Massimo Panella

3.1k citations
146 papers · 2.2k · h-index 25

Impact in

Papers in

Massimo Panella

137 papers receiving 2.1k citations

Peers

Massimo Panella
Comparison fields: 5 of 131
  • Artificial Intelligence 983
  • Computer Vision and Pattern Recognition 320
  • Signal Processing 141
  • Electrical and Electronic Engineering 719
  • Management Science and Operations Research 142
Replace Yonghang Tai with:
Yonghang Tai China
Mu‐Chun Su Taiwan
Lina Stanković United Kingdom
Muhammad Arif Pakistan
Valderi Reis Quietinho Leithardt Portugal
Raymond G. Gosine Canada
Vijander Singh India
Fernando Morgado‐Dias Portugal
Inam Ullah China
Ahmad Ihsan Mohd Yassin Malaysia
Massimo Panella relative to Yonghang Tai China Yonghang Tai's profile →
Citations per field
00.5×10×15×20×24.3×
Yonghang Tai · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Massimo Panella, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Massimo Panella Line = papers co-authored together Massimo Panella links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 146 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016154
2 2015118
3 201581
4 200278
5 201974
6 200565
7 200956
8 201555
9 200651
10 201250
11 201747
12 202047
13 201639
14 201937
15 201637
16 200935
17 202133
18 201533
19 202132
20 201630

About Massimo Panella

Massimo Panella is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Signal Processing and Control and Systems Engineering, having authored 146 papers that have together received 2.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (35 papers), Energy Load and Power Forecasting (27 papers), Quantum Computing Algorithms and Architecture (15 papers), Solar Radiation and Photovoltaics (14 papers), Fuzzy Logic and Control Systems (14 papers), Machine Learning and ELM (12 papers), Stock Market Forecasting Methods (12 papers) and Time Series Analysis and Forecasting (11 papers). The work is most often cited by research in Artificial Intelligence (983 citations), Computer Vision and Pattern Recognition (320 citations), Signal Processing (141 citations), Electrical and Electronic Engineering (719 citations) and Management Science and Operations Research (142 citations). Massimo Panella has collaborated with scholars based in Italy, United States and Australia. Frequent 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. Their work appears in journals such as Electronics Letters, Biomedical Signal Processing and Control, Energies, Quantum Machine Intelligence and Applied Energy.

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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