Smita Nirkhi
- Artificial Intelligence
- Information Systems top 10%
- Signal Processing
- Computer Vision and Pattern Recognition
- Sociology and Political Science
- Co-authors
- R. V. DharaskarV. M. Thakare
- Topics
- Authorship Attribution and Profiling (7 papers)Spam and Phishing Detection (5 papers)Hate Speech and Cyberbullying Detection (4 papers)
- Journals
- International Journal of Advanced Computer Science and ApplicationsProcedia Computer ScienceTransactions on Machine Learning and Artificial Intelligence
- Partner nations
- India
In The Last Decade
Smita Nirkhi
11 papers receiving 74 citations
Peers
Comparison fields: 5 of 24
- Artificial Intelligence 60
- Information Systems 52
- Signal Processing 20
- Computer Vision and Pattern Recognition 10
- Sociology and Political Science 9
Countries citing papers authored by Smita Nirkhi
This map shows the geographic impact of Smita Nirkhi'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 Smita Nirkhi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Smita Nirkhi more than expected).
Fields of papers citing papers by Smita Nirkhi
This network shows the impact of papers produced by Smita Nirkhi. 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 Smita Nirkhi. The network helps show where Smita Nirkhi may publish in the future.
Co-authorship network of co-authors of Smita Nirkhi
This figure shows the co-authorship network connecting the top 25 collaborators of Smita Nirkhi. A scholar is included among the top collaborators of Smita Nirkhi 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 Smita Nirkhi. Smita Nirkhi 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 | 14 | |
| 3 | An Experimental Study on Authorship Identification for Cyber Forensics | 1 |
| 4 | 3 | |
| 5 | 14 | |
| 6 | Application of Network Forensics for Detection of Web Attack using Neural Network | 2 |
| 7 | Author Identification for E-mail Forensic | 4 |
| 8 | 11 | |
| 9 | A Review of Network Forensics Techniques for the Analysis of Web Based Attack | 2 |
| 10 | 14 | |
| 11 | 20 | |
| 12 | A Survey on Clustering Algorithms for web Applications. | 1 |
| 13 | 0 |
About Smita Nirkhi
Smita Nirkhi is a scholar working on Information Systems, Signal Processing and Artificial Intelligence, having authored 13 papers that have together received 86 indexed citations. Recurring topics across this work include Authorship Attribution and Profiling (7 papers), Spam and Phishing Detection (5 papers) and Hate Speech and Cyberbullying Detection (4 papers). The work is most often cited by research in Information Systems (52 citations), Artificial Intelligence (60 citations) and Signal Processing (20 citations). Smita Nirkhi has collaborated with scholars based in India. Frequent co-authors include R. V. Dharaskar and V. M. Thakare. Their work appears in journals such as International Journal of Advanced Computer Science and Applications, Procedia Computer Science and Transactions on Machine Learning and Artificial Intelligence.
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.