Francesco Gargiulo

1.4k total citations
64 papers, 935 citations indexed

About

Francesco Gargiulo is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Francesco Gargiulo has authored 64 papers receiving a total of 935 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 8 papers in Information Systems and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Francesco Gargiulo's work include Topic Modeling (12 papers), Natural Language Processing Techniques (8 papers) and Text and Document Classification Technologies (6 papers). Francesco Gargiulo is often cited by papers focused on Topic Modeling (12 papers), Natural Language Processing Techniques (8 papers) and Text and Document Classification Technologies (6 papers). Francesco Gargiulo collaborates with scholars based in Italy, Vietnam and Japan. Francesco Gargiulo's co-authors include Stefano Silvestri, Mario Ciampi, Giuseppe De Pietro, Carlo Sansone, Massimo Esposito, Angiola Spinetti, Massimo Puoti, Valeria Putzolu, S. Zaltron and Giampiero Carosi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Scientific Reports and The Journal of Infectious Diseases.

In The Last Decade

Francesco Gargiulo

54 papers receiving 903 citations

Peers

Francesco Gargiulo

HEPAT

EPIDE

Wei Ju China

Rohit Anand India

Andrea Campagner Italy

Yu‐Chi Chen Taiwan

Md Ekramul Hossain Australia

Eva Iglesias Spain

Masood Ahmad Pakistan

Zhendong Wu China

Patricia Fernández Spain

Wei Ju China

Francesco Gargiulo

597 ×1.6

72 ×0.4

HEPAT

62 ×0.3

EPIDE

207 ×2.1

17 ×0.2

Citations per year, relative to Francesco Gargiulo Francesco Gargiulo (= 1×) peers Wei Ju

Countries citing papers authored by Francesco Gargiulo

Since Specialization

Citations

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

Fields of papers citing papers by Francesco Gargiulo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesco Gargiulo

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

All Works

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20 of 20 papers shown

Ciani, Daniele, et al.. (2024). Exploring research trends in modern machine learning through a statistical mechanics lens: An Ising-inspired modeling perspective. Reviews in Mathematical Physics. 37(6).

Gargiulo, Francesco, et al.. (2024). The effects of quantum hardware properties on the performances of variational quantum learning algorithms. Quantum Machine Intelligence. 6(1). 6 indexed citations

Silvestri, Stefano, Francesco Gargiulo, & Mario Ciampi. (2023). Integrating PubMed Label Hierarchy Knowledge into a Complex Hierarchical Deep Neural Network. Applied Sciences. 13(24). 13117–13117. 1 indexed citations

Aldewereld, Huib, Stefano Marrone, Carlo Sansone, et al.. (2023). The European AI Tango: Balancing Regulation Innovation and Competitiveness. 2–8. 6 indexed citations

Gargiulo, Francesco, et al.. (2023). Best practices for portfolio optimization by quantum computing, experimented on real quantum devices. Scientific Reports. 13(1). 19434–19434. 15 indexed citations

Cicala, Luca, et al.. (2023). Progressive monitoring of micro-dumps: a case study over the Campania Region (Italy). 5–5. 2 indexed citations

Silvestri, Stefano & Francesco Gargiulo. (2022). Big Data for eHealth Applications.

Silvestri, Stefano & Francesco Gargiulo. (2022). Special Issue on Big Data for eHealth Applications. Applied Sciences. 12(15). 7578–7578.

Catelli, Rosario, Francesco Gargiulo, Valentina Casola, et al.. (2021). A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records. IEEE Access. 9. 19097–19110. 24 indexed citations

10.

Catelli, Rosario, Francesco Gargiulo, Valentina Casola, et al.. (2020). Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set. Applied Soft Computing. 97. 106779–106779. 56 indexed citations

11.

Gargiulo, Francesco, Stefano Silvestri, & Mario Ciampi. (2017). A Big Data architecture for knowledge discovery in PubMed articles. 82–87. 11 indexed citations

12.

Gargiulo, Francesco, Antonio Fratini, Mario Sansone, & Carlo Sansone. (2015). Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction. Computer Methods and Programs in Biomedicine. 121(3). 127–136. 29 indexed citations

13.

Amato, Flora, Francesco Gargiulo, Vincenzo Moscato, Fabio Persia, & Antonio Picariello. (2014). Recommendation of Multimedia Objects for Social Network Applications. View. 288–293. 4 indexed citations

14.

Ferrara, L, et al.. (2014). Low-lipid diet reduces frequency and severity of acute migraine attacks. Nutrition Metabolism and Cardiovascular Diseases. 25(4). 370–375. 55 indexed citations

15.

Lega, Massimiliano, et al.. (2013). IDES project: an advanced tool to investigate illegal dumping. WIT transactions on ecology and the environment. 1. 603–614. 19 indexed citations

16.

Sansone, Mario, Antonio Fratini, Mario Cesarelli, et al.. (2013). Influence of QT correction on temporal and amplitude features for human identification via ECG. 22–27. 9 indexed citations

17.

Pacioni, D., et al.. (2012). Lifestyle Educational Program Strongly Increases Compliance to Nonpharmacologic Intervention in Hypertensive Patients: A 2‐Year Follow‐Up Study. Journal of Clinical Hypertension. 14(11). 767–772. 17 indexed citations

18.

Tomasoni, Lina Rachele, Carlo Torti, Giuseppe Paraninfo, et al.. (2003). Predictors of Long-Term Immunological Outcome in Rebounding Patients on Protease Inhibitor-Based HAART After Initial Successful Virologic Suppression: Implications for Timing to Switch. HIV Clinical Trials. 4(5). 311–323. 5 indexed citations

19.

Puoti, Massimo, Maurizio Bonacini, Angiola Spinetti, et al.. (2001). Liver Fibrosis Progression Is Related to CD4 Cell Depletion in Patients Coinfected with Hepatitis C Virus and Human Immunodeficiency Virus. The Journal of Infectious Diseases. 183(1). 134–137. 169 indexed citations

20.

Puoti, Massimo, Francesco Gargiulo, Eugenia Quirós-Roldán, et al.. (2000). Liver Damage and Kinetics of Hepatitis C Virus and Human Immunodeficiency Virus Replication during the Early Phases of Combination Antiretroviral Treatment. The Journal of Infectious Diseases. 181(6). 2033–2036. 58 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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