Jun Kitazono

403 citations
21 papers · 233 · h-index 9

Impact in

    • Neural dynamics and brain function
    • Functional Brain Connectivity Studies
    • Visual perception and processing mechanisms
    • Face Recognition and Perception
    • EEG and Brain-Computer Interfaces

Papers in

Jun Kitazono

20 papers receiving 225 citations

Peers

Jun Kitazono
Comparison fields: 5 of 66
  • Cognitive Neuroscience 96
  • Artificial Intelligence 64
  • Signal Processing 20
  • Computational Mathematics 1
  • Computer Networks and Communications 33
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Waqar Hussain China
Geetanjali Sharma India
Muhammad Haseeb Aslam Pakistan
Muhammad Adeel Asghar Pakistan
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Citations per field
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Citations per year

Countries citing papers authored by Jun Kitazono

Since Specialization
Citations

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

Fields of papers citing papers by Jun Kitazono

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Kitazono, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Kitazono Line = papers co-authored together Jun Kitazono links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201344
2 201435
3 201730
4 201519
5 201719
6 201419
7 201414
8 202013
9 20229
10 20158
11 20224
12 20164
13 20213
14 20123
15 20162
16 20092
17 20162
18 20251
19 20251
20 20171

About Jun Kitazono

Jun Kitazono is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 233 indexed citations. Recurring topics across this work include Neural dynamics and brain function (8 papers), Functional Brain Connectivity Studies (7 papers), Spam and Phishing Detection (3 papers), Sentiment Analysis and Opinion Mining (3 papers), EEG and Brain-Computer Interfaces (3 papers), Network Security and Intrusion Detection (3 papers), Face and Expression Recognition (2 papers) and Electrochemical Analysis and Applications (2 papers). The work is most often cited by research in Cognitive Neuroscience (96 citations), Artificial Intelligence (64 citations), Signal Processing (20 citations), Computational Mathematics (1 citation) and Computer Networks and Communications (33 citations). Jun Kitazono has collaborated with scholars based in Japan, United States and Taiwan. Frequent co-authors include Seiichi Ozawa, Masato Okada, Masafumi Oizumi, Takayuki Sato, Mark D. Lescroart, Kenji Nagata, Manabu Tanifuji, Toshiaki Omori, Tao Ban and So Kanazawa. Their work appears in journals such as Journal of Neuroscience, iScience, Cerebral Cortex, Frontiers in Human Neuroscience and Journal of the Physical Society of Japan.

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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