Luca Oneto

8.8k citations
185 papers · 4.9k indexed · 3 hit papers · h-index 29

Impact in

Papers in

Luca Oneto

175 papers receiving 4.7k citations

Hit Papers

Technical analysis and sentiment embeddings for market trend prediction 2019 · 202 citations
20220132026201720212505007501000

Peers

Luca Oneto
Comparison fields: 5 of 170
  • Computer Vision and Pattern Recognition 1.5k
  • Artificial Intelligence 1.6k
  • Industrial and Manufacturing Engineering 441
  • Transportation 240
  • Signal Processing 327
Replace Davide Anguita with:
Davide Anguita Italy
Le Zhang China
Wu Deng China
Costas J. Spanos United States
Mohammed A. A. Al‐qaness China
Zhihua Cui China
Huimin Zhao China
Gwanggil Jeon South Korea
Shangce Gao Japan
Seungmin Rho South Korea
Luca Oneto relative to Davide Anguita Italy Davide Anguita's profile →
Citations per field
00.5×1.7×
Davide Anguita · 1×
Citations per year

Countries citing papers authored by Luca Oneto

Since Specialization
Citations

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

Fields of papers citing papers by Luca Oneto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Luca Oneto, 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 Luca Oneto Line = papers co-authored together Luca Oneto links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20254
3 20241
4 20232
5 20233
6 20223
7 202229
8 20223
9 202112
10 20211
11 202116
12 20208
13 20194
14 201928
15 201930
16 20193
17
Technical analysis and sentiment embeddings for market trend prediction
Hit paper breakdown →
2019202
18 201798
19 20179
20 20169

About Luca Oneto

Luca Oneto is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mathematics, Safety Research and Ocean Engineering, having authored 185 papers that have together received 4.9k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (23 papers), Machine Learning and Data Classification (22 papers), Face and Expression Recognition (19 papers), Neural Networks and Applications (15 papers), Ethics and Social Impacts of AI (14 papers), Adversarial Robustness in Machine Learning (14 papers), Fault Detection and Control Systems (14 papers) and Anomaly Detection Techniques and Applications (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Artificial Intelligence (1.6k citations), Industrial and Manufacturing Engineering (441 citations), Transportation (240 citations) and Signal Processing (327 citations). Luca Oneto has collaborated with scholars based in Italy, United Kingdom and Netherlands. Frequent co-authors include Davide Anguita, Alessandro Ghio, Xavier Parra, Andrea Coraddu, Sandro Ridella, Albert Samà, Francesca Cipollini, Erik Cambria, Francesco Baldi and S. Savio. Their work appears in journals such as Neurocomputing, Ocean Engineering, Cognitive Computation, IEEE Access and IEEE Transactions on Neural Networks and Learning Systems.

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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