Matteo Rucco

678 total citations
23 papers, 323 citations indexed

About

Matteo Rucco is a scholar working on Computational Theory and Mathematics, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matteo Rucco has authored 23 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Theory and Mathematics, 4 papers in Molecular Biology and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matteo Rucco's work include Topological and Geometric Data Analysis (8 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Thyroid Cancer Diagnosis and Treatment (3 papers). Matteo Rucco is often cited by papers focused on Topological and Geometric Data Analysis (8 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Thyroid Cancer Diagnosis and Treatment (3 papers). Matteo Rucco collaborates with scholars based in Italy, Spain and United Kingdom. Matteo Rucco's co-authors include Emanuela Merelli, Luca Tesei, Marco Piangerelli, Rocı́o González-Dı́az, P.M.A. Sloot, Roberto Negro, Maurilio Deandrea, Lorenzo Falsetti, Cinzia Nitti and Gabriele Greco and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and European Heart Journal.

In The Last Decade

Matteo Rucco

22 papers receiving 312 citations

Peers

Matteo Rucco

CTM

RNMI

EDM

SURGE

Lichy Han United States

Yiming Zhou China

Sören Richard Stahlschmidt Sweden

Francesco Calimeri Italy

Randall D. Britten New Zealand

Robert Reynolds United States

T. T. Trang Vietnam

L.S. Goodenday United States

Hölger Hoefling Switzerland

Jaume Coll‐Font United States

Lichy Han United States

Matteo Rucco

13 ×0.1

CTM

76 ×1.1

RNMI

78 ×1.6

8 ×0.2

EDM

27 ×0.7

SURGE

Citations per year, relative to Matteo Rucco Matteo Rucco (= 1×) peers Lichy Han

Countries citing papers authored by Matteo Rucco

Since Specialization

Citations

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

Fields of papers citing papers by Matteo Rucco

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matteo Rucco

This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Rucco. A scholar is included among the top collaborators of Matteo Rucco 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 Matteo Rucco. Matteo Rucco 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

González-Dı́az, Rocı́o, et al.. (2024). An in-depth analysis of data reduction methods for sustainable deep learning. Open Research Europe. 4. 101–101. 1 indexed citations

González-Dı́az, Rocı́o, et al.. (2024). An in-depth analysis of data reduction methods for sustainable deep learning. SHILAP Revista de lepidopterología. 4. 101–101.

Negro, Roberto, Matteo Rucco, & Gabriele Greco. (2024). Laser ablation for spongiform thyroid nodules: identifying baseline nodule volume and energy to be delivered for successful treatment. Endocrine. 86(1). 310–314. 1 indexed citations

Cambiaso, Enrico, et al.. (2022). On the Intersection of Explainable and Reliable AI for Physical Fatigue Prediction. IEEE Access. 10. 76243–76260. 10 indexed citations

Falsetti, Lorenzo, Matteo Rucco, Marco Proietti, et al.. (2021). Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation. Scientific Reports. 11(1). 18925–18925. 14 indexed citations

Negro, Roberto, Gabriele Greco, Maurilio Deandrea, Matteo Rucco, & Pierpaolo Trimboli. (2020). Twelve-Month Volume Reduction Ratio Predicts Regrowth and Time to Regrowth in Thyroid Nodules Submitted to Laser Ablation: A 5-Year Follow-Up Retrospective Study. Korean Journal of Radiology. 21(6). 764–764. 29 indexed citations

Negro, Roberto, Matteo Rucco, Alberto Mormile, et al.. (2019). Machine Learning Prediction of Radiofrequency Thermal Ablation Efficacy: A New Option to Optimize Thyroid Nodule Selection. European Thyroid Journal. 9(4). 205–212. 18 indexed citations

Piangerelli, Marco, Matteo Rucco, Luca Tesei, & Emanuela Merelli. (2018). Topological classifier for detecting the emergence of epileptic seizures. BMC Research Notes. 11(1). 392–392. 33 indexed citations

Rucco, Matteo, et al.. (2018). A methodology for part classification with supervised machine learning. Artificial intelligence for engineering design analysis and manufacturing. 33(1). 100–113. 20 indexed citations

10.

Barucci, Andrea, P. Bastiani, S. Fondelli, et al.. (2018). 301. Prostate cancer Radiomics using multiparametric MR imaging: An exploratory study. Physica Medica. 56. 246–246. 7 indexed citations

11.

González-Dı́az, Rocı́o, et al.. (2017). Persistent entropy for separating topological features from noise in vietoris-rips complexes. Journal of Intelligent Information Systems. 52(3). 637–655. 25 indexed citations

12.

Rucco, Matteo, et al.. (2016). Persistent Homology on RNA Secondary Structure Space. 2 indexed citations

13.

Rucco, Matteo, Rocı́o González-Dı́az, María-José Jiménez, et al.. (2016). A new topological entropy-based approach for measuring similarities among piecewise linear functions. Signal Processing. 134. 130–138. 15 indexed citations

14.

Rucco, Matteo, et al.. (2016). Persistent Homology Analysis of RNA. Unicam Scientific Publications (University of Camerino). 4(1). 2 indexed citations

15.

Merelli, Emanuela, et al.. (2016). A topological approach for multivariate time series characterization: the epileptic brain. Unicam Scientific Publications (University of Camerino). 11 indexed citations

16.

Rucco, Matteo, Emanuela Merelli, Damir Herman, et al.. (2015). Using topological data analysis for diagnosis pulmonary embolism. Applied Computer Science. 9(1). 41–55. 11 indexed citations

17.

Rucco, Matteo, Emanuela Merelli, Jeff Johnson, et al.. (2015). Neural hypernetwork approach for pulmonary embolism diagnosis. BMC Research Notes. 8(1). 617–617. 23 indexed citations

18.

Castellano, Antonella, Roberta Rudà, Giorgio De Nunzio, et al.. (2015). Evaluation of low-grade glioma structural changes after chemotherapy using DTI-based histogram analysis and functional diffusion maps. European Radiology. 26(5). 1263–1273. 27 indexed citations

19.

Rucco, Matteo, et al.. (2015). Topological classification of small DC motors. Unicam Scientific Publications (University of Camerino). 7. 192–197. 4 indexed citations

20.

Merelli, Emanuela, et al.. (2014). jHoles: A Tool for Understanding Biological Complex Networks via Clique Weight Rank Persistent Homology. Electronic Notes in Theoretical Computer Science. 306. 5–18. 29 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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