Massimo De Gregorio

30 papers and 237 indexed citations i.

About

Massimo De Gregorio is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Massimo De Gregorio has authored 30 papers receiving a total of 237 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 5 papers in Electrical and Electronic Engineering. Recurrent topics in Massimo De Gregorio’s work include Neural Networks and Applications (15 papers), Evolutionary Algorithms and Applications (4 papers) and Advanced Memory and Neural Computing (3 papers). Massimo De Gregorio is often cited by papers focused on Neural Networks and Applications (15 papers), Evolutionary Algorithms and Applications (4 papers) and Advanced Memory and Neural Computing (3 papers). Massimo De Gregorio collaborates with scholars based in Italy, Brazil and India. Massimo De Gregorio's co-authors include Maurizio Giordano, Felipe M. G. França, Priscila M. V. Lima, Helen Morton, Igor Aleksander, Silvia Rossi, Mariacarla Staffa, João Gama, W. Oliveira and Vito Di Maio and has published in prestigious journals such as Neurocomputing, Applied Soft Computing and Pattern Recognition Letters.

In The Last Decade

Co-authorship network of co-authors of Massimo De Gregorio i

Fields of papers citing papers by Massimo De Gregorio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Massimo De Gregorio. 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 Massimo De Gregorio. The network helps show where Massimo De Gregorio may publish in the future.

Countries citing papers authored by Massimo De Gregorio

Since Specialization
Citations

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

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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