Mohak Shah
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 5%
- Molecular Biology
- Signal Processing top 10%
- Co-authors
- Nathalie JapkowiczMario MarchandMarina SokolovaStan ŚzpakowiczZahra KarimaghalooTal ArbelSimon J. FrancisDouglas L. Arnold
- Topics
- Machine Learning and Algorithms (3 papers)Imbalanced Data Classification Techniques (3 papers)Machine Learning and Data Classification (3 papers)
- Journals
- Lecture notes in computer scienceGroup Decision and NegotiationePrints Soton (University of Southampton)
- Partner nations
- CanadaUnited StatesPoland
In The Last Decade
Mohak Shah
10 papers receiving 995 citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Artificial Intelligence 502
- Computer Vision and Pattern Recognition 129
- Information Systems 127
- Molecular Biology 103
- Signal Processing 81
Countries citing papers authored by Mohak Shah
This map shows the geographic impact of Mohak Shah'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 Mohak Shah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohak Shah more than expected).
Fields of papers citing papers by Mohak Shah
This network shows the impact of papers produced by Mohak Shah. 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 Mohak Shah. The network helps show where Mohak Shah may publish in the future.
Co-authorship network of co-authors of Mohak Shah
This figure shows the co-authorship network connecting the top 25 collaborators of Mohak Shah. A scholar is included among the top collaborators of Mohak Shah 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 Mohak Shah. Mohak Shah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 10 | |
| 3 | Evaluating Learning Algorithms: A Classification Perspectivebreakdown → | 478 |
| 4 | Evaluating Learning Algorithmsbreakdown → | 504 |
| 5 | 9 | |
| 6 | 5 | |
| 7 | 8 | |
| 8 | A PAC-Bayes approach to the Set Covering Machine | 3 |
| 9 | PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data | 9 |
| 10 | The set covering machine with data-dependent half-spaces | 5 |
About Mohak Shah
Mohak Shah is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 10 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Imbalanced Data Classification Techniques (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Artificial Intelligence (502 citations), Signal Processing (81 citations) and Health Informatics (9 citations). Mohak Shah has collaborated with scholars based in Canada, United States and Poland. Frequent co-authors include Nathalie Japkowicz, Mario Marchand, Marina Sokolova, Stan Śzpakowicz, Zahra Karimaghaloo, Tal Arbel, Simon J. Francis, Douglas L. Arnold, D. Louis Collins and François Laviolette. Their work appears in journals such as Lecture notes in computer science, Group Decision and Negotiation and ePrints Soton (University of Southampton).
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.