James Jin Kang

1.7k total citations · 2 hit papers
37 papers, 1.0k citations indexed

About

James Jin Kang is a scholar working on Computer Networks and Communications, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, James Jin Kang has authored 37 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Networks and Communications, 16 papers in Biomedical Engineering and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in James Jin Kang's work include Wireless Body Area Networks (13 papers), IoT and Edge/Fog Computing (12 papers) and Context-Aware Activity Recognition Systems (10 papers). James Jin Kang is often cited by papers focused on Wireless Body Area Networks (13 papers), IoT and Edge/Fog Computing (12 papers) and Context-Aware Activity Recognition Systems (10 papers). James Jin Kang collaborates with scholars based in Australia, Iran and Taiwan. James Jin Kang's co-authors include Muhammad Fazal Ijaz, Akash Kumar Bhoi, Parvathaneni Naga Srinivasu, Wonjoon Kim, Saifur Rahman, Jingwen Zhou, Sasan Adibi, Patryk Szewczyk, Wencheng Yang and Paul Haskell‐Dowland and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

James Jin Kang

36 papers receiving 981 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.

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

2021 495 citations Parvathaneni Naga Srinivasu, Muhammad Fazal Ijaz et al. Sensors profile →
A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

2023 142 citations Saifur Rahman, Jingwen Zhou et al. Sensors profile →

Peers

James Jin Kang

AI

CNC

ONCOL

CVPR

BE

Parvathaneni Naga Srinivasu India

Atef Zaguia Saudi Arabia

Yogesh Kumar India

Halil Murat Ünver Türkiye

Wei Hu China

Daniel Sierra-Sosa United States

Azhar Imran Pakistan

Hüseyin Şeker United Kingdom

Jaehyuk Cha South Korea

Abdullah Alqahtani Saudi Arabia

Parvathaneni Naga Srinivasu India

James Jin Kang

673 ×2.1

AI

168 ×0.7

CNC

225 ×1.1

ONCOL

385 ×2.1

CVPR

139 ×0.9

BE

Citations per year, relative to James Jin Kang James Jin Kang (= 1×) peers Parvathaneni Naga Srinivasu

Countries citing papers authored by James Jin Kang

Since Specialization

Citations

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

Fields of papers citing papers by James Jin Kang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James Jin 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 James Jin Kang. The network helps show where James Jin Kang may publish in the future.

Co-authorship network of co-authors of James Jin Kang

This figure shows the co-authorship network connecting the top 25 collaborators of James Jin Kang. A scholar is included among the top collaborators of James Jin 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 James Jin Kang. James Jin Kang 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.

Wajrak, Magdalena, et al.. (2023). Nafion Modified Titanium Nitride pH Sensor for Future Biomedical Applications. Sensors. 23(2). 699–699. 12 indexed citations

2.

Rahman, Saifur, et al.. (2023). A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors. 23(9). 4178–4178. 142 indexed citations breakdown →

3.

Sikos, Leslie F., et al.. (2023). DoS/DDoS-MQTT-IoT: A dataset for evaluating intrusions in IoT networks using the MQTT protocol. Computer Networks. 231. 109809–109809. 35 indexed citations

4.

Srinivasu, Parvathaneni Naga, et al.. (2021). Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. 21(8). 2852–2852. 495 indexed citations breakdown →

5.

Kang, James Jin, Mahdi Dibaei, Gang Luo, et al.. (2021). An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study. Sensors. 21(1). 312–312. 10 indexed citations

6.

Kang, James Jin, Mahdi Dibaei, Gang Luo, Wencheng Yang, & Xi Zheng. (2020). A Privacy-Preserving Data Inference Framework for Internet of Health Things Networks. Australasian Journal of Paramedicine. 1209–1214. 8 indexed citations

7.

Kang, James Jin. (2020). A Military Mobile Network Design: mHealth, IoT and Low Power Wide Area Networks. Australasian Journal of Paramedicine. 1–3. 6 indexed citations

8.

Yang, Wencheng, et al.. (2020). Secure Fingerprint Authentication with Homomorphic Encryption. 1–6. 15 indexed citations

9.

Kang, James Jin, et al.. (2019). An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization. Big Data and Cognitive Computing. 3(1). 7–7. 1 indexed citations

10.

Kang, James Jin, et al.. (2019). Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms. SHILAP Revista de lepidopterología. 7(1). 25–25. 4 indexed citations

11.

Kang, James Jin, et al.. (2018). Trusted Time-Based Verification Model for Automatic Man-in-the-Middle Attack Detection in Cybersecurity. Cryptography. 2(4). 38–38. 8 indexed citations

12.

Kang, James Jin, et al.. (2017). INTELLIGENT PERSONAL HEALTH DEVICES CONVERGED WITH INTERNET OF THINGS NETWORKS. Journal of Multimedia. 12. 197–212. 9 indexed citations

13.

Kang, James Jin. (2017). An inference system framework for personal sensor devices in mobile health and internet of things networks. Deakin Research Online (Deakin University). 16 indexed citations

14.

Kang, James Jin, et al.. (2017). Application of an Emergency Alarm System for Physiological Sensors Utilizing Smart Devices. Technologies. 5(2). 26–26. 14 indexed citations

15.

Kang, James Jin & Sasan Adibi. (2017). Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN). SHILAP Revista de lepidopterología. 5(4). 65–65. 2 indexed citations

16.

Kang, James Jin, et al.. (2016). Inference System of Body Sensors for Health and Internet of Things Networks. 94–98. 8 indexed citations

17.

Kang, James Jin, et al.. (2016). Inference of Personal Sensors in the Internet of Things. 2(1). 1–23. 9 indexed citations

18.

Kang, James Jin, et al.. (2016). Data Processing of Physiological Sensor Data and Alarm Determination Utilising Activity Recognition. 2(1). 108–131. 12 indexed citations

19.

Kang, James Jin, et al.. (2015). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. 5–10. 12 indexed citations

20.

Kang, James Jin & Sasan Adibi. (2015). A Review of Security Protocols in mHealth Wireless Body Area Networks (WBAN). Communications in computer and information science. 61–83. 31 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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