A Comparative study of Artificial Neural Networks Using Reinforcement learning and Multidimensional Bayesian Classification Using Parzen Density Estimation for Identification o

by   Faramarz Valafar, et al.

This study reports the development of a pattern recognition search engine for a World Wide Web-based database of gas chromatography-electron impact mass spectra (GC-EIMS) of partially methylated Alditol Acetates (PMAAs). Here, we also report comparative results for two pattern recognition techniques that were employed for this study. The first technique is a statistical technique using Bayesian classifiers and Parzen density estimators. The second technique involves an artificial neural network module trained with reinforcement learning. We demonstrate here that both systems perform well in identifying spectra with small amounts of noise. Both system's performance degrades with degrading signal-to-noise ratio (SNR). When dealing with partial spectra (missing data), the artificial neural network system performs better. The developed system is implemented on the world wide web, and is intended to identify PMAAs using submitted spectra of these molecules recorded on any GC-EIMS instrument. The system, therefore, is insensitive to instrument and column dependent variations in GC-EIMS spectra.


page 1

page 2

page 3

page 4


O-type Stars Stellar Parameter Estimation Using Recurrent Neural Networks

In this paper, we present a deep learning system approach to estimating ...

Reduction in the complexity of 1D 1H-NMR spectra by the use of Frequency to Information Transformation

Analysis of 1H-NMR spectra is often hindered by large variations that oc...

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

The ionization edges encoded in the electron energy loss spectroscopy (E...

Web spam classification using supervised artificial neural network algorithms

Due to the rapid growth in technology employed by the spammers, there is...

92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster

Artificial neural networks with millions of adjustable parameters and a ...

Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20≤SNR<30

The accuracy of the estimated stellar atmospheric parameter decreases ev...

Please sign up or login with your details

Forgot password? Click here to reset