Xiaochuan Shi

663 total citations · 1 hit paper
34 papers, 473 citations indexed

About

Xiaochuan Shi is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Xiaochuan Shi has authored 34 papers receiving a total of 473 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 10 papers in Computer Networks and Communications and 9 papers in Signal Processing. Recurrent topics in Xiaochuan Shi's work include Anomaly Detection Techniques and Applications (9 papers), Time Series Analysis and Forecasting (8 papers) and Network Security and Intrusion Detection (2 papers). Xiaochuan Shi is often cited by papers focused on Anomaly Detection Techniques and Applications (9 papers), Time Series Analysis and Forecasting (8 papers) and Network Security and Intrusion Detection (2 papers). Xiaochuan Shi collaborates with scholars based in China, United States and Serbia. Xiaochuan Shi's co-authors include Chao Ma, Lihua Gao, Libing Wu, Shuang Wu, Zi Wang, Wei Li, Libing Wu, Min Wang, Man Li and Dan Wu and has published in prestigious journals such as Nature Communications, Expert Systems with Applications and Sensors.

In The Last Decade

Xiaochuan Shi

30 papers receiving 455 citations

Hit Papers

Explainable artificial intelligence for digital finance a... 2023 2026 2024 2025 2023 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Explainable artificial intelligence for digital finance and consumption upgrading
    2023 146 citations Xiaochuan Shi, Lihua Gao et al. Finance research letters profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Xiaochuan Shi China 11 107 102 74 55 54 34 473
Qingyuan Zhou China 13 105 1.0× 45 0.4× 63 0.9× 87 1.6× 31 0.6× 40 472
Ah-Lian Kor United Kingdom 11 72 0.7× 36 0.4× 85 1.1× 72 1.3× 68 1.3× 64 459
Will Serrano United Kingdom 12 77 0.7× 35 0.3× 77 1.0× 124 2.3× 43 0.8× 28 370
Sven Seuken Switzerland 13 244 2.3× 101 1.0× 140 1.9× 50 0.9× 55 1.0× 51 595
Luis Sánchez Fernández Spain 14 254 2.4× 111 1.1× 126 1.7× 120 2.2× 47 0.9× 70 711
Parul Agarwal India 13 149 1.4× 50 0.5× 84 1.1× 123 2.2× 84 1.6× 37 515
Jheng-Long Wu Taiwan 16 292 2.7× 109 1.1× 47 0.6× 50 0.9× 56 1.0× 38 711
Raed A. Said United Arab Emirates 13 213 2.0× 50 0.5× 118 1.6× 141 2.6× 89 1.6× 31 770
Ahmed Almulihi Saudi Arabia 13 90 0.8× 31 0.3× 46 0.6× 62 1.1× 75 1.4× 25 547
Vikas Garg India 13 118 1.1× 45 0.4× 48 0.6× 53 1.0× 62 1.1× 78 565

Countries citing papers authored by Xiaochuan Shi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaochuan Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaochuan Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaochuan Shi. A scholar is included among the top collaborators of Xiaochuan Shi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaochuan Shi. Xiaochuan Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shi, Xiaochuan, et al.. (2025). Exploration of applications with ChatGPT for green supply chain management. Annals of Operations Research. 355(2). 1503–1536. 1 indexed citations
2.
Shi, Xiaochuan, et al.. (2025). Measuring student attention based on EEG brain signals using deep reinforcement learning. Expert Systems with Applications. 269. 126426–126426. 6 indexed citations
3.
Wang, Yongqi, et al.. (2024). RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach. Applied Sciences. 14(8). 3532–3532. 13 indexed citations
4.
Wang, Yulin, et al.. (2024). Detection of multi‐class lung diseases based on customized neural network. Computational Intelligence. 40(2).
5.
Wang, Yulin, et al.. (2024). Segmentation and identification of brain tumour in MRI images using PG-OneShot learning CNN model. Multimedia Tools and Applications. 83(34). 81361–81382. 3 indexed citations
6.
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8.
Shi, Xiaochuan, et al.. (2023). Explainable artificial intelligence for digital finance and consumption upgrading. Finance research letters. 58. 104489–104489. 146 indexed citations breakdown →
9.
10.
Zhang, Ruijie, et al.. (2023). Enter into society: Digitalized livelihoods and prosumer labor for people with disabilities in China. Frontiers in Psychology. 14. 1103199–1103199. 2 indexed citations
11.
Shi, Xiaochuan, et al.. (2022). Multi-timescale History Modeling for Temporal Knowledge Graph Completion. 26. 477–484. 2 indexed citations
12.
Shi, Xiaochuan, et al.. (2022). Users' Feedback on COVID-19 Lockdown Documentary: An Emotion Analysis and Topic Modeling Analysis. Frontiers in Psychology. 13. 944049–944049. 2 indexed citations
13.
Ma, Chao, et al.. (2022). Points2Shapelets: A Salience-Guided Shapelets Selection Approach to Time Series Classification. 2022 International Joint Conference on Neural Networks (IJCNN). 2. 1–8. 1 indexed citations
14.
Wu, Libing, Debiao He, Qian Wang, et al.. (2022). G-VCFL: Grouped Verifiable Chained Privacy-Preserving Federated Learning. IEEE Transactions on Network and Service Management. 19(4). 4219–4231. 31 indexed citations
15.
Wu, Libing, Rui Zhang, Qingan Li, Chao Ma, & Xiaochuan Shi. (2022). A mobile edge computing-based applications execution framework for Internet of Vehicles. Frontiers of Computer Science. 16(5). 17 indexed citations
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17.
Huang, Yanting, Xiaobo Sun, Shaojun Yu, et al.. (2021). A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease. Nature Communications. 12(1). 4472–4472. 38 indexed citations
18.
Zheng, Li, Chao Ma, Xiaochuan Shi, et al.. (2021). TSA-GAN: A Robust Generative Adversarial Networks for Time Series Augmentation. 1–8. 18 indexed citations
19.
Ma, Chao, Xiaochuan Shi, Wei Li, & Weiping Zhu. (2020). Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment. Sensors. 20(7). 1908–1908. 8 indexed citations
20.
Baz, Didier El, Mhand Hifi, Lei Wu, & Xiaochuan Shi. (2016). A Parallel Ant Colony Optimization for the Maximum-Weight Clique Problem. HAL (Le Centre pour la Communication Scientifique Directe). 6 indexed citations

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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