Davide Anguita

170 papers receiving 4.9k citations

Hit Papers

A public domain dataset for human activity recognition us...2013202620172021201320152505007501000

Peers

Davide Anguita
Comparison fields: 5 of 171
  • Artificial Intelligence 2.0k
  • Computer Vision and Pattern Recognition 1.9k
  • Electrical and Electronic Engineering 794
  • Computer Networks and Communications 702
  • Control and Systems Engineering 581
Replace Luca Oneto with:
Luca Oneto Italy
Le Zhang China
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Zhikui Chen China
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Davide Anguita relative to Luca Oneto Italy Luca Oneto's profile →
Citations per field
00.5×1.5×1.8×
Luca Oneto · 1×
Citations per year

Countries citing papers authored by Davide Anguita

Since Specialization
Citations

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

Fields of papers citing papers by Davide Anguita

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Davide Anguita

This figure shows the co-authorship network connecting the top 25 collaborators of Davide Anguita. A scholar is included among the top collaborators of Davide Anguita 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 Davide Anguita. Davide Anguita is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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6 18
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Emerging trends in machine learning: beyond conventional methods and data.
1
12 60
13 98
14 9
15 32
16 88
17 9
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A public domain dataset for human activity recognition using smartphonesbreakdown →
1164
19
Human activity recognition on smartphones for mobile context awareness
6
20
Interval discriminant analysis using Support Vector Machines
4

About Davide Anguita

Davide Anguita is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering, having authored 176 papers that have together received 5.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (49 papers), Face and Expression Recognition (39 papers) and Machine Learning and Algorithms (25 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.9k citations), Artificial Intelligence (2.0k citations) and Transportation (240 citations). Davide Anguita has collaborated with scholars based in Italy, United Kingdom and Spain. Frequent co-authors include Luca Oneto, Alessandro Ghio, Sandro Ridella, Xavier Parra, Albert Samà, Andrea Coraddu, Andrea Boni, Luca Ghelardoni, Giancarlo Parodi and Rodolfo Zunino. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Sensors and IEEE Transactions on Smart Grid.

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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