Ge Shi

About

Ge Shi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ge Shi has authored 27 papers receiving a total of 269 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Ge Shi’s work include Topic Modeling (8 papers), Multimodal Machine Learning Applications (5 papers) and Sentiment Analysis and Opinion Mining (5 papers). Ge Shi is often cited by papers focused on Topic Modeling (8 papers), Multimodal Machine Learning Applications (5 papers) and Sentiment Analysis and Opinion Mining (5 papers). Ge Shi collaborates with scholars based in China, Ireland and United States. Ge Shi's co-authors include Chong Feng, Arshad Ahmad, Abdallah Yousif, Lifang Wu, Heyan Huang, Xiao Liu, Kan Li, Meng Jian, Lejian Liao and Min Wu and has published in prestigious journals such as Expert Systems with Applications, Sensors and IEEE Access.

In The Last Decade

Co-authorship network of co-authors of Ge Shi i

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Fields of papers citing papers by Ge Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Ge Shi

Since Specialization

Citations

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

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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