รศ.ดร.พยุง มีสัจ

Phuyung Meesaj, PhD.

Department of Information Technology Management

Associate Professor, Director of the Library Office

    Ph.D. Philosophy (Electrical and Computer Engineering), Oklahoma State Water University, USA.
    Master Degree, Master of Science (Electrical and Computer Engineering), Oklahoma State Water University, USA.
    Bachelor of Science in Technical Program (Electrical Engineering), King Mongkut's University of Technology North Bangkok.
  • Data Mining
  • Advanced Data Mining
  • Fuzzy System and Neural Network
  • Advanced Research Methodology
    2012-2016: Dean, Faculty of Information Technology, KMUTNB.
    2008-2012: Associate Dean for Academic and Research Affairs, Faculty of Information Technology, KMUTNB.
    2004-2008: Associate Dean for Administration, Faculty of Information Technology, KMUTNB.
  1. Long, N.C., Meesad, P., Unger, H. (2015). “A highly accurate firefly based algorithm for heart disease prediction,” Expert Systems with Applications, 42 (21), pp. 8221-8231.
  2. Mahmud, M.S., Meesad, P. (2015). “An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction,” Soft Computing, 19 p. Article in Press.
  3. Long, N.C., Wisitpongphan, N., Meesad, P., Unger, H. (2014). “Clustering stock data for multi-objective portfolio optimization,” International Journal of Computational Intelligence and Applications, 13 (2), art. no. 1450011.
  4. Buathong, W., Meesad, P. (2014). “Double linear support vector machine for dimensionality reduction,” Research Journal of Applied Sciences, 9 (4), pp. 208-213.
  5. Hoang T. P. Thanh and Phayung Meesad, (2014). “Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data,” Journal of Advanced Computational Intelligence and Intelligent Informatics, 18 (1), pp. 22-31.
  6. Thammasiri, D., Delen, D., Meesad, P., Kasap, N. (2014). “A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition Expert Systems with Applications, 41 (2), pp. 321-330.
  7. Thammasiri, D., Meesad, P. (2012). “Ensemble data classification based on diversity of classifiers optimized by genetic algorithm,” Advanced Materials Research, 433-440, pp. 6572-6578.
  8. Thammasiri, D., Meesad, P. (2012). “Adaboost ensemble data classification based on diversity of classifiers,” Advanced Materials Research, 403-408, pp. 3682-3687.
  9. Ammaruekarat, P., Meesad, P. (2012). “A multi-objective Memetic Algorithm based on chaos optimization,” Applied Mechanics and Materials, 130-134, pp. 725-729.
  10. Saengsiri, P., Wichian, S.N., Meesad, P. (2012). “Efficient feature selection model for gene expression data,” Applied Mechanics and Materials, 110-116, pp. 1948-1952.
  11. Kularbphettong, K., Meesad, P., Clayton, G. (2012). “A hybrid system based on Multi-agent systems in case of e-Wedding Thailand,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7103 LNAI, pp. 344-359.