Renata Lopes Rosa

710 citations
32 papers · 493 indexed · h-index 12

Renata Lopes Rosa

24 papers receiving 461 citations

Peers

Renata Lopes Rosa
Comparison fields: 5 of 60
  • Signal Processing 139
  • Computer Networks and Communications 208
  • Artificial Intelligence 183
  • Computer Vision and Pattern Recognition 89
  • Information Systems 81
Replace Junzhao Du with:
Junzhao Du China
Dick Carrillo Brazil
Vasileios A. Memos Greece
Manjunath R Kounte India
Pankaj Dadheech India
Shahab Tayeb United States
Kun-Ming Yu Taiwan
Manal Abdullah Alohali Saudi Arabia
Abdulaziz Almehmadi Saudi Arabia
Miao Hu China
Renata Lopes Rosa relative to Junzhao Du China Junzhao Du's profile →
Citations per field
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Junzhao Du · 1×
Citations per year

Countries citing papers authored by Renata Lopes Rosa

Since Specialization
Citations

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

Fields of papers citing papers by Renata Lopes Rosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Renata Lopes Rosa. 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 Renata Lopes Rosa. The network helps show where Renata Lopes Rosa may publish in the future.

Co-authorship network

The 18 scholars most cited alongside Renata Lopes Rosa, 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 Renata Lopes Rosa Line = papers co-authored together Renata Lopes Rosa links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
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6 202371
7 202319
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11 202122
12 20218
13 20200
14 20203
15 20203
16 20197
17 201923
18 20171
19 20161
20
An Extracting Points Strategy for Flatness Measurement on Components by means of CMM
20111

About Renata Lopes Rosa

Renata Lopes Rosa is a scholar working on Signal Processing, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 32 papers that have together received 493 indexed citations. Recurring topics across this work include Speech and Audio Processing (6 papers), IoT and Edge/Fog Computing (4 papers), Network Security and Intrusion Detection (3 papers), Vehicular Ad Hoc Networks (VANETs) (3 papers), Advanced Malware Detection Techniques (3 papers), Sentiment Analysis and Opinion Mining (2 papers), Telecommunications and Broadcasting Technologies (2 papers) and Wireless Networks and Protocols (2 papers). The work is most often cited by research in Signal Processing (139 citations), Computer Networks and Communications (208 citations) and Artificial Intelligence (183 citations). Renata Lopes Rosa has collaborated with scholars based in Brazil, Pakistan and Finland. Frequent co-authors include Demóstenes Zegarra Rodríguez, Muhammad Saadi, Graça Bressan, Dick Carrillo, Ogobuchi Daniel Okey, João Henrique Kleinschmidt, Ahmed Farouk, Lunchakorn Wuttisittikulkij, Pablo Adasme and Júlia Issy Abrahão. Their work appears in journals such as IEEE Access, Sensors and IEEE Transactions on Intelligent Transportation 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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