Anshul Kanakia
- Artificial Intelligence top 5%
- Statistics, Probability and Uncertainty top 2%
- Information Systems top 10%
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Z. ShenYuxiao DongKuansan WangChieh‐Han WuNikolaus CorrellBehrouz TouriJunjie QianAlvin Chen
- Topics
- Semantic Web and Ontologies (2 papers)Scientific Computing and Data Management (2 papers)Modular Robots and Swarm Intelligence (1 paper)
- Cited by
- Statistics, Probability and UncertaintyArtificial IntelligenceStatistical and Nonlinear Physics
- Journals
- SHILAP Revista de lepidopterologíaBMC Medical Informatics and Decision MakingSwarm Intelligence
- Partner nations
- United States
In The Last Decade
Anshul Kanakia
6 papers receiving 433 citations
Hit Papers
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 232
- Statistics, Probability and Uncertainty 99
- Information Systems 78
- Statistical and Nonlinear Physics 71
- Computer Vision and Pattern Recognition 69
Countries citing papers authored by Anshul Kanakia
This map shows the geographic impact of Anshul Kanakia'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 Anshul Kanakia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anshul Kanakia more than expected).
Fields of papers citing papers by Anshul Kanakia
This network shows the impact of papers produced by Anshul Kanakia. 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 Anshul Kanakia. The network helps show where Anshul Kanakia may publish in the future.
Co-authorship network of co-authors of Anshul Kanakia
This figure shows the co-authorship network connecting the top 25 collaborators of Anshul Kanakia. A scholar is included among the top collaborators of Anshul Kanakia 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 Anshul Kanakia. Anshul Kanakia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | Microsoft Academic Graph: When experts are not enoughbreakdown → | 293 |
| 4 | 90 | |
| 5 | 32 | |
| 6 | 22 |
About Anshul Kanakia
Anshul Kanakia is a scholar working on Toxicology, Information Systems and Management and Safety Research, having authored 6 papers that have together received 442 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (2 papers), Scientific Computing and Data Management (2 papers) and Modular Robots and Swarm Intelligence (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (99 citations), Artificial Intelligence (232 citations) and Statistical and Nonlinear Physics (71 citations). Anshul Kanakia has collaborated with scholars based in United States. Frequent co-authors include Z. Shen, Yuxiao Dong, Kuansan Wang, Chieh‐Han Wu, Nikolaus Correll, Behrouz Touri, Junjie Qian, Alvin Chen, Dustin Reishus and Noel Southall. Their work appears in journals such as SHILAP Revista de lepidopterología, BMC Medical Informatics and Decision Making and Swarm 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.