Prediction of GC Retention Indexes for Insect-Produced Methyl-Substituted Alkanes Using an Artificial Neural Network and Simple Structural Descriptors

By Zarei K.   Atabati M.  

Journal of Analytical Chemistry, Vol. 60, Iss. 8, 2005-08 ,pp. : 732-737

MAIK Nauka/Interperiodica

Access to resources Recommend Favorite

Prediction of High Cycle Fatigue Property of Ti-6Al-4V Alloy Using Artificial Neural Network

By  

Materials Science Forum, Vol. 2016, Iss. 849, 2016-04 ,pp. : 360-367

Trans Tech Publications

Access to resources Recommend Favorite

Prediction of the Affinity of the Newly Synthesised Azapirone Derivatives for 5-HT1A Receptors Based on Artificial Neural Network Analysis of Chromatographic Retention Data and Calculation Chemistry Parameters

By  

Combinatorial Chemistry & High Throughput Screening, Vol. 7, Iss. 4, 2004-01 ,pp. : 313-325

Bentham Science Publishers

Access to resources Recommend Favorite

An evaluation of some factors affecting the accuracy of classification by an artificial neural network

By Foody G. M.   Arora M. K.  

International Journal of Remote Sensing, Vol. 18, Iss. 4, 1997-03 ,pp. : 799-810

Taylor & Francis Ltd

Access to resources Recommend Favorite

Prediction on tribological properties of short fibre composites using artificial neural networks

By Zhang Z.   Friedrich K.   Velten K.  

Wear, Vol. 252, Iss. 7, 2002-04 ,pp. : 668-675

Elsevier

Access to resources Recommend Favorite