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2020 – today
- 2022
[i3]Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong:
Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis. CoRR abs/2205.06131 (2022)- 2021
- [j9]Teema Leangarun
, Poj Tangamchit, Suttipong Thajchayapong:
Stock Price Manipulation Detection Using Deep Unsupervised Learning: The Case of Thailand. IEEE Access 9: 106824-106838 (2021) - [j8]Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong:
Identifying Linear Models in Multi-Resolution Population Data Using Minimum Description Length Principle to Predict Household Income. ACM Trans. Knowl. Discov. Data 15(2): 15:1-15:30 (2021) - 2020
- [j7]Teerapaun Tanprasert, Chaiyaphum Siripanpornchana, Navaporn Surasvadi, Suttipong Thajchayapong:
Recognizing Traffic Black Spots From Street View Images Using Environment-Aware Image Processing and Neural Network. IEEE Access 8: 121469-121478 (2020) - [c12]Teema Leangarun, Poj Tangamchit, Suttipong Thajchayapong:
Using Generative Adversarial Networks for Detecting Stock Price Manipulation: The Stock Exchange of Thailand Case Study. SSCI 2020: 2162-2169
2010 – 2019
- 2019
- [c11]Ugyen Jigten Dorji, Chaiyaphum Siripanpornchana, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong:
Exploring Night Light as Proxy for Poverty and Income Inequality Approximation in Thailand. TENCON 2019: 1082-1087 - [i2]Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong:
Identifying Linear Models in Multi-Resolution Population Data using Minimum Description Length Principle to Predict Household Income. CoRR abs/1907.05234 (2019) - [i1]Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong:
A nonparametric framework for inferring orders of categorical data from category-real ordered pairs. CoRR abs/1911.06723 (2019) - 2018
- [c10]Teema Leangarun, Poj Tangamchit, Suttipong Thajchayapong:
Stock Price Manipulation Detection using Generative Adversarial Networks. SSCI 2018: 2104-2111 - 2017
- [j6]Chalermpol Saiprasert, Thunyasit Pholprasit, Suttipong Thajchayapong:
Detection of Driving Events using Sensory Data on Smartphone. Int. J. Intell. Transp. Syst. Res. 15(1): 17-28 (2017) - [c9]Suporn Pongnumkul, Chaiyaphum Siripanpornchana, Suttipong Thajchayapong:
Performance Analysis of Private Blockchain Platforms in Varying Workloads. ICCCN 2017: 1-6 - 2016
- [c8]Teema Leangarun, Poj Tangamchit, Suttipong Thajchayapong:
Stock price manipulation detection using a computational neural network model. ICACI 2016: 337-341 - 2015
- [j5]Chalermpol Saiprasert, Suttipong Thajchayapong:
Remote Driver Identification Using Minimal Sensory Data. IEEE Commun. Lett. 19(10): 1706-1709 (2015) - [j4]Suttipong Thajchayapong, Javier A. Barria:
Spatial Inference of Traffic Transition Using Micro-Macro Traffic Variables. IEEE Trans. Intell. Transp. Syst. 16(2): 854-864 (2015) - [c7]Suttipong Thajchayapong, Chalermpol Saiprasert, C. Charoensiriwath, Chularat Tanprasert:
Severity assessment of anomalies using driving behaviour signals. ICCVE 2015: 297-298 - 2014
- [c6]Chalermpol Saiprasert, Suttipong Thajchayapong, Thunyasit Pholprasit, Chularat Tanprasert:
Driver behaviour profiling using smartphone sensory data in a V2I environment. ICCVE 2014: 552-557 - 2013
- [j3]Suttipong Thajchayapong, Edgar S. García-Treviño, Javier A. Barria:
Distributed Classification of Traffic Anomalies Using Microscopic Traffic Variables. IEEE Trans. Intell. Transp. Syst. 14(1): 448-458 (2013) - 2012
- [c5]K. Hi-ri-o-tappa, C. Likitkhajorn, A. Poolsawat, Suttipong Thajchayapong:
Traffic incident detection system using series of point detectors. ITSC 2012: 182-187 - 2011
- [j2]Javier A. Barria, Suttipong Thajchayapong:
Detection and Classification of Traffic Anomalies Using Microscopic Traffic Variables. IEEE Trans. Intell. Transp. Syst. 12(3): 695-704 (2011) - 2010
- [b1]Suttipong Thajchayapong:
Monitoring of traffic anomalies using microscopic traffic variables in vehicular transportation networks. Imperial College London, UK, 2010 - [c4]Suttipong Thajchayapong, Javier A. Barria, Javier S. García-Treviño:
Lane-level traffic estimations using microscopic traffic variables. ITSC 2010: 1189-1194
2000 – 2009
- 2006
- [j1]Suttipong Thajchayapong, Jon M. Peha:
Mobility Patterns in Microcellular Wireless Networks. IEEE Trans. Mob. Comput. 5(1): 52-63 (2006) - [c3]Suttipong Thajchayapong, Wasan Pattara-Atikom, Noppadol Chadil, Chaichana Mitrpant:
Enhanced detection of road traffic congestion areas using cell dwell times. ITSC 2006: 1084-1089 - 2005
- [c2]Suttipong Thajchayapong, Ozan K. Tonguz:
Performance implications of Pareto-distributed cell residual time in distributed admission control scheme (DACS). WCNC 2005: 2387-2392 - 2003
- [c1]Suttipong Thajchayapong, Jon M. Peha:
Mobility patterns in microcellular wireless networks. WCNC 2003: 1963-1968
Coauthor Index
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