Nikola Kasabov
- Artificial Intelligence top 0.1%
- Electrical and Electronic Engineering top 1%
- Cognitive Neuroscience top 0.5%
- Computer Vision and Pattern Recognition top 0.5%
- Control and Systems Engineering top 1%
- Co-authors
- Qun SongShaoning PangJie YangStefan SchliebsMaryam DoborjehSeiichi OzawaZohreh DoborjehĽubica Beňušková
- Topics
- Neural Networks and Applications (118 papers)Advanced Memory and Neural Computing (82 papers)Neural dynamics and brain function (77 papers)
- Partner nations
- New ZealandChinaUnited Kingdom
In The Last Decade
Nikola Kasabov
424 papers receiving 9.8k citations
Hit Papers
Peers
Comparison fields: 5 of 204
- Artificial Intelligence 4.9k
- Electrical and Electronic Engineering 2.5k
- Cognitive Neuroscience 2.5k
- Computer Vision and Pattern Recognition 1.5k
- Control and Systems Engineering 786
Countries citing papers authored by Nikola Kasabov
This map shows the geographic impact of Nikola Kasabov'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 Nikola Kasabov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikola Kasabov more than expected).
Fields of papers citing papers by Nikola Kasabov
This network shows the impact of papers produced by Nikola Kasabov. 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 Nikola Kasabov. The network helps show where Nikola Kasabov may publish in the future.
Co-authorship network of co-authors of Nikola Kasabov
This figure shows the co-authorship network connecting the top 25 collaborators of Nikola Kasabov. A scholar is included among the top collaborators of Nikola Kasabov 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 Nikola Kasabov. Nikola Kasabov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 7 | |
| 7 | 10 | |
| 8 | 7 | |
| 9 | 10 | |
| 10 | 19 | |
| 11 | 4 | |
| 12 | 12 | |
| 13 | 6 | |
| 14 | 1 | |
| 15 | 4 | |
| 16 | Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum-inspired Particle Swarm Optimization | 13 |
| 17 | 105 | |
| 18 | Genetic Algorithms for the Design of Fuzzy Neural Networks | 9 |
| 19 | Effects of orientation and spatial frequency on monocular and binocular rivalry | 12 |
| 20 | A Filtering Neuron and its Application for Building Connectionist Production Systems | 2 |
About Nikola Kasabov
Nikola Kasabov is a scholar working on Artificial Intelligence, Media Technology and Cognitive Neuroscience, having authored 436 papers that have together received 10.3k indexed citations. Recurring topics across this work include Neural Networks and Applications (118 papers), Advanced Memory and Neural Computing (82 papers) and Neural dynamics and brain function (77 papers). The work is most often cited by research in Artificial Intelligence (4.9k citations), Cognitive Neuroscience (2.5k citations) and Computer Vision and Pattern Recognition (1.5k citations). Nikola Kasabov has collaborated with scholars based in New Zealand, China and United Kingdom. Frequent co-authors include Qun Song, Shaoning Pang, Jie Yang, Stefan Schliebs, Maryam Doborjeh, Seiichi Ozawa, Zohreh Doborjeh, Ľubica Beňušková, Zhenhong Jia and Jeremiah D. Deng. Their work appears in journals such as SHILAP Revista de lepidopterología, ACS Nano and Stroke.
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.