J. Kay
Impact in
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Neural Networks and Applications
- Evolutionary Algorithms and Applications
- Information Systems top 5%
- Data Mining Algorithms and Applications
- Spam and Phishing Detection
Papers in
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- Spam and Phishing Detection 1
- Data Mining Algorithms and Applications 1
-
- Text and Document Classification Technologies 1
- Machine Learning and Data Classification 1
- Advanced Computational Techniques and Applications 1
- Imbalanced Data Classification Techniques 1
- Co-authors
- Floriana Esposito (1 shared paper)Donato Malerba (1 shared paper)Giovanni Semeraro (1 shared paper)Elisabeth Crawford (1 shared paper)Eric McCreath (1 shared paper)A. Jameson (1 shared paper)
- Journals
- IRAL - International Review of Applied Linguistics in Language Teaching (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)ANU Open Research (Australian National University) (1 paper)
- Partner nations
- Italy
In The Last Decade
J. Kay
2 papers receiving 321 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 223
- Information Systems 132
- Health Information Management 23
- Computational Theory and Mathematics 47
- Signal Processing 20
Countries citing papers authored by J. Kay
This map shows the geographic impact of J. Kay'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 J. Kay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Kay more than expected).
Fields of papers citing papers by J. Kay
This network shows the impact of papers produced by J. Kay. 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 J. Kay. The network helps show where J. Kay may publish in the future.
Co-authors
The 6 scholars most cited alongside J. Kay, 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 | 1997 | 340 | |
| 2 | Automatic Induction of Rules of e-mail Classification | 2001 | 24 |
| 3 | 1967 | 0 |
About J. Kay
J. Kay is a scholar working on Information Systems, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 364 indexed citations. Recurring topics across this work include Spam and Phishing Detection (1 paper), Text and Document Classification Technologies (1 paper), Machine Learning and Data Classification (1 paper), Advanced Computational Techniques and Applications (1 paper), Data Mining Algorithms and Applications (1 paper) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (223 citations), Information Systems (132 citations), Health Information Management (23 citations), Computational Theory and Mathematics (47 citations) and Signal Processing (20 citations). J. Kay has collaborated with scholars based in Italy. Frequent co-authors include Floriana Esposito, Donato Malerba, Giovanni Semeraro, Elisabeth Crawford, Eric McCreath and A. Jameson. Their work appears in journals such as IRAL - International Review of Applied Linguistics in Language Teaching, IEEE Transactions on Pattern Analysis and Machine Intelligence and ANU Open Research (Australian National 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.