Daniel Moraes

516 citations
25 papers · 336 indexed · h-index 10

Impact in

Papers in

Daniel Moraes

24 papers receiving 328 citations

Peers

Daniel Moraes
Comparison fields: 5 of 64
  • Media Technology 70
  • Computer Vision and Pattern Recognition 155
  • Signal Processing 47
  • Architecture 6
  • Artificial Intelligence 78
Replace Silvio Jamil F. Guimarães with:
Silvio Jamil F. Guimarães Brazil
Liyuan Xing China
Piotr Biliński Poland
Jakaria Rabbi Bangladesh
Linghui Li China
Ratnadeep R. Deshmukh India
Yen‐Hung Chen Taiwan
Mengmeng Wang China
Ankur Jain United States
Hazra Imran Canada
Daniel Moraes relative to Silvio Jamil F. Guimarães Brazil Silvio Jamil F. Guimarães's profile →
Citations per field
00.5×1.5×
Silvio Jamil F. Guimarães · 1×
Citations per year

Countries citing papers authored by Daniel Moraes

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Moraes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Daniel Moraes, 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 Daniel Moraes Line = papers co-authored together Daniel Moraes links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201693
2 201051
3 201650
4 202230
5 201817
6 201714
7 202111
8 201610
9
RECOD at MediaEval 2014: Violent Scenes Detection Task.
20149
10 20249
11 20246
12 20086
13 20095
14 20085
15
RECOD at MediaEval 2015: Affective Impact of Movies Task
20153
16 20213
17 20213
18 20213
19 20212
20 20252

About Daniel Moraes

Daniel Moraes is a scholar working on Ecology, Media Technology, Computer Networks and Communications, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 25 papers that have together received 336 indexed citations. Recurring topics across this work include Remote Sensing in Agriculture (11 papers), Remote-Sensing Image Classification (7 papers), Land Use and Ecosystem Services (5 papers), IPv6, Mobility, Handover, Networks, Security (4 papers), Human Pose and Action Recognition (4 papers), Video Analysis and Summarization (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Video Surveillance and Tracking Methods (3 papers). The work is most often cited by research in Media Technology (70 citations), Computer Vision and Pattern Recognition (155 citations), Signal Processing (47 citations), Architecture (6 citations) and Artificial Intelligence (78 citations). Daniel Moraes has collaborated with scholars based in Brazil, Portugal and Sweden. Frequent co-authors include Anderson Rocha, Daniel Moreira, Eduardo Valle, Sandra Avila, Mauricio Pérez, Vanessa Testoni, Siome Goldenstein, Eleri Cardozo, Mário Caetano and Pedro Benevides. Their work appears in journals such as Remote Sensing, International Journal of Applied Earth Observation and Geoinformation, Information Fusion, IEEE Transactions on Learning Technologies and European Journal of 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.

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