Tiago Carneiro
- Signal Processing top 10%
- Artificial Intelligence top 10%
- Metaheuristic Optimization Algorithms Research 2
- Media Technology top 10%
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- IoT and Edge/Fog Computing 7
- Optimization and Search Problems 4
- Distributed and Parallel Computing Systems 4
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- Vehicular Ad Hoc Networks (VANETs) 4
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- Cloud Computing and Resource Management 3
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- Scheduling and Optimization Algorithms 2
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- Advanced Multi-Objective Optimization Algorithms 2
Tiago Carneiro
15 papers receiving 683 citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Signal Processing 69
- Artificial Intelligence 199
- Computer Vision and Pattern Recognition 124
- Media Technology 49
- Computer Networks and Communications 127
Countries citing papers authored by Tiago Carneiro
This map shows the geographic impact of Tiago Carneiro'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 Tiago Carneiro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tiago Carneiro more than expected).
Fields of papers citing papers by Tiago Carneiro
This network shows the impact of papers produced by Tiago Carneiro. 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 Tiago Carneiro. The network helps show where Tiago Carneiro may publish in the future.
Co-authorship network
The 23 scholars most cited alongside Tiago Carneiro, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2023 | 17 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 0 | |
| 6 | 2021 | 12 | |
| 7 | 2021 | 4 | |
| 8 | 2020 | 69 | |
| 9 | 2020 | 20 | |
| 10 | 2020 | 10 | |
| 11 | 2019 | 179 | |
| 12 | 2019 | 1 | |
| 13 | A GUI-based Platform for Quickly Prototyping Server-side IoT Applications | 2018 | 2 |
| 14 | Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applicationsbreakdown → | 2018 | 384 |
| 15 | 2018 | 8 | |
| 16 | 2017 | 11 | |
| 17 | Depth-First Search versus Jurema Search on GPU Branch-and-Bound Algorithms: a case study | 2013 | 2 |
About Tiago Carneiro
Tiago Carneiro is a scholar working on Computer Networks and Communications, Industrial and Manufacturing Engineering, Hardware and Architecture, Software and Artificial Intelligence, having authored 17 papers that have together received 721 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (7 papers), Optimization and Search Problems (4 papers), Vehicular Ad Hoc Networks (VANETs) (4 papers), Distributed and Parallel Computing Systems (4 papers), Cloud Computing and Resource Management (3 papers), Scheduling and Optimization Algorithms (2 papers), Advanced Multi-Objective Optimization Algorithms (2 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Signal Processing (69 citations), Artificial Intelligence (199 citations), Computer Vision and Pattern Recognition (124 citations), Media Technology (49 citations) and Computer Networks and Communications (127 citations). Tiago Carneiro has collaborated with scholars based in Brazil, France and Belgium. Frequent co-authors include Victor Hugo C. de Albuquerque, Pedro P. Rebouças Filho, Raul Victor M. da Nóbrega, Gui‐Bin Bian, Rytis Maskeliūnas, Robertas Damaševičius, Wei Wei, Jefferson S. Almeida, José Neuman de Souza and Paulo A. L. Rêgo. Their work appears in journals such as IEEE Access, Vehicular Communications, Future Generation Computer Systems, Swarm and Evolutionary Computation and Pattern Recognition Letters.
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.