Generation of mass spectra using pattern recognition techniques

1975 ◽  
Vol 47 (9) ◽  
pp. 1562-1573 ◽  
Author(s):  
G. S. Zander ◽  
P. C. Jurs
Keyword(s):  
Pattern Recognition ◽  
Mass Spectra ◽  
Pattern Recognition Techniques

Pattern recognition and interpretation of nucleoside mass spectra

1980 ◽  
Vol 33 (7) ◽  
pp. 1401 ◽  
Author(s):  
RGAR Maclagan ◽  
MJ Mitchell
Keyword(s):  
Pattern Recognition ◽  
Mass Spectra ◽  
Figure Of Merit ◽  
Function Analysis ◽  
Structural Features ◽  
Nearest Neighbour ◽  
Linear Discriminant ◽  
Pattern Recognition Techniques ◽  
The Mean ◽  
Learning Machine

Four pattern-recognition techniques-distance from the mean, the learning machine approach, a statistical linear discriminant function analysis and the k-nearest-neighbour method-have been applied to a set of 125 nucleoside mass spectra. Twenty-one structural features, comprising elemental compositions and substitution types, were predicted from binary and logarithmic transforms of the spectra. The best classification was given by the distance from the mean method with logarithmic data. With this method prediction success averaged 79 % over the 21 categories, or in terms of the figure of merit 0.27.

Generation of Simulated Mass Spectra of Small Organic Molecules by Computerized Pattern Recognition Techniques

1973 ◽  
Vol 27 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Joseph Schechter ◽  
Peter C. Jurs
Keyword(s):  
Pattern Recognition ◽  
Mass Spectra ◽  
Organic Molecules ◽  
Molecular Descriptor ◽  
Empirical Method ◽  
Molecular Structures ◽  
Small Organic Molecules ◽  
Mass Spectral ◽  
Pattern Recognition Techniques ◽  
System A

An empirical method employing computerized pattern recognition techniques has been applied to the generation of simulated mass spectra of small organic molecules. Molecular structures are represented in computer-compatible form through the use of a fragmentation code which assigns code designations to specific groups of atoms and/or bonds within the molecules. Using such descriptions of molecules, pattern classifiers have been developed to predict the presence or absence of mass spectral peaks in each of 60 nominal m/e positions and to give a measure of the intensity of peaks in 11 of these. Information in the molecular descriptor lists which correlates with the appearance of specific peaks is shown to be present in relatively few of the descriptors developed. To test the complete system, a number of entire mass spectra were developed; in this test, 93% of the classifications were made correctly.

EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques A Review

2019 ◽  
Vol 7 (1) ◽  
pp. 615-618
Author(s):  
Y. M. Rajput ◽  
S. Abdul Hannan ◽  
M. Eid Alzahrani ◽  
Ramesh R. Manza ◽  
Dnyaneshwari D. Patil
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Emotion Recognition ◽  
Pattern Recognition Techniques

Gravity Models in International Trade: An Exploration in Econo-Physics

2021 ◽  
pp. 227797872198992
Author(s):  
Iman Pal ◽  
Saibal Kar
Keyword(s):  
Pattern Recognition ◽  
International Trade ◽  
Present Article ◽  
Gravity Models ◽  
Scientific Applications ◽  
Physical Sciences ◽  
Trade Patterns ◽  
Pattern Recognition Techniques ◽  
The Subject ◽  
Jel Classifications

Several strands of the static and dynamic theoretical constructs and the empirical applications in the subject of economics owe substantially to the well-known principles of physical sciences. The present article explores as to how the development of the popular gravity models in international trade can be traced back to Newton’s law of gravitation, and to both Ohm’s Law and Kirchhoff’s Law of current electricity, as well as to the pattern recognition techniques commonly deployed in scientific applications. In addition to surveying these theoretical analogies, the article also offers numerical applications for observed trade patterns between India and a set of countries. JEL Classifications: F41, F42, C61, F47

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