Assoc. Prof. Dr. Songyot Nakariyakul
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Department of Electrical and Computer Engineering
Thammasat University Rangsit Campus
Klongluang, Pathumthani 12120 Thailand

Phone: 66(2) 564 3001-9 Ext 3148
E-mail: nsongyot AT engr DOT tu DOT ac DOT th

Home | Research | Publications | Software

Books and Book Chapters

  1. S. Nakariyakul, Digital Circuit Analysis and Design, Bangkok, Thailand: Chulalongkorn University Press, 2017. (355 pages in Thai) Link
  2. S. Nakariyakul, Hyperspectral waveband selection for detection of almonds with internal damage, in Food Supplies and Food Safety: Production, Conservation and Population Impact, M. B. Walsch, Ed. New York, NY: Nova Science Publishers, 2010, ch. 15, pp. 291-306. reprinted in New Topics in Food Engineering, M. A. Comeau, Ed. New York, NY: Nova Science Publishers, 2011, ch. 4, pp. 81-98. Link
  3. S. Nakariyakul, Feature Selection for Anomaly Detection in Hyperspectral Data: Algorithms, Methods, and Applications, Saarbrucken, Germany: VDM Verlag Dr. Muller, 2009. (184 pages) Link

Journals

  1. S. Nakariyakul, A comparative study of suboptimal branch and bound algorithms, Information Sciences, vol. 278, pp. 545-554, 2014. (IF 4.038) Link
  2. S. Nakariyakul, "Suboptimal branch and bound algorithms for feature subset selection: a comparative study," Pattern Recognition Letters 45, 62-70, 2014. (IF 1.266) Link
  3. S. Nakariyakul, "Internal damage inspection of almond nuts using optimal near-infrared waveband selection technique," J. of Food Engineering 126, 173-177, 2014. (IF 2.276) Link
  4. S. Nakariyakul, Z.-P. Liu, L. Chen, "A sequence-based computational approach to predicting PDZ domain-peptide interactions," Biochim. Biophys. Acta (BBA) - Proteins and Proteomics 1844, 165-170, 2014. (IF 3.733) Link
  5. S. Nakariyakul, “Fast spatial averaging: an efficient algorithm for 2D mean filtering,” J. of Supercomputing 65, 262-273, 2013. (IF 0.917) Link
  6. S. Nakariyakul, Z.-P. Liu, L. Chen, “Detecting thermophilic proteins through selecting amino acid and dipeptide composition features,” Amino Acids 42, 1947-1953, 2012. (IF 3.914) Link
  7. S. Nakariyakul and D. Casasent, "Classification of internally damaged almond nuts using hyperspectral imagery," J. of Food Engineering 103, 62-67, 2011. (IF 2.168) Link
  8. S. Nakariyakul and D. Casasent, "Fast feature selection algorithm for poultry skin tumor detection in hyperspectral data," J. of Food Engineering 94, 358-365, 2009. (IF 2.313) Link
  9. S. Nakariyakul and D. Casasent, “An improvement on floating search algorithms for feature subset selection,” Pattern Recognition 42, 1932-1940, 2009. (IF 2.554) Link
  10. S. Nakariyakul and D. Casasent, “Hyperspectral waveband selection for contaminant detection on poultry carcasses,” Optical Engineering 47, 087202, 2008. (IF 0.722) Link
  11. S. Nakariyakul and D. Casasent, “Adaptive branch and bound algorithm for selecting optimal features,” Pattern Recognition Letters 28, 1415-1427, 2007. (IF 0.853) Link
  12. S. Nakariyakul and D. Casasent, “Fusion algorithm for poultry skin tumor detection using hyperspectral data,” Applied Optics 46, 357-364, 2007. (IF 1.701) Link
  13. D. Casasent, X.-W. Chen, and S. Nakariyakul, “Hyperspectral methods to detect aflatoxin in whole kernel corn,” Mycopathologia 157, 422, 2004.

Conferences

  1. S. Nakariyakul, Gene selection using interaction information for microarray-based cancer classification, 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Chiang Mai, Thailand, pp. 1-5, 2016. Link
  2. S. Nakariyakul, Improved sequential search algorithms for classification in hyperspectral remote sensing images, Proc. SPIE, vol. 9273, 927328, 2014. Link
  3. S. Nakariyakul, Z.-P. Liu, and L. Chen, "Protein interaction prediction for mouse PDZ domains using dipeptide composition features," The Fifth IEEE international conference on Systems Biology (ISB 2011), Zhuhai, China, pp. 129-132, 2011. Link
  4. S. Nakariyakul, “Fast implementation of spatial averaging,” The Fourth International Conference on Embedded and Multimedia Computing (EM-Com 2009), Jeju, South Korea, pp. 136-141, 2009. Link
  5. S. Nakariyakul, “A new feature selection algorithm for multispectral and polarimetric vehicle images,” 2009 IEEE International Conference on Image Processing (ICIP 2009), Cairo, Egypt, pp. 2865-2868, 2009. Link
  6. S. Nakariyakul, “Study on criterion function models in the adaptive branch and bound algorithm,” The Eighth International Symposium on Natural Language Processing (SNLP 2009), Bangkok, Thailand, pp. 200-204, 2009. Link
  7. S. Nakariyakul, “Feature subset selection using generalized steepest ascent search algorithm,” The Eighth International Symposium on Natural Language Processing (SNLP 2009), Bangkok, Thailand, pp. 147-151, 2009. Link
  8. S. Nakariyakul, “A review of suboptimal branch and bound algorithms,” Proc. of the 2009 International Conference on Knowledge Discovery (ICKD 2009), Manila, Philippines, pp. 566-570, 2009. Link
  9. S. Nakariyakul and D. Casasent, “Improved forward floating selection algorithm for feature subset selection,” Proc. of the 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2008), Hong Kong, pp. 793-798, 2008. Link
  10. S. Nakariyakul, “On the suboptimal solutions using the adaptive branch and bound algorithm for feature selection,” Proc. of the 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2008), Hong Kong, pp. 384-389, 2008. Link
  11. S. Nakariyakul and D. Casasent, “Contaminant detection on poultry carcasses using hyperspectral data: Part II. Algorithms for selection of sets of ratio features,” Proc. SPIE, vol. 6761, paper 67610S, 2007. Link
  12. S. Nakariyakul and D. Casasent, “Contaminant detection on poultry carcasses using hyperspectral data: Part I. Algorithms for selection of individual wavebands,” Proc. SPIE, vol. 6761, paper 67610R, 2007. Link
  13. S. Nakariyakul and D. Casasent, “Improved forward floating selection algorithm for chicken contaminant detection in hyperspectral imagery,” Proc. SPIE, vol. 6565, paper 65651X, 2007. Link
  14. S. Nakariyakul and D. Casasent, “New adaptive branch and bound algorithm for hyperspectral waveband selection for chicken skin tumor detection,” Proc. SPIE, vol. 6381, paper 63810S, 2006. Link
  15. S. Nakariyakul and D. Casasent, “Adaptive branch and bound algorithm (ABB) for use on hyperspectral data,” Proc. SPIE, vol. 6233, pp. 867-876, 2006. Link
  16. D. Casasent and S. Nakariyakul, “Improved MINACE infrared detection filters,” Proc. SPIE, vol. 5816, pp. 126-135, 2005. Link
  17. D. Casasent, S. Nakariyakul, and P. Topiwala, “Zero-mean Minace filters for detection in visible EO imagery,” Proc. SPIE, vol. 5608, pp. 252-263, 2004. Link
  18. S. Nakariyakul and D. Casasent, “Hyperspectral ratio feature selection: agricultural product inspection example,” Proc. SPIE, vol. 5587, pp. 133-143, 2004. Link
  19. S. Nakariyakul and D. Casasent, “Hyperspectral feature selection and fusion for detection of chicken skin tumors,” Proc. SPIE, vol. 5271, pp.128-139, 2004. Link
  20. D. Casasent, S. Nakariyakul, and R. Shenoy, “Detection filters for visible high-resolution imagery,” Proc. SPIE, vol. 5106, pp. 128-137, 2003. Link
  21. D. Casasent, X.-W. Chen, and S. Nakariyakul, “Hyperspectral methods to detect aflatoxin in whole kernel corn,” Proc. of the 2nd Fungal Genomics, 3rd Fumonism Elimination and 15th Aflatoxin Elimination Workshops, pp. 56, 2002.

last update: Dec 1, 2016