Massimo Panella

3.1k total citations
146 papers, 2.2k citations indexed

About

Massimo Panella is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Massimo Panella has authored 146 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Artificial Intelligence, 46 papers in Electrical and Electronic Engineering and 24 papers in Computer Vision and Pattern Recognition. Recurrent topics in Massimo Panella's work include Neural Networks and Applications (35 papers), Energy Load and Power Forecasting (27 papers) and Quantum Computing Algorithms and Architecture (15 papers). Massimo Panella is often cited by papers focused on Neural Networks and Applications (35 papers), Energy Load and Power Forecasting (27 papers) and Quantum Computing Algorithms and Architecture (15 papers). Massimo Panella collaborates with scholars based in Italy, United States and Australia. Massimo Panella's co-authors include Antonello Rosato, Simone Scardapane, Rodolfo Araneo, Aurelio Uncini, Antonello Rizzi, Dianhui Wang, Fabio Massimo Frattale Mascioli, Giovanni Martinelli, Amedeo Andreotti and Andrea Gallo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and Remote Sensing of Environment.

In The Last Decade

Massimo Panella

137 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Massimo Panella Italy 25 983 719 320 253 240 146 2.2k
Dongshu Wang China 4 566 0.6× 500 0.7× 225 0.7× 203 0.8× 373 1.6× 7 2.1k
Uday K. Chakraborty United States 23 1.6k 1.6× 482 0.7× 297 0.9× 242 1.0× 356 1.5× 58 2.9k
Qasem Al-Tashi Malaysia 16 946 1.0× 311 0.4× 322 1.0× 181 0.7× 263 1.1× 39 2.1k
Yonghang Tai China 22 777 0.8× 695 1.0× 179 0.6× 167 0.7× 89 0.4× 97 1.9k
Ning Jin China 20 761 0.8× 830 1.2× 364 1.1× 283 1.1× 625 2.6× 97 2.4k
Vojislav Kecman New Zealand 22 695 0.7× 309 0.4× 456 1.4× 88 0.3× 281 1.2× 83 2.5k
Ding Liu China 29 721 0.7× 716 1.0× 995 3.1× 157 0.6× 359 1.5× 252 3.5k
Ke-Lin Du Canada 18 557 0.6× 411 0.6× 205 0.6× 238 0.9× 227 0.9× 53 1.9k
Kejun Wang China 18 1.1k 1.1× 920 1.3× 771 2.4× 133 0.5× 113 0.5× 102 2.3k

Countries citing papers authored by Massimo Panella

Since Specialization
Citations

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

Fields of papers citing papers by Massimo Panella

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Massimo Panella

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Panella. A scholar is included among the top collaborators of Massimo Panella 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 Massimo Panella. Massimo Panella 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.
Hoffmann, Maximilian, et al.. (2025). Data imputation methods for intermittent renewable energy sources: Implications for energy system modeling. Energy Conversion and Management. 339. 119857–119857.
2.
Ceschini, Andrea, et al.. (2025). A hybrid quantum-neural network for heart disease classification. Biomedical Signal Processing and Control. 113. 109185–109185.
3.
Ceschini, Andrea, et al.. (2025). On hybrid quanvolutional neural networks optimization. Quantum Machine Intelligence. 7(1). 3 indexed citations
4.
Panella, Massimo, et al.. (2025). Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models. IRIS Research product catalog (Sapienza University of Rome). 1–5. 2 indexed citations
5.
Rosato, Antonello, et al.. (2024). An explainable fast deep neural network for emotion recognition. Biomedical Signal Processing and Control. 100. 107177–107177. 5 indexed citations
6.
7.
Sebastianelli, Alessandro, Federico Serva, Andrea Ceschini, et al.. (2024). Machine learning forecast of surface solar irradiance from meteo satellite data. Remote Sensing of Environment. 315. 114431–114431. 3 indexed citations
8.
Perilli, Stefano, et al.. (2024). Development of a Wearable Electromyographic Sensor with Aerosol Jet Printing Technology. Bioengineering. 11(12). 1283–1283. 5 indexed citations
9.
Scardapane, Simone, et al.. (2024). On the Exploration of Graph State-Space Models for Spatio-Temporal Renewable Energy Forecasting. IRIS Research product catalog (Sapienza University of Rome). 1–5. 1 indexed citations
10.
Angeletti, Federica, Paolo Gasbarri, Massimo Panella, & Antonello Rosato. (2023). Multi-Damage Detection in Composite Space Structures via Deep Learning. Sensors. 23(17). 7515–7515. 5 indexed citations
11.
Ceschini, Andrea, et al.. (2023). A General Approach to Dropout in Quantum Neural Networks. Advanced Quantum Technologies. 8(12). 10 indexed citations
12.
Rosato, Antonello, et al.. (2023). Challenges and Perspectives of Smart Grid Systems in Islands: A Real Case Study. Energies. 16(2). 583–583. 15 indexed citations
13.
Ceschini, Andrea, Antonello Rosato, & Massimo Panella. (2023). Modular quantum circuits for secure communication. SHILAP Revista de lepidopterología. 4(4). 208–217. 3 indexed citations
14.
Ceschini, Andrea, et al.. (2022). Multivariate Time Series Analysis for Electrical Power Theft Detection in the Distribution Grid. 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). 1–5. 2 indexed citations
15.
Rosato, Antonello, Massimo Panella, & Denis Kleyko. (2021). Hyperdimensional computing for efficient distributed classification with randomized neural networks. IRIS Research product catalog (Sapienza University of Rome). 10 indexed citations
16.
Micarelli, Alessandro, Andrea Viziano, Massimo Panella, Elisa Micarelli, & Marco Alessandrini. (2019). Power spectra prognostic aspects of impulsive eye movement traces in superior vestibular neuritis. Medical & Biological Engineering & Computing. 57(8). 1617–1627. 6 indexed citations
17.
Panella, Massimo & Antonello Rosato. (2019). A Training Procedure for Quantum Random Vector Functional-link Networks. IRIS Research product catalog (Sapienza University of Rome). 7973–7977. 2 indexed citations
18.
Scardapane, Simone, et al.. (2016). Distributed semi-supervised support vector machines. Neural Networks. 80. 43–52. 39 indexed citations
19.
Panella, Massimo, et al.. (2011). Neural Networks to Model Energy Commodity Price Dynamics. IRIS Research product catalog (Sapienza University of Rome). 4 indexed citations
20.
Rizzi, Antonello, Massimo Panella, & Fabio Massimo Frattale Mascioli. (2002). Adaptive resolution min-max classifiers. IEEE Transactions on Neural Networks. 13(2). 402–414. 78 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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