I‐Chang Jou

405 citations
29 papers · 283 indexed · h-index 8
Topics
Neural Networks and Applications (6 papers)Coding theory and cryptography (5 papers)Cryptography and Residue Arithmetic (5 papers)

In The Last Decade

I‐Chang Jou

23 papers receiving 254 citations

Peers

I‐Chang Jou
Comparison fields: 5 of 44
  • Artificial Intelligence 187
  • Computer Vision and Pattern Recognition 136
  • Information Systems 96
  • Signal Processing 37
  • Media Technology 29
Replace Xiaochen Lian with:
Xiaochen Lian China
Ramanujan S. Kashi United States
Luca Henzen Switzerland
Caimu Tang United States
Mahdi Jampour Iran
Maurício Breternitz United States
Youshou Wu China
Enrique San Millán Spain
Bin Cao Singapore
Patrick Longa United States
I‐Chang Jou relative to Xiaochen Lian China Xiaochen Lian's profile →
Citations per field
00.5×1.5×2.5×
Xiaochen Lian · 1×
Citations per year

Countries citing papers authored by I‐Chang Jou

Since Specialization
Citations

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

Fields of papers citing papers by I‐Chang Jou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I‐Chang Jou. 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 I‐Chang Jou. The network helps show where I‐Chang Jou may publish in the future.

Co-authorship network of co-authors of I‐Chang Jou

This figure shows the co-authorship network connecting the top 25 collaborators of I‐Chang Jou. A scholar is included among the top collaborators of I‐Chang Jou 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 I‐Chang Jou. I‐Chang Jou 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
#WorkIndexed citations
1 0
2 3
3 7
4 4
5 72
6 5
7 0
8
A Gray Level Watermarking Algorithm Using Double Layer Hidden Approach
9
9 0
10 0
11 1
12 3
13 2
14 53
15 19
16 49
17 0
18 1
19 2
20 13

About I‐Chang Jou

I‐Chang Jou is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 29 papers that have together received 283 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Coding theory and cryptography (5 papers) and Cryptography and Residue Arithmetic (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (136 citations), Artificial Intelligence (187 citations) and Information Systems (96 citations). I‐Chang Jou has collaborated with scholars based in Taiwan, United States and Russia. Frequent co-authors include Suh-Yin Lee, Chiou‐Yng Lee, Jenq–Neng Hwang, Shyh-Rong Lay, Yu Hen Hu, Shih‐Chang Hsia, Weichang Du, Chung‐Yu Wu, L. J. Chang and Shiang‐Bin Jong. Their work appears in journals such as Proceedings of the IEEE, Pattern Recognition and IEEE Transactions on Computers.

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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