Silvia Cateni

742 total citations
24 papers, 353 citations indexed

About

Silvia Cateni is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Silvia Cateni has authored 24 papers receiving a total of 353 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 12 papers in Control and Systems Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Silvia Cateni's work include Fault Detection and Control Systems (10 papers), Neural Networks and Applications (7 papers) and Fuzzy Logic and Control Systems (6 papers). Silvia Cateni is often cited by papers focused on Fault Detection and Control Systems (10 papers), Neural Networks and Applications (7 papers) and Fuzzy Logic and Control Systems (6 papers). Silvia Cateni collaborates with scholars based in Italy and France. Silvia Cateni's co-authors include Valentina Colla, Marco Vannucci, Francesca Marchiori, Barbara Fornai, Alessandro Amato, Paola Mantellini, Francesca Martella, Francesca Carozzi, Francesca Scebba and Giacomo Filippo Porzio and has published in prestigious journals such as International Journal of Molecular Sciences, Neurocomputing and Water.

In The Last Decade

Silvia Cateni

23 papers receiving 334 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Silvia Cateni Italy 10 179 60 51 49 46 24 353
Emilio Corchado Spain 8 163 0.9× 33 0.6× 55 1.1× 34 0.7× 46 1.0× 23 332
Pınar Tüfekçi Türkiye 6 185 1.0× 70 1.2× 40 0.8× 37 0.8× 17 0.4× 12 366
An‐Da Li China 8 196 1.1× 49 0.8× 47 0.9× 22 0.4× 63 1.4× 16 355
Shasha Guo China 11 214 1.2× 70 1.2× 29 0.6× 34 0.7× 29 0.6× 15 343
Sait Ali Uymaz Türkiye 9 283 1.6× 28 0.5× 59 1.2× 19 0.4× 49 1.1× 17 424
Mosa E. Hosney Egypt 9 276 1.5× 47 0.8× 68 1.3× 16 0.3× 18 0.4× 12 447
Junbo Jacob Lian China 6 217 1.2× 44 0.7× 65 1.3× 12 0.2× 34 0.7× 15 421
A. J. Umbarkar India 8 162 0.9× 38 0.6× 32 0.6× 19 0.4× 70 1.5× 23 360
Jesús Soto Mexico 7 301 1.7× 79 1.3× 42 0.8× 30 0.6× 11 0.2× 17 508

Countries citing papers authored by Silvia Cateni

Since Specialization
Citations

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

Fields of papers citing papers by Silvia Cateni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Silvia Cateni

This figure shows the co-authorship network connecting the top 25 collaborators of Silvia Cateni. A scholar is included among the top collaborators of Silvia Cateni 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 Silvia Cateni. Silvia Cateni 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
1.
2.
Scebba, Francesca, Stefano Salvadori, Silvia Cateni, et al.. (2023). Top–Down Proteomics of Human Saliva, Analyzed with Logistic Regression and Machine Learning Methods, Reveal Molecular Signatures of Ovarian Cancer. International Journal of Molecular Sciences. 24(21). 15716–15716. 4 indexed citations
3.
4.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2020). A Genetic Algorithm-Based Approach for Selecting Input Variables and Setting Relevant Network Parameters of a SOM-Based Classifier. International Journal of Simulation Systems Science & Technology. 6 indexed citations
5.
Vannucci, Marco, Valentina Colla, & Silvia Cateni. (2017). Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble Method. Neural Processing Letters. 46(3). 791–809. 1 indexed citations
6.
Marchiori, Francesca, et al.. (2017). Integrated Dynamic Energy Management for Steel Production. Energy Procedia. 105. 2772–2777. 14 indexed citations
7.
Cateni, Silvia & Valentina Colla. (2016). Improving the stability of wrapper variable selection applied to binary classification. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 4 indexed citations
8.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2016). A Fuzzy System for Combining Filter Features Selection Methods. International Journal of Fuzzy Systems. 19(4). 1168–1180. 22 indexed citations
9.
Colla, Valentina, et al.. (2016). Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse. Journal of Intelligent Manufacturing. 29(7). 1545–1557. 32 indexed citations
10.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2014). A Hybrid Feature Selection Method for Classification Purposes. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 39–44. 34 indexed citations
11.
Cateni, Silvia, et al.. (2014). A Procedure for Building Reduced Reliable Training Datasets from Real-World Data. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 4 indexed citations
12.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2014). A method for resampling imbalanced datasets in binary classification tasks for real-world problems. Neurocomputing. 135. 32–41. 124 indexed citations
13.
Cateni, Silvia, et al.. (2013). A multivariate fuzzy system applied for outliers detection. Journal of Intelligent & Fuzzy Systems. 24(4). 889–903. 24 indexed citations
14.
Colla, Valentina, et al.. (2013). Genetic Algorithms Applied to Discrete Distribution Fitting. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 26. 30–35. 1 indexed citations
15.
Cateni, Silvia, Marco Vannucci, & Valentina Colla. (2013). Industrial Multiple Criteria Decision Making Problems Handled by Means of Fuzzy Inference-Based Decision Support Systems. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 4 indexed citations
16.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2011). Novel resampling method for the classification of imbalanced datasets for industrial and other real-world problems. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 402–407. 14 indexed citations
17.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2010). Variable Selection through Genetic Algorithms for Classification Purposes. 16 indexed citations
18.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2010). A Genetic Algorithms-based Approach for Selecting the Most Relevant Input Variables in Classification Tasks. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 3. 63–67. 3 indexed citations
19.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2009). General Purpose Input Variables Extraction: A Genetic Algorithm Based Procedure GIVE A GAP. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 3. 1278–1283. 16 indexed citations
20.
Cateni, Silvia, Valentina Colla, & Marco Vannucci. (2007). A fuzzy logic-based method for outliers detection. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 561–566. 13 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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