Noel Lopes
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Science Applications top 10%
- Mechanical Engineering
- Signal Processing
- Co-authors
- Bernardete RibeiroJoão GonçalvesFrancisco José García‐PeñalvoSiti Mariyam ShamsuddinShafaatunnur HasanMauricio Orozco‐AlzateCatarina Silva
- Topics
- Neural Networks and Applications (8 papers)Advanced Neural Network Applications (4 papers)Machine Learning and Data Classification (4 papers)
In The Last Decade
Noel Lopes
21 papers receiving 213 citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 94
- Computer Vision and Pattern Recognition 60
- Computer Science Applications 25
- Mechanical Engineering 21
- Signal Processing 19
Countries citing papers authored by Noel Lopes
This map shows the geographic impact of Noel Lopes'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 Noel Lopes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noel Lopes more than expected).
Fields of papers citing papers by Noel Lopes
This network shows the impact of papers produced by Noel Lopes. 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 Noel Lopes. The network helps show where Noel Lopes may publish in the future.
Co-authorship network of co-authors of Noel Lopes
This figure shows the co-authorship network connecting the top 25 collaborators of Noel Lopes. A scholar is included among the top collaborators of Noel Lopes 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 Noel Lopes. Noel Lopes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 23 | |
| 3 | 5 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 19 | |
| 7 | Machine Learning Big Data Framework and Analytics for Big Data Problems | 9 |
| 8 | 48 | |
| 9 | 5 | |
| 10 | 19 | |
| 11 | 3 | |
| 12 | 22 | |
| 13 | 3 | |
| 14 | 3 | |
| 15 | 13 | |
| 16 | 3 | |
| 17 | 1 | |
| 18 | 多重逆伝搬アルゴリズムのGPU(Graphics Processing Units)実装 | 1 |
| 19 | 12 | |
| 20 | Part Quality Prediction in an Injection Moulding Process Using Neural Networks | 4 |
About Noel Lopes
Noel Lopes is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Software, having authored 22 papers that have together received 218 indexed citations. Recurring topics across this work include Neural Networks and Applications (8 papers), Advanced Neural Network Applications (4 papers) and Machine Learning and Data Classification (4 papers). The work is most often cited by research in Computer Science Applications (25 citations), Computer Vision and Pattern Recognition (60 citations) and Artificial Intelligence (94 citations). Noel Lopes has collaborated with scholars based in Portugal, Malaysia and Spain. Frequent co-authors include Bernardete Ribeiro, João Gonçalves, Francisco José García‐Peñalvo, Siti Mariyam Shamsuddin, Shafaatunnur Hasan, Mauricio Orozco‐Alzate and Catarina Silva. Their work appears in journals such as Pattern Recognition, Computers in Industry and Lecture notes in computer science.
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.