Daniel Tchiotsop
Impact in
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- Chaos control and synchronization
- stochastic dynamics and bifurcation
- Cognitive Neuroscience top 10%
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
Papers in
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- Digital Imaging for Blood Diseases 11
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- Neural Networks and Applications 6
- AI in cancer detection 6
- Co-authors
- A. Nguomkam Negou (3 shared papers)Jacques Kengne (3 shared papers)Didier Wolf (14 shared papers)Hilaire Bertrand Fotsin (4 shared papers)Zeric Tabekoueng Njitacke (1 shared paper)Aurelle Tchagna Kouanou (8 shared papers)Valérie Louis-Dorr (7 shared papers)Michel Noubom (4 shared papers)
In The Last Decade
Daniel Tchiotsop
44 papers receiving 758 citations
Peers
Comparison fields: 5 of 108
- Statistical and Nonlinear Physics 317
- Cognitive Neuroscience 193
- Computer Vision and Pattern Recognition 183
- Computer Networks and Communications 200
- Signal Processing 67
Countries citing papers authored by Daniel Tchiotsop
This map shows the geographic impact of Daniel Tchiotsop'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 Daniel Tchiotsop with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Tchiotsop more than expected).
Fields of papers citing papers by Daniel Tchiotsop
This network shows the impact of papers produced by Daniel Tchiotsop. 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 Daniel Tchiotsop. The network helps show where Daniel Tchiotsop may publish in the future.
Co-authors
The 19 scholars most cited alongside Daniel Tchiotsop, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 164 | |
| 2 | 2016 | 142 | |
| 3 | 2021 | 66 | |
| 4 | 2021 | 47 | |
| 5 | 2018 | 42 | |
| 6 | 2020 | 38 | |
| 7 | 2021 | 23 | |
| 8 | 2019 | 22 | |
| 9 | 2020 | 20 | |
| 10 | 2021 | 19 | |
| 11 | 2007 | 18 | |
| 12 | 2018 | 17 | |
| 13 | 2018 | 16 | |
| 14 | 2020 | 16 | |
| 15 | 2018 | 14 | |
| 16 | 2019 | 12 | |
| 17 | 2015 | 12 | |
| 18 | 2021 | 12 | |
| 19 | 2015 | 11 | |
| 20 | 2020 | 10 |
About Daniel Tchiotsop
Daniel Tchiotsop is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Radiology, Nuclear Medicine and Imaging and Cognitive Neuroscience, having authored 46 papers that have together received 790 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (11 papers), Blind Source Separation Techniques (11 papers), EEG and Brain-Computer Interfaces (10 papers), ECG Monitoring and Analysis (7 papers), Neural Networks and Applications (6 papers), AI in cancer detection (6 papers), COVID-19 diagnosis using AI (5 papers) and Image Processing Techniques and Applications (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (317 citations), Cognitive Neuroscience (193 citations), Computer Vision and Pattern Recognition (183 citations), Computer Networks and Communications (200 citations) and Signal Processing (67 citations). Daniel Tchiotsop has collaborated with scholars based in Cameroon, France and Rwanda. Frequent co-authors include A. Nguomkam Negou, Jacques Kengne, Didier Wolf, Hilaire Bertrand Fotsin, Zeric Tabekoueng Njitacke, Aurelle Tchagna Kouanou, Valérie Louis-Dorr, Michel Noubom, Réné Tchinda and Godpromesse Kenné. Their work appears in journals such as Chaos Solitons & Fractals, Heliyon, Informatics in Medicine Unlocked, Physical and Engineering Sciences in Medicine and Nonlinear Dynamics.
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.