neural networks & sparse data set
journal articles
Twomey, J.M. and Smith, A. E., (1998), “Bias and variance of validation methods for function approximation neural networks under conditions of sparse data.” IEEE Transactions on Systems, Man, and Cybernetics, 28, pp. 417-430.
Eksioglu, M. Fernandez, J.E., and Twomey, J.M., (1996), “An artificial neural network prediction model for determining peak pinch strength,”International Journal of Industrial Ergonomics,18, pp.431-441.
Twomey, J.M., Smith, A.E., and Redfern, M.S., (1995), “A predictive model for slip resistance using artificial neural networks.” IIE Transactions, 27, pp. 374-381.
Twomey, J.M. and Smith, A.E., (1995), “Performance measures, consistency, and power for artificial neural network models.” Journal of Mathematical and Computer Modeling, 21, pp. 243-258.
journal articles in preparation
Twomey, J., Cheraghi, H., and Ali , A (submitted January 2005) “Neural networks to identify important drill bit factors”, International Journal of Production Research.
Maradana, S., Twomey, J., Ahmad, J. (submitted January 2005) "Toward an automatic method of network construction and validation using the .632e error estimator", Simulation.
Chetchotsak, D. and Twomey, J. (submitted January 2005) “Improving committee networks’ performance under sparse data conditions: The biased regression and bootstrap error estimation approaches”, IEEE Man Systems and Cybernetics.
Ahmad, J. and Twomey, J. (submitted January 2005) “Neural network constitutive modeling”, Machining Science and Technology.
Twomey, J.M., and Cheraghi, S.H. (in preparation) ”Data selection and analysis to identify the quality of drill bits using neural networks", International Journal of Production Research: Special Issue on Data Mining.
conference proceedings
Chetchotsak, D. and Twomey, J. (2004) “Performance Sensitivity Analysis of the r-k Class Estimator Committee (RKC)” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 14, ASME Press.
Sen, S.,Twomey, J. and Ahmad, J. (2002) “Development of an Artificial Neural Network Constitutive Model for Aluminum 7075 Alloy,” 2002 IERC Conference, May 19, 2002.
Sen, S. and Twomey, S. (2002) “Parameter estimation using connectionist constitutive model for aluminum 7075 Alloy,” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 12, ASME Press.
Chetchotsak, D. and Twomey, J. (2002) “Improving generalization when data is scarce,” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 12, ASME Press.
Maradana, S. and Twomey, J. (2001) "0.632e stop training method for neural networks under the conditions of sparse data"Industrial Engineering Research Conference, 2001.
Siriphala, P. and Twomey. J. (2000) Controlling artificial neural networks overtraining when data is scarce.” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 10, ASME Press, pp. 100-105.
Ali, A. and Twomey, J. M., (1997), “Two industrial ergonomic applications of neural networks”, Intelligent Engineering Systems Through Artificial Neural Networks, Volume 7, ASME Press, pp. 1019-1024.
Kattel, B.P., Twomey, J.M., and Fernandez, J.E., (1996), “Application of neural networks to ergonomics: The prediction of maximum grip strength.” The 1st International Conference on Industrial Engineering Applications and Practice, pp. 900-905.
Sivasubramanian, K. and Twomey, J.M,. (1996), “A neural network approach to model over potential in eletrochemical machining applications.” The 1st International Conference on Industrial Engineering Applications and Practice, pp, 906-911.
Gottipati, V.H. and Twomey, J.M., (1996), “Neural network approximation model of as/rs simulation.” The 1st International Conference on Industrial Engineering Applications and Practice, pp. 912-917.
Twomey, J. M. and Smith, A. E., (1996), “Artificial Neural Network Approach to the Control of a Wave Soldering Process,” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 6, ASME Press, pp. 889 – 894.
Al-Rashid, Y. And Twomey, J.M., (1996), “Neural Networks Application to Short Term Power Load Forecasting,” Intelligent Engineering Systems Through Artificial Neural Networks, Volume6, ASME Press, pp. 787-792.
Twomey, J. M. and Smith, A. E., (1995), “Committee Networks by resampling,” Intelligent Neural Engineering Systems Through Artificial Networks, Volume 5, ASME Press, pp. 153-158.
Kilmer, R.A. and Twomey, J. M., (1995), “Applying artificial neural networks to combat simulation,” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 5, ASME Press, pp.1013-1018.
Eksioglu, M. Fernandez, J.E., and Twomey, J.M., (1995), “An artificial neural network (ANN) prediction model for determining peak pinch strength,” Advances in Industrial Ergonomics and Safety VII, Ed. A. Bittner, Taylor and Francis, 1995, pp. 101-106.
Twomey, J. M. and Smith, A. E., (1994), “Nonparametric error estimation methods for the evaluation and validation artificial neural networks,” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 4, ASME Press, pp. 100-105.
Huston, T., Smith, A., & Twomey, J.M., (1994), “Neural networks as an aid to medical decision making: comparing a statistical resampling technique with the train-and-test method for validation of sparse data sets,” Artificial Intelligence in Medicine: Interpreting Clinical Data, AAAI Press Technical Report SS-94-01, pp. 70-73.
Twomey, J. M. and Smith, A. E., (1993), “Power curves for pattern classification networks,” Proceedings of the 1993 IEEE International Conference on Neural Networks, San Francisco, CA, March, pp. 950-955.
Twomey, J. M., Smith, A. E., and Redfern, M.S., (1993), “A neural network model of the dynamic coefficient of friction,” Proceedings of the Second IIE Research Conference, Los Angeles, CA, pp. 187-191.
Twomey, J. M. and Smith, A. E., (1992), “An examination of performance measures for pattern classification backpropagation neural networks,” Intelligent Engineering Systems Through Artificial Neural Networks: Volume 2, ASME Press, pp. 343-348.
refereed conference proceedings
Chetchotsak, D. and Twomey, J. (2004) “Performance Sensitivity Analysis of the r-k Class Estimator Committee (RKC)” Intelligent Engineering Systems Through Artificial Neural Networks, Volume 14, ASME Press.
Liu , Y., Twomey, J.M., Cheraghi, S.H., (2000), "Artificial Neural Network Classification of Drill Bit Quality," Intelligent Engineering Systems Through Artificial Neural Networks, Volume 9, ASME Press.
book chapter
Methods for estimating the true performance of supervised artificial neural networks. (1997) Artificial Neural Networks for Civil Engineers: Fundamentals and Applications. N. Kartam, I. Flood and J. Garrett (Eds.).
technical reports
Twomey, J.M., “Neural Network Strategy for Manufacturing Processes when Data is Sparse,” Proceedings of 2000 NSF Design and Manufacturing Grantees Conference, CD-ROM. 2000
Year-end report of "Neural Network Strategy for Machining when Data is Sparse," NSF-CAREER Award Aug. 1999.
Final report of "Optimal Training and Validation Strategy for Neural Networks When Problems are Ill-Posed," First Award NSF-EPSCoR, Dec. 1996.
invited presentations
“Optimal Training and Validation Strategy for Neural Networks when data is Sparse”, NSF-EPSCoR Meeting, Topeka, 1999.