Yang Shi

45 papers receiving 409 citations

Peers

Yang Shi
Comparison fields: 5 of 81
  • Computer Science Applications 51
  • Signal Processing 76
  • Health Informatics 9
  • Computer Networks and Communications 151
  • Artificial Intelligence 145
Replace Gaoyang Liu with:
Gaoyang Liu China
Xuefei Yin Australia
Xingxing Xiong China
Manu Sood India
Majdi Rawashdeh Saudi Arabia
Alsharif Abuadbba Australia
Victor Sucasas Portugal
Yazan A. Alsariera Saudi Arabia
Rahim Rahmani Sweden
Riccardo Coppola Italy
Yang Shi relative to Gaoyang Liu China Gaoyang Liu's profile →
Citations per field
00.5×1.5×2.1×
Gaoyang Liu · 1×
Citations per year

Countries citing papers authored by Yang Shi

Since Specialization
Citations

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

Fields of papers citing papers by Yang Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201996
2 201978
3 202428
4 202226
5 200419
6 202417
7 202116
8 201914
9 202312
10 202210
11 20189
12 20228
13 20228
14 20187
15 20227
16 20246
17 20206
18 20215
19
More With Less: Exploring How to Use Deep Learning Effectively through Semi-supervised Learning for Automatic Bug Detection in Student Code
20215
20 20025

About Yang Shi

Yang Shi is a scholar working on Artificial Intelligence, Information Systems, Computer Science Applications, Transportation and Computer Networks and Communications, having authored 53 papers that have together received 429 indexed citations. Recurring topics across this work include Online Learning and Analytics (9 papers), Transportation Planning and Optimization (7 papers), Teaching and Learning Programming (7 papers), Railway Systems and Energy Efficiency (5 papers), Software Engineering Research (4 papers), Educational Technology and Assessment (4 papers), Intelligent Tutoring Systems and Adaptive Learning (4 papers) and Traffic Prediction and Management Techniques (3 papers). The work is most often cited by research in Computer Science Applications (51 citations), Signal Processing (76 citations), Health Informatics (9 citations), Computer Networks and Communications (151 citations) and Artificial Intelligence (145 citations). Yang Shi has collaborated with scholars based in China, United States and Thailand. Frequent co-authors include Wen‐Zhan Song, Fangyu Li, Jin Ye, Xiang‐Yang Li, Thomas Price, Bingxue Zhang, Bin-Bin Hu, Hai‐Tao Zhang, Francis Quek and Xuan Li. Their work appears in journals such as International Journal of Transportation Science and Technology, IEEE Internet of Things Journal, International Journal of Environmental Research and Public Health, Journal of college student development and Physica A Statistical Mechanics and its Applications.

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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