Enmei Tu

1.4k total citations · 1 hit paper
33 papers, 889 citations indexed

About

Enmei Tu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Enmei Tu has authored 33 papers receiving a total of 889 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 10 papers in Electrical and Electronic Engineering. Recurrent topics in Enmei Tu's work include Advanced Memory and Neural Computing (8 papers), Neural dynamics and brain function (6 papers) and Face and Expression Recognition (6 papers). Enmei Tu is often cited by papers focused on Advanced Memory and Neural Computing (8 papers), Neural dynamics and brain function (6 papers) and Face and Expression Recognition (6 papers). Enmei Tu collaborates with scholars based in China, New Zealand and Australia. Enmei Tu's co-authors include Jie Yang, Guanghao Zhang, Guang-Bin Huang, Eshan Rajabally, Lily Rachmawati, Nikola Kasabov, Keren Fu, Dacheng Tao, Chen Gong and Tongliang Liu and has published in prestigious journals such as Information Sciences, IEEE Transactions on Fuzzy Systems and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Enmei Tu

32 papers receiving 866 citations

Hit Papers

align trajectories

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.

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

2017 318 citations Enmei Tu, Guanghao Zhang et al. profile →

Peers

Enmei Tu

AI

OE

CVPR

EEE

CN

Jianga Shang China

Xiaoheng Jiang China

Han‐Pang Huang Taiwan

Nobuo Kawaguchi Japan

José M. González Spain

Bilel Benjdira Saudi Arabia

Brian Coltin United States

Susu Xu United States

Addisson Salazar Spain

Jianga Shang China

Enmei Tu

164 ×0.5

AI

256 ×0.8

OE

280 ×1.2

CVPR

633 ×4.3

EEE

37 ×0.3

CN

Citations per year, relative to Enmei Tu Enmei Tu (= 1×) peers Jianga Shang

Countries citing papers authored by Enmei Tu

Since Specialization

Citations

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

Fields of papers citing papers by Enmei Tu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Enmei Tu

This figure shows the co-authorship network connecting the top 25 collaborators of Enmei Tu. A scholar is included among the top collaborators of Enmei Tu 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 Enmei Tu. Enmei Tu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Tu, Enmei, Zihao Wang, Jie Yang, & Nikola Kasabov. (2021). Deep semi-supervised learning via dynamic anchor graph embedding in latent space. Neural Networks. 146. 350–360. 13 indexed citations

2.

Yang, Jie, et al.. (2021). Multi-Stream 3D latent feature clustering for abnormality detection in videos. Applied Intelligence. 52(1). 1126–1143. 18 indexed citations

3.

Yang, Jie, et al.. (2021). Anomaly3D: Video anomaly detection based on 3D-normality clusters. Journal of Visual Communication and Image Representation. 75. 103047–103047. 28 indexed citations

4.

Xie, Zhiqiang, et al.. (2021). Reinforcement Learning-Based Insulin Injection Time And Dosages Optimization. 1–8. 7 indexed citations

5.

Tu, Enmei, et al.. (2020). End-To-End Graph-Based Deep Semi-Supervised Learning with Extended Graph Laplacian. 7. 5948–5953. 2 indexed citations

6.

Doborjeh, Maryam, et al.. (2019). Personalised modelling with spiking neural networks integrating temporal and static information. Neural Networks. 119. 162–177. 9 indexed citations

7.

Chen, Mingjian, Hao Zheng, Changsheng Lu, et al.. (2019). Accurate breast lesion segmentation by exploiting spatio-temporal information with deep recurrent and convolutional network. Journal of Ambient Intelligence and Humanized Computing. 14(12). 15609–15617. 4 indexed citations

8.

Suryanarayana, Gunnam, Enmei Tu, & Jie Yang. (2018). Infrared super-resolution imaging using multi-scale saliency and deep wavelet residuals. Infrared Physics & Technology. 97. 177–186. 20 indexed citations

9.

Zhang, Guanghao, et al.. (2017). Stable and improved generative adversarial nets (GANS): A constructive survey. 1871–1875. 7 indexed citations

10.

Tu, Enmei, et al.. (2017). A theoretical study of the relationship between an ELM network and its subnetworks. 7. 1794–1801. 4 indexed citations

11.

Tu, Enmei, et al.. (2016). A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification. Information Sciences. 367-368. 673–688. 30 indexed citations

12.

Tu, Enmei, Nikola Kasabov, & Jie Yang. (2016). Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data. IEEE Transactions on Neural Networks and Learning Systems. 28(6). 1305–1317. 48 indexed citations

13.

Kasabov, Nikola, Enmei Tu, Stefan Marks, et al.. (2015). Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications. Neural Networks. 78. 1–14. 107 indexed citations

14.

Tu, Enmei, et al.. (2015). Posterior Distribution Learning (PDL): A novel supervised learning framework using unlabeled samples to improve classification performance. Neurocomputing. 157. 173–186. 10 indexed citations

15.

Taylor, Denise, Nikola Kasabov, Elisa Capecci, et al.. (2014). Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCIapplications. 3221–3225. 11 indexed citations

16.

Tu, Enmei, Longbing Cao, Jie Yang, & Nikola Kasabov. (2014). A novel graph-based k-means for nonlinear manifold clustering and representative selection. Neurocomputing. 143. 109–122. 30 indexed citations

17.

Gong, Chen, Keren Fu, Qiang Wu, Enmei Tu, & Jie Yang. (2014). Semi-supervised classification with pairwise constraints. Neurocomputing. 139. 130–137. 19 indexed citations

18.

Othman, Muhaini, Nikola Kasabov, Enmei Tu, et al.. (2014). Improved predictive personalized modelling with the use of Spiking Neural Network system and a case study on stroke occurrences data. 275. 3197–3204. 8 indexed citations

19.

Tu, Enmei, et al.. (2013). An Experimental Comparison of Semi-supervised Learning Algorithms for Multispectral Image Classification. Photogrammetric Engineering & Remote Sensing. 79(4). 347–357. 5 indexed citations

20.

Fang, Jiangxiong, et al.. (2011). [Narrow band multi-region level set method for remote sensing image].. PubMed. 31(11). 3001–5. 1 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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