Alessandro Renda

731 citations
29 papers · 455 · h-index 11

Impact in

    • Privacy-Preserving Technologies in Data
    • Explainable Artificial Intelligence (XAI)
    • Sentiment Analysis and Opinion Mining
    • Data Stream Mining Techniques

Papers in

    • Privacy-Preserving Technologies in Data 12
    • Explainable Artificial Intelligence (XAI) 11
    • Data Stream Mining Techniques 5
    • Advanced Clustering Algorithms Research 4
    • Adversarial Robustness in Machine Learning 3
    • Spam and Phishing Detection 3

Alessandro Renda

27 papers receiving 441 citations

Peers

Alessandro Renda
Comparison fields: 5 of 80
  • Health Informatics 22
  • Artificial Intelligence 267
  • Statistical and Nonlinear Physics 46
  • Computer Vision and Pattern Recognition 59
  • Health 19
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Citations per year

Countries citing papers authored by Alessandro Renda

Since Specialization
Citations

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

Fields of papers citing papers by Alessandro Renda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Alessandro Renda, 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 Alessandro Renda Line = papers co-authored together Alessandro Renda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018121
2 202264
3 201948
4 202044
5 202331
6 202221
7 202120
8 202119
9 202015
10 202411
11 202410
12 20238
13 20238
14 20246
15 20225
16 20195
17 20224
18 20222
19 20242
20 20252

About Alessandro Renda

Alessandro Renda is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Health Informatics and Signal Processing, having authored 29 papers that have together received 455 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (12 papers), Explainable Artificial Intelligence (XAI) (11 papers), Data Stream Mining Techniques (5 papers), Advanced Clustering Algorithms Research (4 papers), Artificial Intelligence in Healthcare and Education (4 papers), Adversarial Robustness in Machine Learning (3 papers), Data Management and Algorithms (3 papers) and Spam and Phishing Detection (3 papers). The work is most often cited by research in Health Informatics (22 citations), Artificial Intelligence (267 citations), Statistical and Nonlinear Physics (46 citations), Computer Vision and Pattern Recognition (59 citations) and Health (19 citations). Alessandro Renda has collaborated with scholars based in Italy, Germany and Switzerland. Frequent co-authors include Francesco Marcelloni, Alessio Bechini, Pietro Ducange, Eleonora D’Andrea, Giovanni Stea, Giovanni Nardini, Antonio Virdis, F. Ruffini, Dario Sabella and Leonardo Gomes Baltar. Their work appears in journals such as Expert Systems with Applications, IEEE Access, IEEE Intelligent Systems, Computer Communications and IEEE Transactions on Fuzzy Systems.

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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