Ping‐Feng Pai

4.9k total citations · 1 hit paper
90 papers, 3.6k citations indexed

About

Ping‐Feng Pai is a scholar working on Management Science and Operations Research, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Ping‐Feng Pai has authored 90 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Management Science and Operations Research, 30 papers in Electrical and Electronic Engineering and 27 papers in Artificial Intelligence. Recurrent topics in Ping‐Feng Pai's work include Stock Market Forecasting Methods (29 papers), Energy Load and Power Forecasting (27 papers) and Forecasting Techniques and Applications (18 papers). Ping‐Feng Pai is often cited by papers focused on Stock Market Forecasting Methods (29 papers), Energy Load and Power Forecasting (27 papers) and Forecasting Techniques and Applications (18 papers). Ping‐Feng Pai collaborates with scholars based in Taiwan, United States and Japan. Ping‐Feng Pai's co-authors include Wei‐Chiang Hong, Chih-Sheng Lin, Kuo-Ping Lin, Ping-Teng Chang, Ming‐Lang Tseng, Jung-Pin Lai, Wenchang Wang, Chen‐Tung Arthur Chen, Yu-Ming Chang and Kuo-Chen Hung and has published in prestigious journals such as Journal of Cleaner Production, Expert Systems with Applications and Energy Conversion and Management.

In The Last Decade

Ping‐Feng Pai

87 papers receiving 3.4k citations

Hit Papers

A hybrid ARIMA and support vector machines model in stock... 2004 2026 2011 2018 2004 200 400 600
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.

  1. A hybrid ARIMA and support vector machines model in stock price forecasting
    2004 723 citations Ping‐Feng Pai et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ping‐Feng Pai Taiwan 28 1.5k 1.4k 945 414 347 90 3.6k
B. Eddy Patuwo United States 13 1.9k 1.2× 1.2k 0.8× 1.3k 1.4× 535 1.3× 467 1.3× 23 4.3k
Morteza Saberi Iran 30 1.6k 1.1× 903 0.7× 830 0.9× 377 0.9× 181 0.5× 198 4.0k
Yingjie Yang United Kingdom 31 2.2k 1.5× 889 0.7× 486 0.5× 403 1.0× 286 0.8× 195 4.0k
Liang Liang China 28 728 0.5× 866 0.6× 564 0.6× 488 1.2× 346 1.0× 250 3.3k
Mehdi Bijari Iran 17 1.3k 0.8× 860 0.6× 628 0.7× 371 0.9× 246 0.7× 43 2.4k
S.F. Ghaderi Iran 30 1.2k 0.8× 1.3k 0.9× 428 0.5× 380 0.9× 360 1.0× 119 2.9k
Vassilios Assimakopoulos Greece 25 2.2k 1.5× 872 0.6× 540 0.6× 545 1.3× 249 0.7× 74 3.8k
Haixiang Guo China 29 366 0.2× 798 0.6× 1.6k 1.7× 542 1.3× 494 1.4× 105 4.4k
Evangelos Spiliotis Greece 23 1.6k 1.1× 951 0.7× 542 0.6× 390 0.9× 260 0.7× 70 3.1k
Yi‐Chung Hu Taiwan 30 1.2k 0.8× 446 0.3× 699 0.7× 246 0.6× 111 0.3× 142 2.5k

Countries citing papers authored by Ping‐Feng Pai

Since Specialization
Citations

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

Fields of papers citing papers by Ping‐Feng Pai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ping‐Feng Pai. 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 Ping‐Feng Pai. The network helps show where Ping‐Feng Pai may publish in the future.

Co-authorship network of co-authors of Ping‐Feng Pai

This figure shows the co-authorship network connecting the top 25 collaborators of Ping‐Feng Pai. A scholar is included among the top collaborators of Ping‐Feng Pai 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 Ping‐Feng Pai. Ping‐Feng Pai 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.
Lai, Jung-Pin, et al.. (2025). Predicting Thermal Resistance of Packaging Design by Machine Learning Models. Micromachines. 16(3). 350–350. 2 indexed citations
2.
Hsu, Wei‐Hsiu, et al.. (2025). Forecasting Corporate Financial Performance Using Deep Learning with Environmental, Social, and Governance Data. Electronics. 14(3). 417–417. 1 indexed citations
3.
Pai, Ping‐Feng, et al.. (2024). Forecasting Flower Prices by Long Short-Term Memory Model with Optuna. Electronics. 13(18). 3646–3646. 2 indexed citations
4.
Lai, Jung-Pin, et al.. (2024). Using Deep Learning, Optuna, and Digital Images to Identify Necrotizing Fasciitis. Electronics. 13(22). 4421–4421. 3 indexed citations
5.
Yang, Wen‐Chieh, et al.. (2023). Using Medical Data and Clustering Techniques for a Smart Healthcare System. Electronics. 13(1). 140–140. 10 indexed citations
6.
Lai, Jung-Pin, et al.. (2023). Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis. Micromachines. 14(2). 265–265. 34 indexed citations
7.
Lai, Jung-Pin, et al.. (2023). A Study of Optimization in Deep Neural Networks for Regression. Electronics. 12(14). 3071–3071. 17 indexed citations
8.
Pai, Ping‐Feng, et al.. (2022). The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection. Electronics. 11(6). 887–887. 17 indexed citations
9.
Pai, Ping‐Feng, et al.. (2022). Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing. Water Resources Management. 36(13). 5207–5223. 4 indexed citations
10.
Pai, Ping‐Feng, et al.. (2022). Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis. Electronics. 11(21). 3513–3513. 21 indexed citations
11.
Li, Yushan, et al.. (2022). Forecasting inflation rates be extreme gradient boosting with the genetic algorithm. Journal of Ambient Intelligence and Humanized Computing. 14(3). 2211–2220. 6 indexed citations
12.
Pai, Ping‐Feng, Ming-Fu Hsu, & Lin Lin. (2013). Enhancing decisions with life cycle analysis for risk management. Neural Computing and Applications. 24(7-8). 1717–1724. 3 indexed citations
13.
Chen, Chen‐Tung Arthur, et al.. (2011). Applying Linguistic VIKOR and Knowledge Map in Personnel Selection. Asia Pacific Management Review. 16(4). 491–502. 14 indexed citations
14.
Pai, Ping‐Feng, et al.. (2010). Computer-assisted audit techniques based on an enhanced rough set model. 207–212. 1 indexed citations
15.
Chen, Chen‐Tung Arthur, et al.. (2010). An Integrated Methodology using Linguistic PROMETHEE and Maximum Deviation Method for Third-party Logistics Supplier Selection. International Journal of Computational Intelligence Systems. 3(4). 438–451. 41 indexed citations
16.
Lin, Kuo-Ping & Ping‐Feng Pai. (2010). A fuzzy support vector regression model for business cycle predictions. Expert Systems with Applications. 37(7). 5430–5435. 33 indexed citations
17.
Chang, Ping-Teng, et al.. (2008). Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem. International Journal of Production Research. 46(20). 5719–5759. 23 indexed citations
18.
Pai, Ping‐Feng. (2006). System reliability forecasting by support vector machines with genetic algorithms. Mathematical and Computer Modelling. 43(3-4). 262–274. 108 indexed citations
19.
Pai, Ping‐Feng, et al.. (2005). Forecasting Electric Load by Support Vector Machines with Genetic Algorithms. Journal of Advanced Computational Intelligence and Intelligent Informatics. 9(2). 134–141. 7 indexed citations
20.
Pai, Ping‐Feng. (2003). Capacitated Lot size problems with fuzzy capacity. Mathematical and Computer Modelling. 38(5-6). 661–669. 7 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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