Daniel Pérez

635 citations
36 papers · 354 · h-index 11

Impact in

Papers in

Daniel Pérez

36 papers receiving 346 citations

Peers

Daniel Pérez
Comparison fields: 5 of 89
  • Computer Vision and Pattern Recognition 79
  • Industrial and Manufacturing Engineering 24
  • Artificial Intelligence 76
  • Signal Processing 22
  • Control and Systems Engineering 44
Replace Chandrashekhar Azad with:
Chandrashekhar Azad India
Alexander Karlsson Sweden
Bharti Khemani India
Hongjian Li China
Aditya Prakash India
Long Peng China
Yaghoub Farjami Iran
Muhammad Akmal Remli Malaysia
Pradnya Vikhar India
Daniel Pérez relative to Chandrashekhar Azad India Chandrashekhar Azad's profile →
Citations per field
00.5×10×16.5×
Chandrashekhar Azad · 1×
Citations per year

Countries citing papers authored by Daniel Pérez

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Pérez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Pérez, 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 Pérez Line = papers co-authored together Daniel Pérez links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201962
2 202128
3 200925
4 202220
5 201516
6 202014
7 201813
8 201813
9 201413
10 201111
11 202011
12 20239
13 20109
14 20169
15 20139
16
The Decentralized Financial Crisis: Attacking DeFi.
20208
17 20198
18 20197
19 20187
20 20187

About Daniel Pérez

Daniel Pérez is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 36 papers that have together received 354 indexed citations. Recurring topics across this work include Data Visualization and Analytics (7 papers), Building Energy and Comfort Optimization (6 papers), Time Series Analysis and Forecasting (5 papers), Anomaly Detection Techniques and Applications (5 papers), Fault Detection and Control Systems (3 papers), Natural Language Processing Techniques (3 papers), Neural Networks and Applications (3 papers) and Handwritten Text Recognition Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (79 citations), Industrial and Manufacturing Engineering (24 citations), Artificial Intelligence (76 citations), Signal Processing (22 citations) and Control and Systems Engineering (44 citations). Daniel Pérez has collaborated with scholars based in Spain, Belgium and Portugal. Frequent co-authors include Manuel Domí­nguez, Ignacio Díaz, Miguel A. Prada, Abel A. Cuadrado, Juan J. Fuertes, Serafí­n Alonso, Antonio Morán, Alfons Juan, Oriol Ramos Terrades and Ángel Piñeiro. Their work appears in journals such as Energy and Buildings, Sensors, Neural Computing and Applications, Integrated Computer-Aided Engineering and Computers in Industry.

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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