In-Ho Kang

712 citations
20 papers · 419 indexed · h-index 6
Topics
Topic Modeling (5 papers)Multimodal Machine Learning Applications (4 papers)Natural Language Processing Techniques (4 papers)
Journals
Expert Systems with ApplicationsACM Transactions on Information SystemsIEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)

In The Last Decade

In-Ho Kang

16 papers receiving 392 citations

Peers

In-Ho Kang
Comparison fields: 5 of 57
  • Artificial Intelligence 294
  • Information Systems 170
  • Computer Vision and Pattern Recognition 83
  • Computer Networks and Communications 53
  • Signal Processing 50
Replace Bhavana Dalvi with:
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Alexander Zhou Hong Kong
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In-Ho Kang relative to Bhavana Dalvi United States Bhavana Dalvi's profile →
Citations per field
00.5×1.5×
Bhavana Dalvi · 1×
Citations per year

Countries citing papers authored by In-Ho Kang

Since Specialization
Citations

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

Fields of papers citing papers by In-Ho Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by In-Ho Kang. 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 In-Ho Kang. The network helps show where In-Ho Kang may publish in the future.

Co-authorship network of co-authors of In-Ho Kang

This figure shows the co-authorship network connecting the top 25 collaborators of In-Ho Kang. A scholar is included among the top collaborators of In-Ho Kang 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 In-Ho Kang. In-Ho Kang 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 4
3 2
4 1
5 21
6 111
7 5
8 0
9 1
10 1
11 32
12
The Effectiveness of Barista Educational Programs on Occupational Selection and Recommendation Intention
1
13 42
14 1
15
The Asymmetric Relationship between Hospital Service Attributes and Patient Satisfaction: An Implication to Important-Performance Analysis Application
1
16
The impact of the strategic posture to the learning and growth perspective of BSC
1
17
Differentiated Intrusion Detection and SVDD-based Feature Selection for Anomaly Detection
1
18
A Study on the Influences of Urban Area Expansion by Developing a Large Scale Residential District on Changes of Urban Spatial Structure - In the Daejeon Metropolitan City -
1
19 170
20 23

About In-Ho Kang

In-Ho Kang is a scholar working on Leadership and Management, Artificial Intelligence and Signal Processing, having authored 20 papers that have together received 419 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Multimodal Machine Learning Applications (4 papers) and Natural Language Processing Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (294 citations), Information Systems (170 citations) and Signal Processing (50 citations). In-Ho Kang has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Nojun Kwak, Myong K. Jeong, Young‐Seon Jeong, Jaeho Kim, Key‐Sun Choi, Tetsuya Sakai, Seung‐Hoon Na and Sungjin Park. Their work appears in journals such as Expert Systems with Applications, ACM Transactions on Information Systems and IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews).

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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