Man-Fai Leung

2.4k total citations · 3 hit papers
88 papers, 1.4k citations indexed

About

Man-Fai Leung is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Man-Fai Leung has authored 88 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 33 papers in Computer Vision and Pattern Recognition and 14 papers in Computational Mechanics. Recurrent topics in Man-Fai Leung's work include Face and Expression Recognition (21 papers), Metaheuristic Optimization Algorithms Research (10 papers) and Sparse and Compressive Sensing Techniques (8 papers). Man-Fai Leung is often cited by papers focused on Face and Expression Recognition (21 papers), Metaheuristic Optimization Algorithms Research (10 papers) and Sparse and Compressive Sensing Techniques (8 papers). Man-Fai Leung collaborates with scholars based in United Kingdom, China and Hong Kong. Man-Fai Leung's co-authors include Jun Wang, Hangjun Che, Sin-Chun Ng, Cheng Liu, Andrew Kwok-Fai Lui, Chenglu Li, Yan Zheng, Zheng Yan, Shiping Wen and Keping Yu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

Man-Fai Leung

76 papers receiving 1.4k citations

Hit Papers

A BERT Framework to Sentiment Analysis of Tweets 2023 2026 2024 2025 2023 2023 2024 25 50 75 100
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. A BERT Framework to Sentiment Analysis of Tweets
    2023 109 citations Sin-Chun Ng, Man-Fai Leung et al. Sensors profile →
  2. Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints
    2023 82 citations Chenglu Li, Hangjun Che et al. profile →
  3. Projected cross-view learning for unbalanced incomplete multi-view clustering
    2024 53 citations Hangjun Che, Man-Fai Leung et al. Information Fusion profile →

Peers

Man-Fai Leung
Jie Zhou China
Bin Gu China
Changan Yuan China
Zaer S. Abo‐Hammour Jordan
Huan Zhang China
Rómer Rosales United States
Tarek Abd El‐Hafeez Egypt
Luis E. Ortiz United States
Chao Luo China
Peng Zhou China
Jie Zhou China
Man-Fai Leung
Citations per year, relative to Man-Fai Leung Man-Fai Leung (= 1×) peers Jie Zhou

Countries citing papers authored by Man-Fai Leung

Since Specialization
Citations

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

Fields of papers citing papers by Man-Fai Leung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Man-Fai Leung. 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 Man-Fai Leung. The network helps show where Man-Fai Leung may publish in the future.

Co-authorship network of co-authors of Man-Fai Leung

This figure shows the co-authorship network connecting the top 25 collaborators of Man-Fai Leung. A scholar is included among the top collaborators of Man-Fai Leung 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 Man-Fai Leung. Man-Fai Leung 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.
2.
Che, Hangjun, et al.. (2025). Robust Diverse Multi-View Learning for Cancer Subtyping. 22(6). 2685–2696.
3.
Ali, Liaqat, Man-Fai Leung, Muhammad Asghar Khan, et al.. (2025). ChiGa-Net: A genetically optimized neural network with refined deeply extracted features using χ2 statistical score for trustworthy Parkinson’s disease detection. Neurocomputing. 624. 129450–129450. 2 indexed citations
4.
Che, Hangjun, et al.. (2025). Matrix Factorization for Multimedia Clustering.
5.
Che, Hangjun, et al.. (2025). High-order consensus graph learning for incomplete multi-view clustering. Applied Intelligence. 55(7). 4 indexed citations
6.
Xie, Xiurui, et al.. (2025). SEMQ: Efficient non-uniform quantization with sensitivity-based error minimization for large language models. Future Generation Computer Systems. 175. 108120–108120. 1 indexed citations
7.
Wu, Zhenyu, et al.. (2025). Enhanced Sensing for Automotive mmWave Radar: Super-Resolution Range and Angle Estimation With Coherent Extension and Advanced IAA. IEEE Transactions on Consumer Electronics. 71(2). 4797–4806. 1 indexed citations
8.
Che, Hangjun, et al.. (2024). Tensor-based unsupervised feature selection for error-robust handling of unbalanced incomplete multi-view data. Information Fusion. 114. 102693–102693. 23 indexed citations
9.
Ng, Sin-Chun, et al.. (2024). Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121. Applied Sciences. 14(17). 7707–7707. 19 indexed citations
10.
Liu, Cheng, Rui Li, Hangjun Che, et al.. (2024). Latent Structure-Aware View Recovery for Incomplete Multi-View Clustering. IEEE Transactions on Knowledge and Data Engineering. 36(12). 8655–8669. 7 indexed citations
11.
Che, Hangjun, et al.. (2024). Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering. IEEE Transactions on Emerging Topics in Computational Intelligence. 9(2). 1817–1829. 7 indexed citations
12.
13.
Che, Hangjun, et al.. (2023). Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering. Signal Processing. 217. 109341–109341. 28 indexed citations
14.
Wang, Zhenkun, Man-Fai Leung, Hangjun Che, & Carlos A. Coello Coello. (2023). Thematic issue on advances in analysis and application of multi-objective memetic optimization algorithms. Memetic Computing. 15(1). 1–2. 2 indexed citations
15.
Che, Hangjun, et al.. (2023). Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing. IEEE Transactions on Consumer Electronics. 70(1). 2925–2938. 46 indexed citations
16.
Leung, Man-Fai, et al.. (2023). An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments. SHILAP Revista de lepidopterología. 2(1). 153–169. 5 indexed citations
17.
Ng, Sin-Chun, et al.. (2023). A BERT Framework to Sentiment Analysis of Tweets. Sensors. 23(1). 506–506. 109 indexed citations breakdown →
18.
Leung, Man-Fai, Carlos A. Coello Coello, Chi-Chung Cheung, Sin-Chun Ng, & Andrew Kwok-Fai Lui. (2020). A Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization. IEEE Access. 8. 189527–189545. 25 indexed citations
19.
Ng, Sin-Chun, et al.. (2020). A Competitive Mechanism Multi-Objective Particle Swarm Optimization Algorithm and Its Application to Signalized Traffic Problem. Cybernetics & Systems. 52(1). 73–104. 36 indexed citations
20.
Leung, Man-Fai, et al.. (2014). A new strategy for finding good local guides in MOPSO. 1990–1997. 25 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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