Gerhard Paaß
- Artificial Intelligence top 1%
- Information Systems top 1%
- Sociology and Political Science top 10%
- Signal Processing top 5%
- Management Science and Operations Research top 5%
- Co-authors
- Andreas HothoAndreas NürnbergerJörg KindermannEdda LeopoldJoachim DiederichAndré BergholzMarie‐Francine MoensJeong Ho Chang
- Topics
- Natural Language Processing Techniques (7 papers)Topic Modeling (5 papers)Spam and Phishing Detection (4 papers)
In The Last Decade
Gerhard Paaß
27 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 845
- Information Systems 514
- Sociology and Political Science 140
- Signal Processing 122
- Management Science and Operations Research 103
Countries citing papers authored by Gerhard Paaß
This map shows the geographic impact of Gerhard Paaß'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 Gerhard Paaß with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerhard Paaß more than expected).
Fields of papers citing papers by Gerhard Paaß
This network shows the impact of papers produced by Gerhard Paaß. 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 Gerhard Paaß. The network helps show where Gerhard Paaß may publish in the future.
Co-authorship network of co-authors of Gerhard Paaß
This figure shows the co-authorship network connecting the top 25 collaborators of Gerhard Paaß. A scholar is included among the top collaborators of Gerhard Paaß 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 Gerhard Paaß. Gerhard Paaß is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 6 | |
| 3 | 18 | |
| 4 | 12 | |
| 5 | Named Entity Resolution Using Automatically Extracted Semantic Information. | 6 |
| 6 | 6 | |
| 7 | Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics | 1 |
| 8 | Detecting Known and New Salting Tricks in Unwanted Emails. | 6 |
| 9 | Improved Phishing Detection using Model-Based Features. | 93 |
| 10 | Learning Prototype Ontologies by Hierachical Latent Semantic Analysis. | 7 |
| 11 | 2 | |
| 12 | 218 | |
| 13 | Künstliche Neuronale Netze: eine Bestandsaufnahme. | 1 |
| 14 | Bayesian Query Construction for Neural Network Models | 14 |
| 15 | European Conference on Symbolic and Quantitative Approaches for Uncertainty. | 13 |
| 16 | Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm | 19 |
| 17 | Second order probabilities for uncertain and conflicting evidence | 8 |
| 18 | 21 | |
| 19 | 64 | |
| 20 | Consistent evaluation of uncertain reasoning systems | 3 |
About Gerhard Paaß
Gerhard Paaß is a scholar working on Artificial Intelligence, Information Systems and Signal Processing, having authored 27 papers that have together received 1.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Topic Modeling (5 papers) and Spam and Phishing Detection (4 papers). The work is most often cited by research in Artificial Intelligence (845 citations), Information Systems (514 citations) and Signal Processing (122 citations). Gerhard Paaß has collaborated with scholars based in Germany, Belgium and Australia. Frequent co-authors include Andreas Hotho, Andreas Nürnberger, Jörg Kindermann, Edda Leopold, Joachim Diederich, André Bergholz, Marie‐Francine Moens, Jeong Ho Chang, Sven Giesselbach and Jochen Heinsohn. Their work appears in journals such as Journal of Business and Economic Statistics, Applied Intelligence and Neurological Research.
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.