āļžāļēāļžāļīāļĻ āļ§āļ‡āļĻāđŒāļŠāļąāļĒāļŠāļļāļ§āļąāļ’āļ™āđŒ
āļĢāļ­āļ‡āļĻāļēāļŠāļ•āļĢāļēāļˆāļēāļĢāļĒāđŒ
āļ āļēāļ„āļ§āļīāļŠāļēāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļ­āļļāļ•āļŠāļēāļŦāļāļēāļĢ āļ„āļ“āļ°āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļ•āļĢāđŒ āļšāļēāļ‡āđ€āļ‚āļ™
papis.w@ku.ac.th
0-2797-0999
EDUCATION
  • Ph.D.(Industrial Engineering and Management), Northwestern University, USA, 2561



RESOURCE
āđāļŦāļĨāđˆāļ‡āļ—āļĩāđˆāļĄāļē
āļˆāļģāļ™āļ§āļ™āļŦāļ™āđˆāļ§āļĒāļ›āļāļīāļšāļąāļ•āļīāļāļēāļĢāļ—āļĩāđˆāđ€āļ‚āđ‰āļēāļĢāđˆāļ§āļĄ 0 āļŦāļ™āđˆāļ§āļĒ āļˆāļģāļ™āļ§āļ™āđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĄāļ·āļ­āļ§āļīāļˆāļąāļĒ 0 āļŠāļīāđ‰āļ™ āļŠāļ–āļēāļ™āļ—āļĩāđˆāļ›āļāļīāļšāļąāļ•āļīāļ‡āļēāļ™āļ§āļīāļˆāļąāļĒ āļŦāđ‰āļ­āļ‡ 109 103 āđāļĨāļ° 104 āļŠāļąāđ‰āļ™ 1 āļ­āļēāļ„āļēāļĢāļ•āļķāļ āļĻ āļ„āļļāļ“āļŠāļ§āļ™āļŠāļĄ āļˆāļąāļ™āļ—āļĢāļ°āđ€āļ›āļēāļĢāļĒāļ° āļ āļēāļ„āļ§āļīāļŠāļēāļ„āļŦāļāļĢāļĢāļĄāļĻāļēāļŠāļ•āļĢāđŒ
āđāļŠāļ”āļ‡āđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄ


āļœāļĨāļ‡āļēāļ™
Works
PROJECT
āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ—āļĩāđˆāļ­āļĒāļđāđˆāļĢāļ°āļŦāļ§āđˆāļēāļ‡āļāļēāļĢāļ”āļģāđ€āļ™āļīāļ™āļāļēāļĢ: 5
āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ—āļĩāđˆāđ€āļŠāļĢāđ‡āļˆāļŠāļīāđ‰āļ™āđāļĨāđ‰āļ§: 15
OUTPUT
āļšāļ—āļ„āļ§āļēāļĄ: 31
OUTCOME
AWARD
āļ›āļĢāļ°āļāļēāļĻāđ€āļāļĩāļĒāļĢāļ•āļīāļ„āļļāļ“/āļĢāļēāļ‡āļ§āļąāļĨāļ™āļąāļāļ§āļīāļˆāļąāļĒ: 0
āļĢāļēāļ‡āļ§āļąāļĨāļœāļĨāļ‡āļēāļ™āļ§āļīāļˆāļąāļĒ/āļŠāļīāđˆāļ‡āļ›āļĢāļ°āļ”āļīāļĐāļāđŒ: 0
āļĢāļēāļ‡āļ§āļąāļĨāļœāļĨāļ‡āļēāļ™āļ™āļģāđ€āļŠāļ™āļ­āđƒāļ™āļāļēāļĢāļ›āļĢāļ°āļŠāļļāļĄāļ§āļīāļŠāļēāļāļēāļĢ: 0


INTEREST
āļ„āļ§āļēāļĄāļŠāļ™āđƒāļˆ
data analytics, data mining, text analytics


Expertise Cloud
āļ„āļ§āļēāļĄāđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļ
Person Relationship
āļ™āļąāļāļ§āļīāļˆāļąāļĒ
āļ—āļĩāđˆāļĄāļĩāļœāļĨāļ‡āļēāļ™āļĄāļēāļāļ—āļĩāđˆāļŠāļļāļ” 10 āļ„āļ™āđāļĢāļ
Scopus h-index
h-index: 9
# Document title Authors Year Source Cited by
1 Anomaly detection using a sliding window technique and data imputation with machine learning for hydrological time series Kulanuwat L., Chantrapornchai C., Maleewong M., Wongchaisuwat P., Wimala S., Sarinnapakorn K., Boonya-Aroonnet S. 2021
Water (Switzerland),
13(13), 1862
64
2 Automated quality inspection of baby corn using image processing and deep learning Wonggasem K., Chakranon P., Wongchaisuwat P. 2024
Artificial Intelligence in Agriculture,
11, pp. 61-69
22
3 Article Hydrological Time Series Clustering: A Case Study of Telemetry Stations in Thailand Prakaisak I., Wongchaisuwat P. 2022
Water (Switzerland),
14(13), 2095
15
4 A semi-supervised learning approach to enhance health care community-based question answering: A case study in alcoholism Wongchaisuwat P., Klabjan D., Jonnalagadda S.R. 2016
JMIR Medical Informatics,
4(3), e24
14
5 Predicting litigation likelihood and time to litigation for patents Wongchaisuwat P., Klabjan D., McGinnis J. 2017
Proceedings of the International Conference on Artificial Intelligence and Law,
pp. 257-260
14
6 A method of music autotagging based on audio and lyrics Wang H.C., Syu S.W., Wongchaisuwat P. 2021
Multimedia Tools and Applications
13
7 In-person verification of deep learning algorithm for diabetic retinopathy screening using different techniques across fundus image devices Wongchaisuwat N., Trinavarat A., Rodanant N., Thoongsuwan S., Phasukkijwatana N., Prakhunhungsit S., Preechasuk L., Wongchaisuwat P. 2021
Translational Vision Science and Technology,
10(13), 17
13
8 Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography Wongchaisuwat P., Thamphithak R., Jitpukdee P., Wongchaisuwat N. 2022
Translational Vision Science and Technology,
11(10), 16
13
9 Machine learning algorithms for predicting air pollutants Boonphun J., Kaisornsawad C., Wongchaisuwat P. 2019
E3S Web of Conferences,
120, 03004
12
10 Automatic Keyword Extraction Using TextRank Wongchaisuwat P. 2019
2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019,
pp. 377-381, 8714976
8
11 Utilizing sequential modeling in collaborative method for flood forecasting Thaisiam W., Yomwilai K., Wongchaisuwat P. 2024
Journal of Hydrology,
636, 131290
7
12 Rapid maize seed vigor classification using deep learning and hyperspectral imaging techniques Wongchaisuwat P., Chakranon P., Yinpin A., Onwimol D., Wonggasem K. 2025
Smart Agricultural Technology,
10, 100820
7
13 Enhancing a Multi-Step Discharge Prediction with Deep Learning and a Response Time Parameter Thaisiam W., Saelo W., Wongchaisuwat P. 2022
Water (Switzerland),
14(18), 2898
5
14 Utilization of Machine Learning and Hyperspectral Imaging Technologies for Classifying Coated Maize Seed Vigor: A Case Study on the Assessment of Seed DNA Repair Capability Wonggasem K., Wongchaisuwat P., Chakranon P., Onwimol D. 2024
Agronomy,
14(9), 1991
3
15 Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging Onwimol D., Chakranon P., Wonggasem K., Wongchaisuwat P. 2025
Journal of Agriculture and Food Research,
21, 101836
2
16 Deep Learning for Music Genre Classification: A case study of Thai music Sawaengsawangarom P., Phongoen S., Wongchaisuwat P. 2025
Proceedings of 2025 9th International Conference on Control Engineering and Artificial Intelligence Cceai 2025,
pp. 40-44
2
17 Forecasting Burned Areas of Wildfires: A Case Study of Mae Hong Son Province in Thailand Sawetsuthipan T., Wongchaisuwat P. 2023
2023 4th International Conference on Computers and Artificial Intelligence Technology, CAIT 2023,
pp. 52-56
2
18 Automated classification of polypoidal choroidal vasculopathy and wet age-related macular degeneration by spectral domain optical coherence tomography using self-supervised learning Wongchaisuwat N., Thamphithak R., Watunyuta P., Wongchaisuwat P. 2023
Procedia Computer Science,
220, pp. 1003-1008
2
19 Semantic similarity measure for Thai language Wongchaisuwat P. 2018
2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018 - Proceedings,
8692800
2
20 Factors Affecting Crop Choices in Thailand: Rice or Sugarcane? Jaijit S., Paoprasert N., Wongchaisuwat P., Kittipadakul P. 2023
Industrial Engineering and Management Systems,
22(3), pp. 244-258
1
21 A spatiotemporal deep learning ensemble for multi-step PM2.5 prediction: A case study of Bangkok metropolitan region in Thailand Kaewbundit V., Churngam C., Wongchaisuwat P. 2025
Atmospheric Pollution Research,
16(3), 102406
1
22 A stochastic game model for infectious disease management decisions in schools Sawetsuthipan T., Paoprasert N., Wongchaisuwat P. 2025
Asia Pacific Management Review,
30(2), 100343
0
23 Application of machine learning to identify key factors influencing agricultural workers’ mental health: A case study of Thai farmers Wongchaisuwat P., Kaewbundit V., Noomnual S. 2025
Health Informatics Journal,
31(4), 14604582251388827
0
24 Multi-step reservoir inflow prediction using a rolling window strategy and decomposed LSTM Thaisiam W., Rattanapant P., Kraisornnukhor P., Wongchaisuwat P. 2025
Water Science and Engineering
0
25 Data envelopment analysis for identifying the most suitable cassava cultivar: a case study of various cultivated areas in Thailand Paoprasert N., Paisaltanakij W., Kittipadakul P., Wongchaisuwat P. 2023
International Journal of Innovation and Learning,
34(4), pp. 368-379
0
26 Analyzing Hybrid Deep Learning Models with Decomposition Methods: A Case Study of RSS3 Price in Thailand Kraisornnukhor P., Wongchaisuwat P., Paoprasert N., Mungwattana A. 2023
Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023,
pp. 237-241
0
27 Multi-Step Rubber Price Prediction Using Deep Learning Models with External Factors Kraisornnukhor P., Wongchaisuwat P., Paoprasert N., Mungwattana A. 2023
IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC),
pp. 1125-1129
0
28 Truth validation with evidence Wongchaisuwat P., Klabjan D. 2022
Knowledge and Information Systems
0
āļĄāļŦāļēāļ§āļīāļ—āļĒāļēāļĨāļąāļĒāđ€āļāļĐāļ•āļĢāļĻāļēāļŠāļ•āļĢāđŒ www.ku.ac.th āđ€āļĨāļ‚āļ—āļĩāđˆ 50 āļ–āļ™āļ™āļ‡āļēāļĄāļ§āļ‡āļĻāđŒāļ§āļēāļ™ āđāļ‚āļ§āļ‡āļĨāļēāļ”āļĒāļēāļ§ āđ€āļ‚āļ•āļˆāļ•āļļāļˆāļąāļāļĢ āļāļĢāļļāļ‡āđ€āļ—āļžāļŊ 10900 āđ‚āļ—āļĢāļĻāļąāļžāļ—āđŒ 0-2579-0113, 0-2942-8500-11 āđ‚āļ—āļĢāļŠāļēāļĢ 0-2942-8998

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