Feipei Lai

5.4k total citations
293 papers, 3.5k citations indexed

About

Feipei Lai is a scholar working on Computer Networks and Communications, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Feipei Lai has authored 293 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Computer Networks and Communications, 62 papers in Artificial Intelligence and 44 papers in Electrical and Electronic Engineering. Recurrent topics in Feipei Lai's work include Parallel Computing and Optimization Techniques (33 papers), Low-power high-performance VLSI design (21 papers) and EEG and Brain-Computer Interfaces (17 papers). Feipei Lai is often cited by papers focused on Parallel Computing and Optimization Techniques (33 papers), Low-power high-performance VLSI design (21 papers) and EEG and Brain-Computer Interfaces (17 papers). Feipei Lai collaborates with scholars based in Taiwan, United States and Germany. Feipei Lai's co-authors include Yu‐Fang Chung, Te‐Wei Ho, Hung‐Chang Lee, Tzer‐Shyong Chen, Zhen-Yu Wu, Chun‐Ta Huang, Yueh‐Chun Lee, Yi‐Ju Tsai, Chong‐Jen Yu and Jin‐Ming Wu and has published in prestigious journals such as PLoS ONE, NeuroImage and Scientific Reports.

In The Last Decade

Feipei Lai

273 papers receiving 3.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Feipei Lai Taiwan 30 774 720 542 395 343 293 3.5k
Ilias Maglogiannis Greece 32 1.2k 1.5× 906 1.3× 490 0.9× 210 0.5× 359 1.0× 263 4.3k
Karrar Hameed Abdulkareem Iraq 37 1.2k 1.6× 954 1.3× 703 1.3× 113 0.3× 298 0.9× 98 4.0k
John Yearwood Australia 30 1.4k 1.7× 560 0.8× 605 1.1× 87 0.2× 234 0.7× 182 3.8k
Giuseppe De Pietro Italy 32 1.7k 2.1× 400 0.6× 354 0.7× 210 0.5× 127 0.4× 211 4.4k
Sami Azam Australia 31 1.5k 1.9× 591 0.8× 710 1.3× 163 0.4× 291 0.8× 158 3.6k
Md. Milon Islam Bangladesh 34 1.8k 2.3× 866 1.2× 329 0.6× 161 0.4× 314 0.9× 58 4.5k
Chinmay Chakraborty India 38 1.4k 1.8× 1.2k 1.6× 830 1.5× 118 0.3× 591 1.7× 195 4.5k
Mohamed Hammad Egypt 28 822 1.1× 347 0.5× 343 0.6× 164 0.4× 155 0.5× 78 2.8k
Piyush Kumar Shukla India 30 842 1.1× 587 0.8× 369 0.7× 69 0.2× 341 1.0× 181 2.8k
Sally McClean United Kingdom 38 1.0k 1.3× 949 1.3× 551 1.0× 87 0.2× 424 1.2× 371 5.6k

Countries citing papers authored by Feipei Lai

Since Specialization
Citations

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

Fields of papers citing papers by Feipei Lai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feipei Lai

This figure shows the co-authorship network connecting the top 25 collaborators of Feipei Lai. A scholar is included among the top collaborators of Feipei Lai 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 Feipei Lai. Feipei Lai 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
1.
Wang, Farn, et al.. (2025). Real-time fall detection with ground height awareness using LiDAR and a camera of a mobile device. Biomedical Signal Processing and Control. 110. 108292–108292.
2.
3.
Chang, Tu‐Hsuan, et al.. (2024). Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit. Pediatric Pulmonology. 59(5). 1256–1265. 1 indexed citations
4.
Chang, Tu‐Hsuan, et al.. (2023). Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission. Journal of Microbiology Immunology and Infection. 56(4). 772–781. 14 indexed citations
5.
Huang, Hsien‐Liang, et al.. (2023). Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. Journal of Medical Internet Research. 25. e47366–e47366. 18 indexed citations
6.
Cheng, Hao‐Yuan, Tu‐Hsuan Chang, Te‐Wei Ho, et al.. (2022). Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach. JMIR Medical Informatics. 10(1). e28934–e28934. 8 indexed citations
7.
Lai, Feipei, et al.. (2022). Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study. JMIR Medical Informatics. 10(2). e33063–e33063. 24 indexed citations
8.
Hsu, Fu-Shun, et al.. (2022). A Progressively Expanded Database for Automated Lung Sound Analysis: An Update. Applied Sciences. 12(15). 7623–7623. 14 indexed citations
9.
Chu, Yuan-Chia, Feipei Lai, Kuan‐Chih Chen, et al.. (2022). Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study. JMIR Medical Informatics. 10(11). e41342–e41342. 8 indexed citations
10.
Lai, Feipei, et al.. (2021). Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study. JMIR Medical Informatics. 9(12). e22798–e22798. 14 indexed citations
11.
Kuo, Lu-Cheng, et al.. (2021). Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning. JMIR Medical Informatics. 9(8). e23230–e23230. 43 indexed citations
12.
Lai, Feipei, et al.. (2020). ICD-10 Auto-coding System Using Deep Learning. 2 indexed citations
13.
Wu, Jin‐Ming, et al.. (2020). A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence. Applied Sciences. 10(15). 5353–5353. 13 indexed citations
14.
Chu, Yuan-Chia, et al.. (2020). A Smartphone-Based Approach to Screening for Sudden Sensorineural Hearing Loss: Cross-Sectional Validity Study. JMIR mhealth and uhealth. 8(11). e23047–e23047. 17 indexed citations
15.
Chen, Yung-Wei, Te‐Wei Ho, Hao‐Chih Tai, et al.. (2019). Chronic wound assessment and infection detection method. BMC Medical Informatics and Decision Making. 19(1). 99–99. 29 indexed citations
16.
Chen, Yung-Wei, et al.. (2018). Surgical Wounds Assessment System for Self-Care. IEEE Transactions on Systems Man and Cybernetics Systems. 50(12). 5076–5091. 13 indexed citations
17.
Chen, Kuo‐Hsin, et al.. (2015). A cross-hospital cost and quality assessment system by extracting frequent physician order set from a nationwide Health Insurance Research Database. Computer Methods and Programs in Biomedicine. 120(3). 142–153. 1 indexed citations
18.
Wu, Jin‐Ming, et al.. (2015). Tablet PC-enabled application intervention for patients with gastric cancer undergoing gastrectomy. Computer Methods and Programs in Biomedicine. 119(2). 101–109. 16 indexed citations
19.
Chen, Yee‐Chun, Hui-Chi Lin, Yingyu Chen, et al.. (2009). Real-time Automated MDRO Surveillance System.. 764–769. 3 indexed citations
20.
Chen, Tzer‐Shyong, et al.. (1997). Modified Cryptographic Key Assignment Scheme for a Group-Oriented User Hierarchy. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 80(10). 2032–2034. 2 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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