Rajesh Parekh
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Marketing top 10%
- Sociology and Political Science
- Co-authors
- Vasant HonavarJihoon YangRon KohaviLlew MasonZijian ZhengVineet ChaojiRushi BhattAngela Han
- Topics
- Neural Networks and Applications (11 papers)Data Mining Algorithms and Applications (6 papers)Machine Learning and Algorithms (5 papers)
- Partner nations
- United StatesIndiaChina
In The Last Decade
Rajesh Parekh
31 papers receiving 544 citations
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 324
- Information Systems 146
- Computer Vision and Pattern Recognition 92
- Marketing 82
- Sociology and Political Science 54
Countries citing papers authored by Rajesh Parekh
This map shows the geographic impact of Rajesh Parekh'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 Rajesh Parekh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rajesh Parekh more than expected).
Fields of papers citing papers by Rajesh Parekh
This network shows the impact of papers produced by Rajesh Parekh. 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 Rajesh Parekh. The network helps show where Rajesh Parekh may publish in the future.
Co-authorship network of co-authors of Rajesh Parekh
This figure shows the co-authorship network connecting the top 25 collaborators of Rajesh Parekh. A scholar is included among the top collaborators of Rajesh Parekh 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 Rajesh Parekh. Rajesh Parekh 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 | 4 | |
| 3 | 19 | |
| 4 | 27 | |
| 5 | 9 | |
| 6 | 77 | |
| 7 | 6 | |
| 8 | Ten Supplementary Analyses to Improve E-commerce Web Sites | 31 |
| 9 | 12 | |
| 10 | 23 | |
| 11 | An empirical comparison of the performance so single-layer algorithms for training threshold logic units | 2 |
| 12 | 136 | |
| 13 | Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples | 3 |
| 14 | A Polynomial Time Incremental Algorithm for Regular Grammar Inference | 6 |
| 15 | Constructive Neural Network Learning Algorithms for Multi-Category Real-Valued Pattern Classification | 32 |
| 16 | An incremental interactive algorithm for grammar inference | 12 |
| 17 | Constructive neural network learning algorithms | 0 |
| 18 | Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms | 6 |
| 19 | Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification | 20 |
| 20 | Efficient Learning of Regular Languages Using Teacher-Supplied Positive Samples and Learner-Generated Queries | 3 |
About Rajesh Parekh
Rajesh Parekh is a scholar working on Artificial Intelligence, Information Systems and Marketing, having authored 34 papers that have together received 604 indexed citations. Recurring topics across this work include Neural Networks and Applications (11 papers), Data Mining Algorithms and Applications (6 papers) and Machine Learning and Algorithms (5 papers). The work is most often cited by research in Artificial Intelligence (324 citations), Marketing (82 citations) and Information Systems (146 citations). Rajesh Parekh has collaborated with scholars based in United States, India and China. Frequent co-authors include Vasant Honavar, Jihoon Yang, Ron Kohavi, Llew Mason, Zijian Zheng, Vineet Chaoji, Rushi Bhatt, Angela Han, Adwait Ratnaparkhi and Andrew Hatch. Their work appears in journals such as Machine Learning, Advances in Therapy and Ophthalmology and Therapy.
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.