Neural network approach

In this section, a fast neural network approach [46] for the predication of NMR chemical shifts is explained and exemplified. 

Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

Burns, R.S. and Richter, R. (1995) A Neural Network Approach to the Control of Surface Ships, Journal of Control Engineering Practice, International Federation of Automatic Control, Elsevier Science, 4(3), pp. 411M16. 

An interesting approach is also the combination of a commercial log D predictor with proprietary descriptors using a Bayesian neural network approach [98]. 

J.M. Andrews and S.H. Lieberman, Neural network approach to qualitative identifications of fuels from laser induced fluorescence spectra. Anal. Chim. Acta, 285 (1994) 237-246. 

Zhang et al.14 develop a neural network approach to bacterial classification using MALDI MS. The developed neural network is used to classify bacteria and to classify culturing time for each bacterium. To avoid the problem of overfitting a neural network to the large number of channels present in a raw MALDI spectrum, the authors first normalize and then reduce the dimensionality of the spectra by performing a wavelet transformation. 

Robb EW, Munk ME (1990) A neural network approach to infrared spectrum interpretation. Mikrochim Acta [Wien] 1990/1 131... 

A classical Hansch approach and an artificial neural networks approach were applied to a training set of 32 substituted phenylpiperazines characterized by their affinity for the 5-HTiA-R and the generic arAR [91]. The study was aimed at evaluating the structural requirements for the 5-HTiA/ai selectivity. Each chemical structure was described by six physicochemical parameters and three indicator variables. As electronic descriptors, the field and resonance constants of Swain and Lupton were used. Furthermore, the vdW volumes were employed as steric parameters. The hydrophobic effects exerted by the ortho- and meta-substituents were measured by using the Hansch 7t-ortho and n-meta constants [91]. The resulting models provided a significant correlation of electronic, steric and hydro-phobic parameters with the biological affinities. Moreover, it was inferred that the... 

Kiss IZ, Mandi G, Beck MT (2000) Artificial neural network approach to predict the solubility of C60 in various solvents. J. Phys. Chem. Sect A 104 8081-8088. 

A NEURAL NETWORK APPROACH FOR THE CLASSIFICATION OF BODY MOVEMENTS... 

B. Basu, G.S. Kapur, A.S. Saipal, and R. Meusinger, A neural network approach to the prediction of cetane number of diesel fuels using nuclear magnetic resonance (NMR) spectroscopy, Energy Fuels, 17, 1570-1575 (2003). 

QSAR and neural network approaches in combination with physiologicaUy-based pharmacokinetic (PBPK) modelling hold promise in becoming a powerful tool in drug discovery [45]. Below we briefly discuss some of these studies. 

C. Schierle, M. Otto and W. Wegscheider, A neural network approach to qualitative analysis in inductively coupled plasma-atomic emission spectroscopy (ICP-AES), Fresenius J. Anal. Chem., 343(7), 1992, 561-565. 

E. A. Hernandez-Caraballo and L. M. Marco-Parra, Direct analysis of blood serum by total reflection X-ray fluorescence spectrometry and application of an artificial neural network approach for cancer diagnosis, Spectrochim. Acta, Part B, 58(12), 2003, 2205-2213. 

S. Brunak and R.M.J. Cotterill (Technical University of Denmark) pursued the approach further, based upon data inputs from NMR and x-ray diffraction. The neural network approach remains very active so that encoding of the intricacy of interconnections may be achievable. 

Ergungor describes the application of on-line Raman spectroscopy and neural networks to the simultaneous prediction of temperature and crystallinity of nylon-6 nanocomposites as a function of cooling rate. The authors prefer their neural network approach because they make use of information in the entire spectrum rather than from a few bands as most studies have done.84 Van Wijk etal. of Akzo Nobel obtained a patent on the use of a Raman spectrum of a polymeric fiber to determine dye uptake and other structural or mechanical properties based on previously developed models.85... 

The neural network approach has been successfully used by Gakh et al.74 to model the stability constants of Na+, K+, and Cs+complexes with some unsubstituted... 

SOME ASPECTS OF NEURAL NETWORK APPROACH FOR INTRUSION DETECTION... 

The rest of the paper is organized as follows. The Section 2 describes attack classification and training data set. In the Section 3 the intrusion detection system is described, based on neural network approach. Section 4 presents the nonlinear PCA neural network and multilayer perceptron for identification and classification of computer network attack. In Section 5 the results of experiments are presented. Conclusion is given in Section 6. 

Cannady, J., An adaptive neural network approach to intrusion detection and response, Ph.D. Thesis, School of Comp, and Inf. Sci., Nova Southeastern University,... 

Uberbacher, E. C. Mural, R. J. (1991). Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach. Proc Natl Acad Sci USA 88, 11261-5. 

Dubchak, I., Holbrook, S. R. Kim, S.-H. (1993b). Comparison of two variations of neural network approach to the prediction of protein folding pattern. Ismb 1,118-26. 

Muskal, S. M. Kim, S.-H. (1992). Predicting protein secondary structure content. A tandem neural network approach. J Mol Biol 225,713-27. 

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