Nasimul Noman
- Artificial Intelligence top 1%
- Electrical and Electronic Engineering top 10%
- Computational Theory and Mathematics top 1%
- Molecular Biology
- Control and Systems Engineering top 5%
- Co-authors
- Hitoshi IbaPablo MoscatoRaymond ChiongMehdi AbediRui ZhangAshis Kumer BiswasRegina BerrettaM. Shahjahan Kabir
- Topics
- Evolutionary Algorithms and Applications (24 papers)Gene Regulatory Network Analysis (19 papers)Metaheuristic Optimization Algorithms Research (16 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceIndustrial and Manufacturing Engineering
- Partner nations
- JapanAustraliaBangladesh
In The Last Decade
Nasimul Noman
57 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Artificial Intelligence 873
- Electrical and Electronic Engineering 465
- Computational Theory and Mathematics 460
- Molecular Biology 445
- Control and Systems Engineering 162
Countries citing papers authored by Nasimul Noman
This map shows the geographic impact of Nasimul Noman'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 Nasimul Noman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nasimul Noman more than expected).
Fields of papers citing papers by Nasimul Noman
This network shows the impact of papers produced by Nasimul Noman. 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 Nasimul Noman. The network helps show where Nasimul Noman may publish in the future.
Co-authorship network of co-authors of Nasimul Noman
This figure shows the co-authorship network connecting the top 25 collaborators of Nasimul Noman. A scholar is included among the top collaborators of Nasimul Noman 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 Nasimul Noman. Nasimul Noman 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 | 0 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 4 | |
| 6 | 7 | |
| 7 | Evolutionary Computation in Gene Regulatory Network Research | 5 |
| 8 | 6 | |
| 9 | 54 | |
| 10 | 25 | |
| 11 | 1 | |
| 12 | 14 | |
| 13 | Informative Motif Detection Using Data Mining | 1 |
| 14 | 12 | |
| 15 | 4 | |
| 16 | 58 | |
| 17 | 67 | |
| 18 | 95 | |
| 19 | A memetic algorithm for reconstructing gene regulatory networks from expression profile | 4 |
| 20 | 0 |
About Nasimul Noman
Nasimul Noman is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Modeling and Simulation, having authored 63 papers that have together received 1.9k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (24 papers), Gene Regulatory Network Analysis (19 papers) and Metaheuristic Optimization Algorithms Research (16 papers). The work is most often cited by research in Computational Theory and Mathematics (460 citations), Artificial Intelligence (873 citations) and Industrial and Manufacturing Engineering (132 citations). Nasimul Noman has collaborated with scholars based in Japan, Australia and Bangladesh. Frequent co-authors include Hitoshi Iba, Pablo Moscato, Raymond Chiong, Mehdi Abedi, Rui Zhang, Ashis Kumer Biswas, Regina Berretta, M. Shahjahan Kabir, Mohammad Nazmul Haque and Danushka Bollegala. Their work appears in journals such as Blood, PLoS ONE and Expert Systems with 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.