Preliminary classification of mass spectral patterns using a simplified learning machine

1973 ◽  
Vol 26 (9) ◽  
pp. 1955 ◽  
Author(s):  
RJ Mathews
Keyword(s):  
Mass Spectra ◽  
Functional Group ◽  
Training Process ◽  
Training Set ◽  
Mass Spectral ◽  
Learning Machine ◽  
Spectral Patterns ◽  
Feature Pattern ◽  
Preliminary Classification

A simplified learning machine technique is presented which is suitable for forming preliminary classification of patterns. This technique allows preliminary compression of data prior to the training process, and generates a reliable classifier even when there are linearly inseparable data in the training set. This method has been used to form an eight-feature pattern classifier which identifies, directly from their mass spectra, compounds of the structure (RO)2P(=X)Y where R is H, Me, or Et; X is O or S; and Y is any functional group.

scholarly journals The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

2004 ◽  
Vol 19 (4-5) ◽  
pp. 169-183 ◽  
Author(s):  
Que N. Van ◽  
John R. Klose ◽  
David A. Lucas ◽  
DaRue A. Prieto ◽  
Brian Luke ◽  
...  
Keyword(s):  
Interstitial Cystitis ◽  
Mass Spectra ◽  
Proton Nuclear Magnetic Resonance ◽  
Training Set ◽  
H Nmr ◽  
New Methods ◽  
System A ◽  
Spectral Patterns ◽  
Bioinformatic Approach ◽  
Disease Specific

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precisem/zor chemical shift values plotted inn-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Classification of multiwavelength transients with Machine Learning

2020 ◽  
Author(s):  
K Sooknunan ◽  
M Lochner ◽  
Bruce A Bassett ◽  
H V Peiris ◽  
R Fender ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Sample ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Test Time ◽  
Test Accuracy ◽  
Training Set ◽  
The Impact

Abstract With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the eleven classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78%. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97%, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19%.

scholarly journals Discrimination of structural isomers of amphetamine using carbon dioxide negative-ion chemical ionization mass spectrometry

1997 ◽  
Vol 13 (2) ◽  
pp. 151-161 ◽  
Author(s):  
Kevin B. Thurbide ◽  
C. M. Elson ◽  
P. G. Sim
Keyword(s):  
Carbon Dioxide ◽  
Mass Spectra ◽  
Chemical Ionization ◽  
Negative Ion ◽  
Ionization Mass ◽  
Structural Isomers ◽  
Mass Spectral ◽  
Negative Ion Chemical Ionization ◽  
Spectral Behaviour ◽  
Carbon Dioxide Plasma

The negative‒ion chemical ionization mass spectra of a group of structural isomers of amphetamine have been studied using carbon dioxide as the reagent gas. Characteristic and reproducible differences are observed for each member of the set implying that this technique offers a means of distinguishing among groups of amphetamine isomers. Characteristic adducts to the molecular ion are observed in the form (M–[H]+[O]) and (M–[H]+[CO2]). Descriptions of some fragments are given based on the mass spectral behaviour of a set of analogue compounds and the results of oxygen-18 labelled carbon dioxide reagent gas experiments. Contents of the carbon dioxide plasma and their impact on various analytes is also discussed.

Mass Spectra of β-Keto Esters

1972 ◽  
Vol 50 (16) ◽  
pp. 2707-2710 ◽  
Author(s):  
Larry Weiler
Keyword(s):  
Mass Spectra ◽  
Carbonyl Groups ◽  
Mass Spectral Fragmentation ◽  
Mass Spectral ◽  
Keto Esters ◽  
Fragmentation Patterns

The mass spectra of several γ-substituted β-keto esters have been recorded and interpreted. The fragmentation patterns are compared to those of α-substituted β-keto esters and are found to be very useful in differentiating the α- and γ-substituted isomers. The mass spectral fragmentation schemes are dominated by cleavages α to the carbonyl groups and by McLafferty rearrangements.

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine

Classification of cola beverages on the basis of mass spectra measured by membrane inlet mass spectrometry

1998 ◽  
Vol 12 (15) ◽  
pp. 1011-1018 ◽  
Author(s):  
Raimo A. Ketola ◽  
Jukka Heikkonen ◽  
Sulo Piepponen ◽  
Frants R. Lauritsen ◽  
Tapio Kotiaho
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectra ◽  
Membrane Inlet Mass Spectrometry
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