Francesco Camastra

3.2k citations
39 papers · 1.8k indexed · 1 hit paper · h-index 19

Francesco Camastra

35 papers receiving 1.7k citations

Hit Papers

A survey of kernel and spectral methods for clustering5542007202620132019100200300400500

Peers

Francesco Camastra
Comparison fields: 5 of 146
  • Computer Vision and Pattern Recognition 779
  • Artificial Intelligence 900
  • Media Technology 234
  • Signal Processing 223
  • Statistical and Nonlinear Physics 144
Replace Maya R. Gupta with:
Maya R. Gupta United States
Yuanyan Tang China
Ling Tian China
Hadi Sadoghi Yazdi Iran
Timothy C. Havens United States
Fanzhang Li China
Maurizio Filippone United Kingdom
Minnan Luo China
Ji‐Xiang Du China
Seth Rogers United States
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Citations per field
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Citations per year

Countries citing papers authored by Francesco Camastra

Since Specialization
Citations

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

Fields of papers citing papers by Francesco Camastra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 17 scholars most cited alongside Francesco Camastra, 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 Francesco Camastra Line = papers co-authored together Francesco Camastra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20252
2 20250
3 20241
4 202360
5 201825
6 201514
7 20142
8 201218
9 201118
10 20079
11
Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing)
200713
12 200637
13
CURSIVE CHARACTER CHALLENGE: A NEW DATABASE FOR MACHINE LEARNING AND PATTERN RECOGNITION
20053
14 2005225
15
ENHANCING CURSIVE WORD RECOGNITION PERFORMANCE BY THE INTEGRATION OF ALL THE AVAILABLE INFORMATION
20041
16 2003179
17 2002132
18
Cursive character recognition based on neural nets
19990
19
Isolated Cursive Character Recognition based on Neural Nets.
19992
20 199917

About Francesco Camastra

Francesco Camastra is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing and Environmental Engineering, having authored 39 papers that have together received 1.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Handwritten Text Recognition Techniques (8 papers), Image Retrieval and Classification Techniques (7 papers), Chaos control and synchronization (6 papers), Air Quality Monitoring and Forecasting (3 papers), Time Series Analysis and Forecasting (3 papers), Image Processing and 3D Reconstruction (3 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (779 citations), Artificial Intelligence (900 citations), Media Technology (234 citations), Signal Processing (223 citations) and Statistical and Nonlinear Physics (144 citations). Francesco Camastra has collaborated with scholars based in Italy, Switzerland and United States. Frequent co-authors include Alessandro Vinciarelli, Maurizio Filippone, Francesco Masulli, Stefano Rovetta, Alessandro Verri, Antonino Staiano, Angelo Ciaramella, Angelo Riccio, Valentina Rastelli and A.M. Colla. Their work appears in journals such as Ecological Informatics, Pattern Recognition, Neural Computing and Applications, Expert Systems with Applications and IEEE Transactions on Pattern Analysis and Machine Intelligence.

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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