Roberto Corizzo

1.6k total citations
64 papers, 990 citations indexed

About

Roberto Corizzo is a scholar working on Artificial Intelligence, Signal Processing and Electrical and Electronic Engineering. According to data from OpenAlex, Roberto Corizzo has authored 64 papers receiving a total of 990 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 14 papers in Signal Processing and 12 papers in Electrical and Electronic Engineering. Recurrent topics in Roberto Corizzo's work include Anomaly Detection Techniques and Applications (20 papers), Data Stream Mining Techniques (13 papers) and Time Series Analysis and Forecasting (11 papers). Roberto Corizzo is often cited by papers focused on Anomaly Detection Techniques and Applications (20 papers), Data Stream Mining Techniques (13 papers) and Time Series Analysis and Forecasting (11 papers). Roberto Corizzo collaborates with scholars based in United States, Italy and Poland. Roberto Corizzo's co-authors include Michelangelo Ceci, Nathalie Japkowicz, Eftim Zdravevski, Donato Malerba, Petre Lameski, Aleksandra Rashkovska, Colin Bellinger, Bartosz Krawczyk, Gianvito Pio and Vladimir Trajkovik and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

Roberto Corizzo

55 papers receiving 964 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roberto Corizzo United States 18 446 178 159 123 113 64 990
Belal Abuhaija China 9 362 0.8× 150 0.8× 61 0.4× 182 1.5× 57 0.5× 24 1.3k
Giorgio Corani Switzerland 18 437 1.0× 139 0.8× 272 1.7× 67 0.5× 39 0.3× 68 1.1k
Jin Fan China 18 175 0.4× 107 0.6× 119 0.7× 79 0.6× 87 0.8× 72 807
Jinran Wu Australia 19 386 0.9× 466 2.6× 177 1.1× 126 1.0× 46 0.4× 95 1.3k
Hsun-Ping Hsieh Taiwan 11 219 0.5× 112 0.6× 465 2.9× 94 0.8× 164 1.5× 62 1.3k
Choujun Zhan China 17 236 0.5× 173 1.0× 66 0.4× 135 1.1× 29 0.3× 86 1.2k
Konstantinos Demertzis Greece 20 329 0.7× 56 0.3× 84 0.5× 42 0.3× 139 1.2× 51 889
Jie Feng China 19 418 0.9× 199 1.1× 37 0.2× 240 2.0× 369 3.3× 48 2.1k
Filipe Rodrigues Denmark 17 362 0.8× 100 0.6× 50 0.3× 65 0.5× 160 1.4× 62 1.5k
Xiuwen Yi China 15 311 0.7× 148 0.8× 547 3.4× 173 1.4× 342 3.0× 23 2.2k

Countries citing papers authored by Roberto Corizzo

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Corizzo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roberto Corizzo

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Corizzo. A scholar is included among the top collaborators of Roberto Corizzo 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 Roberto Corizzo. Roberto Corizzo 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.
Śnieżyński, Bartłomiej, et al.. (2025). Distance-based change point detection for novelty detection in concept-agnostic continual anomaly detection. Journal of Intelligent Information Systems. 64(1). 37–75.
2.
Ceci, Michelangelo, et al.. (2025). An end-to-end explainability framework for spatio-temporal predictive modeling. Machine Learning. 114(4). 1 indexed citations
3.
Śnieżyński, Bartłomiej, et al.. (2024). pyCLAD: The universal framework for continual lifelong anomaly detection. SoftwareX. 29. 101994–101994.
4.
Chen, Pin‐Yu, Zois Boukouvalas, & Roberto Corizzo. (2024). A deep fusion model for stock market prediction with news headlines and time series data. Neural Computing and Applications. 36(34). 21229–21271. 7 indexed citations
5.
Corizzo, Roberto, et al.. (2024). AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems. 36(5). 8939–8953. 2 indexed citations
6.
Chin, Matthew G. & Roberto Corizzo. (2024). Continual Semi-Supervised Malware Detection. SHILAP Revista de lepidopterología. 6(4). 2829–2854. 1 indexed citations
7.
Wu, Yaning, Nathalie Japkowicz, Sébastien Gilbert, & Roberto Corizzo. (2024). Attention-Based Medical Knowledge Injection in Deep Image Classification Models. 1–8.
8.
Mustafa, Raza Ul, et al.. (2024). Coded Term Discovery for Online Hate Speech Detection. 1–10.
9.
Corizzo, Roberto, et al.. (2024). AD-NEv++ - The multi-architecture neuroevolution-based multivariate anomaly detection framework. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 607–610.
10.
Japkowicz, Nathalie, et al.. (2024). From MNIST to ImageNet and back: benchmarking continual curriculum learning. Machine Learning. 113(10). 8137–8164. 6 indexed citations
11.
Corizzo, Roberto & Jacob Rosén. (2023). Stock market prediction with time series data and news headlines: a stacking ensemble approach. Journal of Intelligent Information Systems. 62(1). 27–56. 19 indexed citations
12.
Japkowicz, Nathalie, et al.. (2023). Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events. Machine Learning. 113(4). 2183–2205. 1 indexed citations
13.
Corizzo, Roberto, et al.. (2023). HURI: Hybrid user risk identification in social networks. World Wide Web. 26(5). 3409–3439. 4 indexed citations
14.
Corizzo, Roberto, et al.. (2023). One-Class Learning for AI-Generated Essay Detection. Applied Sciences. 13(13). 7901–7901. 6 indexed citations
15.
Corizzo, Roberto, et al.. (2023). Lifelong Learning for Anomaly Detection: New Challenges, Perspectives, And Insights. SSRN Electronic Journal. 4 indexed citations
16.
Ding, Lei, et al.. (2022). Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data. 2022 IEEE International Conference on Big Data (Big Data). 45. 5902–5909. 1 indexed citations
17.
Corizzo, Roberto, Michelangelo Ceci, Hadi Fanaee‐T, & João Gama. (2020). Multi-aspect renewable energy forecasting. Information Sciences. 546. 701–722. 60 indexed citations
18.
Kalajdziski, Slobodan, Eftim Zdravevski, Petre Lameski, et al.. (2020). Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review. Biology. 9(12). 453–453. 18 indexed citations
19.
Corizzo, Roberto, et al.. (2019). Pattern and Anomaly Localization in Complex and Dynamic Data. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 1756–1763. 8 indexed citations
20.
Ceci, Michelangelo, et al.. (2014). Big Data Techniques For Renewable Energy Market.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 369–377. 5 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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