. Johnson, Alexander Mendenhall, Isabella Ellinger, Giovanna Bises and Ildikó Mesteri. Their work appears in journals such as PLoS ONE, Biophysical Journal and BioMed Research International.
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.