Gourav Kumar
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
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- Financial Markets and Investment Strategies
Papers in
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- Stock Market Forecasting Methods 6
- Forecasting Techniques and Applications 3
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- Neural Networks and Applications 2
- Co-authors
- Uday Pratap Singh (6 shared papers)J. Krishna Murthy (2 shared papers)Nitish Chopra (1 shared paper)Geetika Arora (1 shared paper)V. K. Panchal (1 shared paper)Shruti Goel (1 shared paper)Kuljeet Singh (1 shared paper)Neerendra Kumar (1 shared paper)
- Journals
- Archives of Computational Methods in Engineering (1 paper)Computational Economics (1 paper)Journal of Intelligent & Robotic Systems (1 paper)Soft Computing (1 paper)International Journal of Intelligent Systems (1 paper)
- Partner nations
- India
In The Last Decade
Gourav Kumar
12 papers receiving 257 citations
Peers
Comparison fields: 5 of 59
- Management Science and Operations Research 154
- Finance 32
- Signal Processing 23
- Economics and Econometrics 56
- Artificial Intelligence 64
Countries citing papers authored by Gourav Kumar
This map shows the geographic impact of Gourav Kumar'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 Gourav Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gourav Kumar more than expected).
Fields of papers citing papers by Gourav Kumar
This network shows the impact of papers produced by Gourav Kumar. 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 Gourav Kumar. The network helps show where Gourav Kumar may publish in the future.
Co-authors
The 8 scholars most cited alongside Gourav Kumar, 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 | 2020 | 124 | |
| 2 | 2022 | 41 | |
| 3 | 2021 | 32 | |
| 4 | 2021 | 12 | |
| 5 | 2021 | 12 | |
| 6 | 2015 | 11 | |
| 7 | 2016 | 11 | |
| 8 | Analytical evaluation for the enhancement of satellite images using swarm intelligence techniques | 2016 | 6 |
| 9 | 2016 | 5 | |
| 10 | 2023 | 3 | |
| 11 | 2014 | 3 | |
| 12 | 2021 | 3 | |
| 13 | 2025 | 0 |
About Gourav Kumar
Gourav Kumar is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering and Economics and Econometrics, having authored 13 papers that have together received 263 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (6 papers), Energy Load and Power Forecasting (3 papers), Forecasting Techniques and Applications (3 papers), Neural Networks and Applications (2 papers), Modular Robots and Swarm Intelligence (2 papers), Complex Systems and Time Series Analysis (2 papers), Robotics and Sensor-Based Localization (2 papers) and Housing Market and Economics (1 paper). The work is most often cited by research in Management Science and Operations Research (154 citations), Finance (32 citations), Signal Processing (23 citations), Economics and Econometrics (56 citations) and Artificial Intelligence (64 citations). Gourav Kumar has collaborated with scholars based in India. Frequent co-authors include Uday Pratap Singh, J. Krishna Murthy, Nitish Chopra, Geetika Arora, V. K. Panchal, Shruti Goel, Kuljeet Singh and Neerendra Kumar. Their work appears in journals such as Archives of Computational Methods in Engineering, Computational Economics, Journal of Intelligent & Robotic Systems, Soft Computing and International Journal of Intelligent Systems.
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.