Rui Su

11 papers receiving 329 citations

Rui Su's Hit Papers

Deep learning for depression recognition with audiovisual cues: A review 2021 · 163 citations
1630+1+3Years since publication50100150

Peers

Rui Su
Comparison fields: 5 of 80
  • Experimental and Cognitive Psychology 138
  • Applied Psychology 43
  • Cognitive Neuroscience 72
  • Health Informatics 5
  • Social Psychology 69
Replace Asim Jan with:
Asim Jan United Kingdom
Minqiang Yang China
Sudarshan Pant South Korea
Wheidima Carneiro de Melo Brazil
Bochao Zou China
Juliana Carneiro Gomes Brazil
Abhishek Tiwari Canada
Jorge Sancho Spain
Justin Brooks United States
Christopher R. Cox United States
Rui Su relative to Asim Jan United Kingdom Asim Jan's profile →
Citations per field
00.5×3.2×
Asim Jan · 1×
Citations per year

Countries citing papers authored by Rui Su

Since Specialization
Citations

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

Fields of papers citing papers by Rui Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

14 of 14 papers shown
#Work
1
Deep learning for depression recognition with audiovisual cues: A review
Hit paper breakdown →
2021163
2 202074
3 202128
4 202122
5 201717
6 20217
7 20217
8 20215
9 20215
10 20233
11 20241
12 20250
13 20250
14 20130

About Rui Su

Rui Su is a scholar working on Social Psychology, Experimental and Cognitive Psychology, Applied Psychology, Molecular Biology and Civil and Structural Engineering, having authored 14 papers that have together received 332 indexed citations. Recurring topics across this work include Mental Health via Writing (3 papers), Emotion and Mood Recognition (3 papers), Digital Mental Health Interventions (2 papers), Multimodal Machine Learning Applications (1 paper), RNA and protein synthesis mechanisms (1 paper), AI in cancer detection (1 paper), AI and Big Data Applications (1 paper) and E-commerce and Technology Innovations (1 paper). The work is most often cited by research in Experimental and Cognitive Psychology (138 citations), Applied Psychology (43 citations), Cognitive Neuroscience (72 citations), Health Informatics (5 citations) and Social Psychology (69 citations). Rui Su has collaborated with scholars based in China, Finland and Taiwan. Frequent co-authors include Prayag Tiwari, Lang He, Wei Dang, Chenguang Guo, Pekka Marttinen, Mingyue Niu, Zhongmin Wang, Jiewei Jiang, Hongyu Wang and Xiaoying Pan. Their work appears in journals such as Journal of Chemical Information and Modeling, Chaos Solitons & Fractals, International Journal of Intelligent Systems, IEEE Internet of Things Journal and Applied Intelligence.

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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