Data mining Research

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Groups and Facilities

(1 mention)

Manufacturing Excellence 
Assisting industries, with a focus on statistical process control, lean manufacturing, and real-time process modeling.


Recent Publications

Improving manufacturing data quality with data fusion and advanced algorithms for improved total data quality management.  Juriga D.  2019.  M.S. Thesis. [Abstract]

Improving manufacturing data quality with data fusion and advanced algorithms for improved total data quality management.  Juriga D., and T. M. Young.  2019.  30th International Conference on Wood Science and Technology - ICWST, Zagreb, Croatia. Dec. 12, 2019. [Abstract]

Data Science Revolution for Sustainable Biomaterials Academic Programs Series 1 – A Focus on Data Quality.  Young, T. M.  2019.  12th Wood Science and Engineering in the 3rd Millennium (ICWSE), p. 21-31. [Abstract]

Modeling using Bayesian Additive Regression Trees (BART) for Wood Composite Products Tian N., T. M. Young, A. Petutschnigg, Y. Sun, and Z. Pei.  2018.  Research Inventy: International Journal of Engineering and Science (IJSE), 7(5):66-75. [Abstract]

The missing puzzle piece - is engineered wood ready for big data?.  Young, T. M.  2018.  Engineered Wood Journal, 21(2):64. [Abstract]

Dynamic simulation of the continuous flow of bulk material during production to improve the statistical modeling of final product strength properties.  Reigler M., N. O. Andre, M. Gronalt, and T. M. Young.  2015.  International Journal of Production Research, 53(21):6629–6636. [Abstract]

How bioinformatics is helping to restore the American Chestnut: next generation sequencing, data mining and online resources.  Staton, M. E., N. Cannon, J. Davitt, N. Henry, M. Cook, and J. Carlson.  2015.  American Society of Plant Biologists Southern Section.

Driving an empirical data mining-based model to predict soil water retention curve.  Haghverdi, A., and B. G. Leib.  2014.  3rd Annual Watershed Symposium, February 18th, 2014, University of Tennessee, TN, USA.

Deriving data mining and regression based water-salinity production functions for spring wheat (Triticum aestivum).  Haghverdi, A., B. Ghahraman, B. G. Leib, I. Pulido-Calvo, M. Kafi, K. Davary, and B. Ashorun.  2014.  Computers and Electronics in Agriculture, 101:68-75.

Processing technologies use of data in the new millennium and the evolution of process knowledge.  Young, T. M.  2014.  PTF BPI 2014. 3rd Intern. Conf. Processing Tech. for the Forest and Bio-based Products Industries, Salzburg/Kuchl, Austria, 24-26 September 2014. p. 21-29. [Abstract]

The evolution of knowledge in the forest products manufacturing Young, T. M., A. Petutschnigg, and M. Barbu.  2013.  9th edition of the International Conference “Wood Science and Engineering in the Third Millennium”, p. 22-27. [Abstract]

Variation discovery, data mining and predictive process in modeling of engineered wood processes Young, T. M.  2010.  Engineered Wood Journal. 13(2):25-26.