B. Cannas

4.1k citations
125 papers · 1.9k · h-index 23

Impact in

Papers in

B. Cannas

117 papers receiving 1.8k citations

Peers

B. Cannas
Comparison fields: 5 of 103
  • Nuclear and High Energy Physics 728
  • Environmental Engineering 322
  • Statistical and Nonlinear Physics 193
  • Water Science and Technology 212
  • Signal Processing 158
Replace Alessandra Fanni with:
Alessandra Fanni Italy
G. Sias Italy
Ann Almgren United States
Georgios C. Anagnostopoulos United States
Huan Liu China
Alexander D. Poularikas United States
Erçan E. Kuruoğlu Italy
Jun Lin China
Yangkang Chen United States
F. R. de Hoog Australia
B. Cannas relative to Alessandra Fanni Italy Alessandra Fanni's profile →
Citations per field
00.5×3.5×
Alessandra Fanni · 1×
Citations per year

Countries citing papers authored by B. Cannas

Since Specialization
Citations

This map shows the geographic impact of B. Cannas'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 B. Cannas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites B. Cannas more than expected).

Fields of papers citing papers by B. Cannas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by B. Cannas. 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 B. Cannas. The network helps show where B. Cannas may publish in the future.

Co-authors

The 25 scholars most cited alongside B. Cannas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with B. Cannas Line = papers co-authored together B. Cannas links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 125 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006234
2 2021111
3 2002105
4 200376
5 201072
6 200772
7 200367
8
River flow forecasting using neural networks and wavelet analysis
200551
9 201950
10 200144
11 201842
12 202234
13 201833
14 200732
15 201931
16 200431
17 201130
18 201328
19 201827
20 201527

About B. Cannas

B. Cannas is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Electrical and Electronic Engineering, Aerospace Engineering and Computer Networks and Communications, having authored 125 papers that have together received 1.9k indexed citations. Recurring topics across this work include Magnetic confinement fusion research (41 papers), Neural Networks and Applications (19 papers), Anomaly Detection Techniques and Applications (17 papers), Chaos control and synchronization (16 papers), Time Series Analysis and Forecasting (14 papers), Nuclear reactor physics and engineering (10 papers), Geophysical Methods and Applications (10 papers) and Quantum chaos and dynamical systems (9 papers). The work is most often cited by research in Nuclear and High Energy Physics (728 citations), Environmental Engineering (322 citations), Statistical and Nonlinear Physics (193 citations), Water Science and Technology (212 citations) and Signal Processing (158 citations). B. Cannas has collaborated with scholars based in Italy, Germany and United Kingdom. Frequent co-authors include Alessandra Fanni, G. Sias, Silvano Cincotti, P. Sonato, Linda See, A. Murari, A. Pau, F. Pisano, Sara Carcangiu and G. Pautasso. Their work appears in journals such as Nuclear Fusion, Fusion Engineering and Design, Plasma Physics and Controlled Fusion, IEEE Transactions on Plasma Science and Chaos Solitons & Fractals.

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.

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