Hsin‐Hung Chou

520 total citations
33 papers, 310 citations indexed

About

Hsin‐Hung Chou is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Hsin‐Hung Chou has authored 33 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Electrical and Electronic Engineering, 8 papers in Artificial Intelligence and 6 papers in Computer Networks and Communications. Recurrent topics in Hsin‐Hung Chou's work include Sensorless Control of Electric Motors (6 papers), Advanced Optical Network Technologies (5 papers) and Interconnection Networks and Systems (5 papers). Hsin‐Hung Chou is often cited by papers focused on Sensorless Control of Electric Motors (6 papers), Advanced Optical Network Technologies (5 papers) and Interconnection Networks and Systems (5 papers). Hsin‐Hung Chou collaborates with scholars based in Taiwan, United States and Vietnam. Hsin‐Hung Chou's co-authors include Sun‐Yuan Hsieh, Ching‐Te Chiu, Ying‐Shieh Kung, Che-Nan Kuo, Stone Cheng, Nguyen Vu Quynh, Nai-Wen Chang, Peng‐Chan Lin, Meng‐Ru Shen and Liwei Cheng and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Neurocomputing.

In The Last Decade

Hsin‐Hung Chou

28 papers receiving 301 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hsin‐Hung Chou Taiwan 9 88 64 56 53 49 33 310
Jorge Estrela da Silva Portugal 10 95 1.1× 59 0.9× 94 1.7× 62 1.2× 75 1.5× 36 381
Zhang Shi China 11 74 0.8× 113 1.8× 52 0.9× 67 1.3× 57 1.2× 87 438
Harsukhpreet Singh India 7 127 1.4× 77 1.2× 71 1.3× 36 0.7× 29 0.6× 20 361
Shuang Zhou China 12 106 1.2× 50 0.8× 75 1.3× 52 1.0× 106 2.2× 68 447
Junhong Xu United States 9 100 1.1× 53 0.8× 46 0.8× 62 1.2× 81 1.7× 26 306
Yang Jia China 12 65 0.7× 35 0.5× 65 1.2× 12 0.2× 193 3.9× 54 410
Rohin Daruwala India 9 130 1.5× 98 1.5× 36 0.6× 17 0.3× 113 2.3× 60 385
Shaohua Li China 8 58 0.7× 95 1.5× 87 1.6× 13 0.2× 94 1.9× 30 426

Countries citing papers authored by Hsin‐Hung Chou

Since Specialization
Citations

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

Fields of papers citing papers by Hsin‐Hung Chou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hsin‐Hung Chou. 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 Hsin‐Hung Chou. The network helps show where Hsin‐Hung Chou may publish in the future.

Co-authorship network of co-authors of Hsin‐Hung Chou

This figure shows the co-authorship network connecting the top 25 collaborators of Hsin‐Hung Chou. A scholar is included among the top collaborators of Hsin‐Hung Chou 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 Hsin‐Hung Chou. Hsin‐Hung Chou 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.
Hsieh, Sun‐Yuan, et al.. (2025). Deep learning-based prediction of mortality using brain midline shift and clinical information. Heliyon. 11(2). e41271–e41271. 3 indexed citations
2.
Chou, Hsin‐Hung, et al.. (2025). Machine learning approach for predicting ovarian maturation in Penaeus monodon. Smart Agricultural Technology. 12. 101597–101597.
3.
Chou, Hsin‐Hung, et al.. (2025). System-level integration of deep learning and computer vision for contact ring seal defect detection in semiconductor manufacturing. Expert Systems with Applications. 298. 129551–129551.
4.
Lee, Dasheng, Xiwei Huang, Hsiang Chen, et al.. (2024). Thermal Comfort Model Established by Using Machine Learning Strategies Based on Physiological Parameters in Hot and Cold Environments. Indoor Air. 2024(1). 5 indexed citations
5.
Lin, Yung‐Chieh, et al.. (2024). Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs. Journal of Imaging Informatics in Medicine. 37(5). 2063–2073. 3 indexed citations
6.
Chou, Hsin‐Hung, et al.. (2024). Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results. The American Journal of Emergency Medicine. 85. 80–85. 4 indexed citations
7.
Lin, Peng‐Chan, et al.. (2023). Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(5). 3267–3277. 8 indexed citations
8.
Cheng, Liwei, et al.. (2023). Automated detection of vertebral fractures from X-ray images: A novel machine learning model and survey of the field. Neurocomputing. 566. 126946–126946. 16 indexed citations
9.
Li, Yanjun, Hsin‐Hung Chou, Peng‐Chan Lin, Meng‐Ru Shen, & Sun‐Yuan Hsieh. (2023). A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images. Journal of Translational Medicine. 21(1). 731–731. 6 indexed citations
10.
Lien, Wan‐Ching, et al.. (2022). Detecting Hydronephrosis Through Ultrasound Images Using State-of-the-Art Deep Learning Models. Ultrasound in Medicine & Biology. 49(3). 723–733. 10 indexed citations
11.
Lin, Peng‐Chan, et al.. (2021). Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(1). 606–615. 4 indexed citations
12.
Kung, Ying‐Shieh, et al.. (2016). Field programmable gate array–based servo control integrated chip for a six-axis articulated robot manipulator. Advances in Mechanical Engineering. 8(5). 2 indexed citations
13.
Kung, Ying‐Shieh, et al.. (2015). ModelSim/Simulink Cosimulation and FPGA Realization of a Multiaxis Motion Controller. Mathematical Problems in Engineering. 2015. 1–17. 3 indexed citations
14.
Kung, Ying‐Shieh, et al.. (2015). Digital hardware implementation of a radial basis function neural network. Computers & Electrical Engineering. 53. 106–121. 17 indexed citations
15.
16.
Cheng, Jay, et al.. (2013). A necessary and sufficient condition for SDL constructions of optical FIFO queues. 57. 2339–2345. 2 indexed citations
17.
Kuo, Che-Nan, Hsin‐Hung Chou, Nai-Wen Chang, & Sun‐Yuan Hsieh. (2012). Fault-tolerant path embedding in folded hypercubes with both node and edge faults. Theoretical Computer Science. 475. 82–91. 33 indexed citations
18.
Chou, Hsin‐Hung, Ying‐Shieh Kung, Nguyen Vu Quynh, & Stone Cheng. (2012). Optimized FPGA design, verification and implementation of a neuro-fuzzy controller for PMSM drives. Mathematics and Computers in Simulation. 90. 28–44. 32 indexed citations
19.
Cheng, Jay, et al.. (2011). Average Number of Recirculations in SDL Constructions of Optical Priority Queues. IEEE Communications Letters. 15(8). 899–901. 3 indexed citations
20.
Chou, Hsin‐Hung, Ming‐Tat Ko, Chin-Wen Ho, & Gen-Huey Chen. (2007). Node-searching problem on block graphs. Discrete Applied Mathematics. 156(1). 55–75. 3 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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