Nirmala Sharma
- Artificial Intelligence top 10%
- Industrial and Manufacturing Engineering top 5%
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computational Theory and Mathematics
- Co-authors
- Ajay SharmaHarish SharmaAnnapurna BhargavaJagdish Chand BansalSanjay DwivediGajendra SinghNaveen ChandraSubarna Shakya
- Topics
- Metaheuristic Optimization Algorithms Research (27 papers)Evolutionary Algorithms and Applications (15 papers)Advanced Multi-Objective Optimization Algorithms (10 papers)
- Cited by
- Industrial and Manufacturing EngineeringArtificial IntelligenceComputational Theory and Mathematics
In The Last Decade
Nirmala Sharma
34 papers receiving 228 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 128
- Industrial and Manufacturing Engineering 63
- Electrical and Electronic Engineering 52
- Control and Systems Engineering 34
- Computational Theory and Mathematics 34
Countries citing papers authored by Nirmala Sharma
This map shows the geographic impact of Nirmala Sharma'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 Nirmala Sharma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nirmala Sharma more than expected).
Fields of papers citing papers by Nirmala Sharma
This network shows the impact of papers produced by Nirmala Sharma. 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 Nirmala Sharma. The network helps show where Nirmala Sharma may publish in the future.
Co-authorship network of co-authors of Nirmala Sharma
This figure shows the co-authorship network connecting the top 25 collaborators of Nirmala Sharma. A scholar is included among the top collaborators of Nirmala Sharma 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 Nirmala Sharma. Nirmala Sharma 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 | 1 | |
| 3 | 6 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 7 | |
| 8 | 2 | |
| 9 | 63 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 3 | |
| 13 | 0 | |
| 14 | 3 | |
| 15 | 4 | |
| 16 | 9 | |
| 17 | 19 | |
| 18 | 32 | |
| 19 | 2 | |
| 20 | Design of High Performance Digital Fir Filter Using Distributed Arithmetic Algorithm with Residue Number System | 0 |
About Nirmala Sharma
Nirmala Sharma is a scholar working on Artificial Intelligence, Industrial and Manufacturing Engineering and Computational Theory and Mathematics, having authored 37 papers that have together received 234 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (27 papers), Evolutionary Algorithms and Applications (15 papers) and Advanced Multi-Objective Optimization Algorithms (10 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (63 citations), Artificial Intelligence (128 citations) and Computational Theory and Mathematics (34 citations). Nirmala Sharma has collaborated with scholars based in India and Nepal. Frequent co-authors include Ajay Sharma, Harish Sharma, Harish Sharma, Annapurna Bhargava, Jagdish Chand Bansal, Sanjay Dwivedi, Gajendra Singh, Naveen Chandra, Subarna Shakya and Priya Sharma. Their work appears in journals such as Applied Soft Computing, International Journal of Systems Science and IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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.