Manuel Carranza-García
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Artificial Intelligence top 10%
- Media Technology top 5%
- Ecology
- Co-authors
- José C. RiquelmeJorge García–GutiérrezPedro Lara-BenítezJosé María Luna-RomeraFrancisco González-LongattÁngel Arcos-VargasDavid Gutiérrez‐AvilésSimonas Kecorius
- Topics
- Energy Load and Power Forecasting (5 papers)Data Stream Mining Techniques (4 papers)Advanced Neural Network Applications (4 papers)
- Partner nations
- SpainUnited KingdomUnited States
In The Last Decade
Manuel Carranza-García
15 papers receiving 615 citations
Peers
Comparison fields: 5 of 107
- Computer Vision and Pattern Recognition 152
- Electrical and Electronic Engineering 130
- Artificial Intelligence 121
- Media Technology 111
- Ecology 99
Countries citing papers authored by Manuel Carranza-García
This map shows the geographic impact of Manuel Carranza-García'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 Manuel Carranza-García with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manuel Carranza-García more than expected).
Fields of papers citing papers by Manuel Carranza-García
This network shows the impact of papers produced by Manuel Carranza-García. 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 Manuel Carranza-García. The network helps show where Manuel Carranza-García may publish in the future.
Co-authorship network of co-authors of Manuel Carranza-García
This figure shows the co-authorship network connecting the top 25 collaborators of Manuel Carranza-García. A scholar is included among the top collaborators of Manuel Carranza-García 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 Manuel Carranza-García. Manuel Carranza-García is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 8 | |
| 7 | 25 | |
| 8 | 23 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 17 | |
| 12 | 32 | |
| 13 | 36 | |
| 14 | 154 | |
| 15 | 167 | |
| 16 | 169 |
About Manuel Carranza-García
Manuel Carranza-García is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 16 papers that have together received 642 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (5 papers), Data Stream Mining Techniques (4 papers) and Advanced Neural Network Applications (4 papers). The work is most often cited by research in Media Technology (111 citations), Computer Vision and Pattern Recognition (152 citations) and Environmental Engineering (73 citations). Manuel Carranza-García has collaborated with scholars based in Spain, United Kingdom and United States. Frequent co-authors include José C. Riquelme, Jorge García–Gutiérrez, Pedro Lara-Benítez, José María Luna-Romera, Francisco González-Longatt, Ángel Arcos-Vargas, David Gutiérrez‐Avilés, Simonas Kecorius, Luis Miguel Soria Morillo and Avideh Zakhor. Their work appears in journals such as Journal of Hazardous Materials, Applied Energy and Remote Sensing.
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.