Pietro Ducange

3.1k total citations · 1 hit paper
97 papers, 2.1k citations indexed

About

Pietro Ducange is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Pietro Ducange has authored 97 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Artificial Intelligence, 17 papers in Information Systems and 13 papers in Computer Networks and Communications. Recurrent topics in Pietro Ducange's work include Fuzzy Logic and Control Systems (25 papers), Metaheuristic Optimization Algorithms Research (21 papers) and Evolutionary Algorithms and Applications (20 papers). Pietro Ducange is often cited by papers focused on Fuzzy Logic and Control Systems (25 papers), Metaheuristic Optimization Algorithms Research (21 papers) and Evolutionary Algorithms and Applications (20 papers). Pietro Ducange collaborates with scholars based in Italy, Spain and United States. Pietro Ducange's co-authors include Francesco Marcelloni, Beatrice Lazzerini, Michela Antonelli, Eleonora D’Andrea, Alessandro Renda, Alessio Bechini, Michela Fazzolari, Riccardo Pecori, Armando Segatori and Massimo Vecchio and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information Sciences.

In The Last Decade

Pietro Ducange

83 papers receiving 2.0k citations

Hit Papers

Real-Time Detection of Traffic From Twitter Stream Analysis 2015 2026 2018 2022 2015 50 100 150 200

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pietro Ducange Italy 25 1.4k 287 221 194 188 97 2.1k
Xiaoyan Zhu China 31 1.6k 1.2× 309 1.1× 148 0.7× 79 0.4× 135 0.7× 141 4.0k
Fei Hao China 26 853 0.6× 534 1.9× 151 0.7× 263 1.4× 148 0.8× 167 2.2k
Jorge Casillas Spain 25 1.6k 1.2× 233 0.8× 145 0.7× 210 1.1× 86 0.5× 78 2.4k
Sadok Ben Yahia Tunisia 19 612 0.5× 618 2.2× 103 0.5× 326 1.7× 107 0.6× 188 1.6k
Gongqing Wu China 13 1.1k 0.8× 753 2.6× 123 0.6× 100 0.5× 84 0.4× 55 2.2k
Zhiqiu Huang China 23 749 0.6× 999 3.5× 171 0.8× 203 1.0× 150 0.8× 241 2.1k
Alberto Cano United States 30 1.7k 1.3× 448 1.6× 174 0.8× 173 0.9× 30 0.2× 93 2.6k
Pranab K. Muhuri India 24 735 0.5× 218 0.8× 148 0.7× 225 1.2× 45 0.2× 114 2.3k
Giuseppe Fenza Italy 21 768 0.6× 487 1.7× 119 0.5× 160 0.8× 190 1.0× 88 1.5k
Marcin Paprzycki Poland 17 550 0.4× 384 1.3× 126 0.6× 148 0.8× 88 0.5× 215 1.8k

Countries citing papers authored by Pietro Ducange

Since Specialization
Citations

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

Fields of papers citing papers by Pietro Ducange

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pietro Ducange

This figure shows the co-authorship network connecting the top 25 collaborators of Pietro Ducange. A scholar is included among the top collaborators of Pietro Ducange 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 Pietro Ducange. Pietro Ducange 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
2.
Ducange, Pietro, et al.. (2025). Leveraging Explainable AI for 3-D Geometry-Based Channel Status Prediction in UAV-Assisted Communication Networks. IEEE Open Journal of the Communications Society. 6. 7256–7269.
3.
Ducange, Pietro, et al.. (2025). A Comparative Analysis of Models for Real-Time Personal Protective Equipment Detection on Edge Devices. CINECA IRIS Institutial research information system (University of Pisa). 1–8.
4.
Ducange, Pietro, et al.. (2024). Trustworthy AI in Heterogeneous Settings: Federated Learning of Explainable Classifiers. CINECA IRIS Institutial research information system (University of Pisa). 1–9.
5.
Marcelloni, Francesco, et al.. (2024). Federated $c$-Means and Fuzzy $c$-Means Clustering Algorithms for Horizontally and Vertically Partitioned Data. IEEE Transactions on Artificial Intelligence. 5(12). 6426–6441. 4 indexed citations
6.
Ducange, Pietro, et al.. (2024). Nets4Learning: A Web Platform for Designing and Testing ANN/DNN Models. Electronics. 13(22). 4378–4378. 1 indexed citations
7.
Marcelloni, Francesco, et al.. (2024). On Predicting Spare Parts for Field Services by Leveraging Fault Description and Historical Repairing Data. IEEE Access. 12. 162756–162768.
8.
Nardini, Giovanni, et al.. (2023). Exploiting Simu5G for generating datasets for training and testing AI models for 5G/6G network applications. SoftwareX. 21. 101320–101320. 8 indexed citations
10.
Bechini, Alessio, et al.. (2022). A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting. ACM Transactions on Knowledge Discovery from Data. 16(6). 1–29. 4 indexed citations
11.
Renda, Alessandro, Pietro Ducange, Francesco Marcelloni, et al.. (2022). Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking. Information. 13(8). 395–395. 52 indexed citations
12.
Burgos, Daniel, Pietro Ducange, Pierpaolo Limone, et al.. (2021). Bridges and Mediation in Higher Distance Education: HELMeTO 2020 Report. Education Sciences. 11(7). 334–334. 4 indexed citations
13.
Bechini, Alessio, Alessandro Bondielli, Pietro Ducange, Francesco Marcelloni, & Alessandro Renda. (2021). Addressing Event-Driven Concept Drift in Twitter Stream: A Stance Detection Application. IEEE Access. 9. 77758–77770. 18 indexed citations
14.
Tavoschi, Lara, Filippo Quattrone, Eleonora D’Andrea, et al.. (2020). Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy. Human Vaccines & Immunotherapeutics. 16(5). 1062–1069. 86 indexed citations
15.
Bechini, Alessio, Pietro Ducange, Francesco Marcelloni, & Alessandro Renda. (2020). Stance Analysis of Twitter Users: The Case of the Vaccination Topic in Italy. IEEE Intelligent Systems. 36(5). 131–139. 12 indexed citations
17.
D’Andrea, Eleonora, Pietro Ducange, Alessio Bechini, Alessandro Renda, & Francesco Marcelloni. (2018). Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Systems with Applications. 116. 209–226. 111 indexed citations
18.
Antonelli, Michela, Pietro Ducange, Beatrice Lazzerini, & Francesco Marcelloni. (2015). Multi-objective evolutionary design of granular rule-based classifiers. Granular Computing. 1(1). 37–58. 85 indexed citations
19.
Antonelli, Michela, Pietro Ducange, Francesco Marcelloni, & Armando Segatori. (2015). On the influence of feature selection in fuzzy rule-based regression model generation. Information Sciences. 329. 649–669. 32 indexed citations
20.
Antonelli, Michela, Pietro Ducange, Beatrice Lazzerini, & Francesco Marcelloni. (2009). Learning Concurrently Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Systems. CINECA IRIS Institutial research information system (University of Pisa). 1033–1038. 4 indexed citations

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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