Madhu Shukla
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Y.P. KostaVassilis C. GerogiannisAndreas KanavosBiswaranjan AcharyaRajendrasinh JadejaHiren Kumar ThakkarPriyanka SinghKetan Kotecha
- Topics
- Data Stream Mining Techniques (8 papers)Anomaly Detection Techniques and Applications (7 papers)Time Series Analysis and Forecasting (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessMultimedia Tools and Applications
In The Last Decade
Madhu Shukla
24 papers receiving 198 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 105
- Information Systems 47
- Computer Vision and Pattern Recognition 38
- Computer Networks and Communications 38
- Signal Processing 24
Countries citing papers authored by Madhu Shukla
This map shows the geographic impact of Madhu Shukla'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 Madhu Shukla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madhu Shukla more than expected).
Fields of papers citing papers by Madhu Shukla
This network shows the impact of papers produced by Madhu Shukla. 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 Madhu Shukla. The network helps show where Madhu Shukla may publish in the future.
Co-authorship network of co-authors of Madhu Shukla
This figure shows the co-authorship network connecting the top 25 collaborators of Madhu Shukla. A scholar is included among the top collaborators of Madhu Shukla 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 Madhu Shukla. Madhu Shukla 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 | 2 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 8 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 6 | |
| 14 | 9 | |
| 15 | 33 | |
| 16 | 4 | |
| 17 | 31 | |
| 18 | A survey of outlier detection algorithms for data streams | 7 |
| 19 | Consolidated study & analysis of different clustering techniques for data streams | 1 |
| 20 | 3 |
About Madhu Shukla
Madhu Shukla is a scholar working on Signal Processing, Artificial Intelligence and Software, having authored 31 papers that have together received 216 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (8 papers), Anomaly Detection Techniques and Applications (7 papers) and Time Series Analysis and Forecasting (6 papers). The work is most often cited by research in Artificial Intelligence (105 citations), Human-Computer Interaction (13 citations) and Signal Processing (24 citations). Madhu Shukla has collaborated with scholars based in India, Greece and China. Frequent co-authors include Y.P. Kosta, Vassilis C. Gerogiannis, Andreas Kanavos, Biswaranjan Acharya, Rajendrasinh Jadeja, Hiren Kumar Thakkar, Priyanka Singh, Ketan Kotecha, Debabrata Swain and Santosh Kumar Satapathy. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Multimedia Tools and Applications.
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.