Irene Macaluso

54 papers receiving 964 citations

Hit Papers

An Overview on Application of Machine Learning Techniques...20182026202020232018100200300400

Peers

Irene Macaluso
Comparison fields: 5 of 79
  • Electrical and Electronic Engineering 593
  • Computer Networks and Communications 396
  • Artificial Intelligence 201
  • Control and Systems Engineering 96
  • Computer Vision and Pattern Recognition 66
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Xiao Tang China
Lei Jiao Norway
Zhiyong Du China
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Anwer Al‐Dulaimi United Kingdom
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Ruichen Zhang China
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Countries citing papers authored by Irene Macaluso

Since Specialization
Citations

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

Fields of papers citing papers by Irene Macaluso

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Irene Macaluso

This figure shows the co-authorship network connecting the top 25 collaborators of Irene Macaluso. A scholar is included among the top collaborators of Irene Macaluso 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 Irene Macaluso. Irene Macaluso 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
1 2
2 2
3 5
4 32
5 1
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An Overview on Application of Machine Learning Techniques in Optical Networksbreakdown →
420
7 5
8 11
9 12
10 2
11 7
12 6
13 2
14
Learning Nash Equilibria in Distributed Channel Selection for Frequency-Agile Radios
8
15 17
16 23
17
Machine Consciousness in CiceRobot, a Museum Guide Robot.
2
18
Automatic Landmark Detection and Recognition in Autonomous Robotics
1
19 9
20 17

About Irene Macaluso

Irene Macaluso is a scholar working on Computer Networks and Communications, Human-Computer Interaction and Control and Systems Engineering, having authored 54 papers that have together received 998 indexed citations. Recurring topics across this work include Advanced MIMO Systems Optimization (16 papers), Cognitive Radio Networks and Spectrum Sensing (12 papers) and Cooperative Communication and Network Coding (10 papers). The work is most often cited by research in Computer Networks and Communications (396 citations), Electrical and Electronic Engineering (593 citations) and Artificial Intelligence (201 citations). Irene Macaluso has collaborated with scholars based in Ireland, Italy and United Kingdom. Frequent co-authors include Avishek Nag, Marco Ruffini, Francesco Musumeci, Massimo Tornatore, Darko Zibar, Cristina Rottondi, Linda Doyle, Luiz A. DaSilva, Antonio Chella and Nicola Marchetti. Their work appears in journals such as IEEE Communications Surveys & Tutorials, IEEE Journal on Selected Areas in Communications and IEEE Communications Magazine.

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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