Fatma Nasoz

17 papers receiving 879 citations

Peers

Fatma Nasoz
Comparison fields: 5 of 94
  • Experimental and Cognitive Psychology 602
  • Human-Computer Interaction 136
  • Cognitive Neuroscience 389
  • Social Psychology 279
  • Computer Vision and Pattern Recognition 165
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Andrea Kleinsmith United States
Pramila Rani United States
Yao Chong Lim United States
Sten Hanke Austria
Christos D. Katsis Greece
Radosław Niewiadomski Italy
Arturo Martínez‐Rodrigo Spain
Cyril Rebetez Switzerland
Dimitris Manousos Greece
Abdallah El Ali Netherlands
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Citations per field
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Citations per year

Countries citing papers authored by Fatma Nasoz

Since Specialization
Citations

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

Fields of papers citing papers by Fatma Nasoz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 10 scholars most cited alongside Fatma Nasoz, 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 Fatma Nasoz Line = papers co-authored together Fatma Nasoz links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2004297
2 2004239
3 2003107
4 202075
5 201065
6 200264
7 202026
8 200619
9 202118
10 200217
11 200211
12 20246
13 20064
14
Adaptive intelligent user interfaces with emotion recognition
20043
15 20022
16
Application of the Multilevel Process Theory of Emotion to User-Modeling
20021
17
The MAUl Project: Building MultiModal Affective User Interfaces for Everyone
20021
18 20250

About Fatma Nasoz

Fatma Nasoz is a scholar working on Experimental and Cognitive Psychology, Social Psychology, Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience, having authored 18 papers that have together received 955 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (12 papers), Social Robot Interaction and HRI (7 papers), Sleep and Work-Related Fatigue (3 papers), EEG and Brain-Computer Interfaces (3 papers), Bone health and osteoporosis research (2 papers), Speech and dialogue systems (2 papers), Face and Expression Recognition (2 papers) and Face recognition and analysis (2 papers). The work is most often cited by research in Experimental and Cognitive Psychology (602 citations), Human-Computer Interaction (136 citations), Cognitive Neuroscience (389 citations), Social Psychology (279 citations) and Computer Vision and Pattern Recognition (165 citations). Fatma Nasoz has collaborated with scholars based in United States, France and Greece. Frequent co-authors include Christine Lisetti, Neal Finkelstein, Cynthia LeRouge, Athanasios V. Vasilakos, Mira Han, Jong-Yun Jung, Qing Wu, Robert A. Greenes, Kenneth G. Saag and Daniel Álvarez. Their work appears in journals such as BMC Bioinformatics, Journal of Visual Languages & Computing, Cognition Technology & Work, Information Sciences and Calcified Tissue International.

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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