Nannan Ji

466 total citations
19 papers, 335 citations indexed

About

Nannan Ji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Nannan Ji has authored 19 papers receiving a total of 335 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 4 papers in Signal Processing. Recurrent topics in Nannan Ji's work include Generative Adversarial Networks and Image Synthesis (7 papers), Neural Networks and Applications (5 papers) and Machine Learning and ELM (3 papers). Nannan Ji is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (7 papers), Neural Networks and Applications (5 papers) and Machine Learning and ELM (3 papers). Nannan Ji collaborates with scholars based in China and Hong Kong. Nannan Ji's co-authors include Chunxia Zhang, Yee Leung, Jianghong Ma, Sha Ji, Jiangshe Zhang, Yuhuan Wang, Chunxia Zhang, Gao Guo, Junying Hu and Fang Du and has published in prestigious journals such as Pattern Recognition, Neurocomputing and Knowledge-Based Systems.

In The Last Decade

Nannan Ji

18 papers receiving 325 citations

Peers

Nannan Ji

CVPR

CSE

Salima Ouadfel Algeria

Anton Akusok Finland

Edwin Vans Fiji

Zhixiang Xu China

Zhang Xuegong China

Daw-Ran Liou Taiwan

Martin Pawelczyk Germany

Yanpeng Qu China

Ayça Deniz Türkiye

Óscar Reyes Spain

Salima Ouadfel Algeria

Nannan Ji

287 ×2.1

200 ×2.4

CVPR

48 ×0.9

8 ×0.2

36 ×1.1

CSE

Citations per year, relative to Nannan Ji Nannan Ji (= 1×) peers Salima Ouadfel

Countries citing papers authored by Nannan Ji

Since Specialization

Citations

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

Fields of papers citing papers by Nannan Ji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nannan Ji

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

All Works

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19 of 19 papers shown

Ji, Nannan, Shilong Liu, Lingzhi Wei, et al.. (2024). Boosting oxygen reduction in acidic media through integration of Pt-Co alloy effect and strong interaction with carbon defects. Nano Research. 17(9). 7900–7908. 16 indexed citations

Wen, Yi, et al.. (2023). SAR image change detection based on Gabor wavelets and convolutional wavelet neural networks. Multimedia Tools and Applications. 82(20). 30895–30908. 1 indexed citations

Ji, Nannan, et al.. (2019). Hopf bifurcation analysis in a predator–prey model with time delay and food subsidies. Advances in Difference Equations. 2019(1). 8 indexed citations

Ji, Nannan, et al.. (2018). LncRNA SNHG14 promotes the progression of cervical cancer by regulating miR-206/YWHAZ. Pathology - Research and Practice. 215(4). 668–675. 50 indexed citations

Shi, Guang, et al.. (2018). A new variant of restricted Boltzmann machine with horizontal connections. Neural Computing and Applications. 31(10). 6521–6533. 2 indexed citations

Du, Fang, Jiangshe Zhang, Nannan Ji, Guang Shi, & Chunxia Zhang. (2018). An effective hierarchical extreme learning machine based multimodal fusion framework. Neurocomputing. 322. 141–150. 15 indexed citations

Du, Fang, et al.. (2018). Discriminative Representation Learning with Supervised Auto-encoder. Neural Processing Letters. 49(2). 507–520. 9 indexed citations

Hu, Junying, et al.. (2017). A modified version of Helmholtz machine by using a Restricted Boltzmann Machine to model the generative probability of the top layer. Neurocomputing. 267. 1–17. 4 indexed citations

Leung, Yee, Rongrong Li, & Nannan Ji. (2017). Application of extended Dempster–Shafer theory of evidence in accident probability estimation for dangerous goods transportation. Journal of Geographical Systems. 19(3). 249–271. 13 indexed citations

10.

Hu, Junying, et al.. (2017). A new regularized restricted Boltzmann machine based on class preserving. Knowledge-Based Systems. 123. 1–12. 11 indexed citations

11.

Zhang, Chunxia, et al.. (2016). Randomizing outputs to increase variable selection accuracy. Neurocomputing. 218. 91–102. 7 indexed citations

12.

Zhang, Jiangshe, et al.. (2014). Enhancing performance of the backpropagation algorithm via sparse response regularization. Neurocomputing. 153. 20–40. 13 indexed citations

13.

Ji, Nannan, et al.. (2014). A sparse-response deep belief network based on rate distortion theory. Pattern Recognition. 47(9). 3179–3191. 62 indexed citations

14.

Ji, Nannan, et al.. (2014). Enhancing performance of restricted Boltzmann machines via log-sum regularization. Knowledge-Based Systems. 63. 82–96. 19 indexed citations

15.

Zhang, Jiangshe, et al.. (2014). A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling. Mathematical Problems in Engineering. 2014(1). 14 indexed citations

16.

Ji, Nannan, et al.. (2014). Parallel tempering with equi-energy moves for training of restricted boltzmann machines. 165. 120–127. 3 indexed citations

17.

Ji, Nannan, et al.. (2014). Discriminative restricted Boltzmann machine for invariant pattern recognition with linear transformations. Pattern Recognition Letters. 45. 172–180. 6 indexed citations

18.

Zhang, Chunxia, et al.. (2013). Learning ensemble classifiers via restricted Boltzmann machines. Pattern Recognition Letters. 36. 161–170. 27 indexed citations

19.

Leung, Yee, Nannan Ji, & Jianghong Ma. (2012). An integrated information fusion approach based on the theory of evidence and group decision-making. Information Fusion. 14(4). 410–422. 55 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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