Pattern recognition analysis of differential mobility spectra with classification by chemical family

Eiceman, Gary A.; Wang, M.; Prasad, Satendra; Schmidt, H.; Tadjimukhamedov, F.K.; Lavine, Barry K.; Mirjankar, Nikhil

doi:10.1016/j.aca.2006.07.013

Cited by 41 publications

(33 citation statements)

References 31 publications

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“…Over time, the pattern recognition GA learns its optimal parameters in a manner similar to a neural network. Further details about the fitness function of the pattern recognition GA used in this study can be found elsewhere [16][17][18][19][20][21].…”

Section: Pattern Recognition Methodologymentioning

confidence: 99%

Forensic analysis of automotive paints using a pattern recognition assisted infrared library searching system: Ford (2000–2006)

Lavine

White

Allen

2016

Microchemical Journal

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Section: Pattern Recognition Methodologymentioning

confidence: 99%

Forensic analysis of automotive paints using a pattern recognition assisted infrared library searching system: Ford (2000–2006)

Lavine

White

Allen

2016

Microchemical Journal

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“…Eiceman et al [17] used a similar MLP approach to classify differential ion mobility spectra of a number of chemicals classes such as alcohols, ketones, substituted ketones and aromatics. Between 20 and 41 spectra from each class was used for training the MLP.…”

Section: Chemical Classification By Imsmentioning

confidence: 99%

Synergistic Reconfiguration of Adaptive Precision Chemical Classifiers

Gilberti

Doboli

2009

2009 NASA/ESA Conference on Adaptive Hardware and Systems

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We present parallel implementations of a multilayer perceptron that uses reduced variable bit width hardware to improve resource utilization while still providing known levels of accuracy. We show results for a chemical classification application and introduce ways in which to take advantage of the capabilities of a reconfigurable device. We show how the optimized circuit can be used synergistically in parallel with other classifiers for added capability and alone for fault tolerance and saving power.

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“…A block diagram of the pattern recognition GA [10][11][12][13][14][15] is shown in Figure 2. Selected feature subsets represented as chromosomes are sent to a fitness function for evaluation.…”

Section: Genetic Algorithms For Pattern Recognition Analysismentioning

confidence: 99%

One stop shopping: feature selection, classification and prediction in a single step

Lavine

Nuguru

Mirjankar

2011

Journal of Chemometrics

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We report on the application of a genetic algorithm (GA) for pattern recognition that uses both supervised and transverse learning to mine spectroscopic and proteomic data. The pattern recognition GA selects features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. For training sets with small amounts of labeled data (i.e. data points tagged with a class label) and large amounts of unlabeled data (i.e. data points that are not tagged with a class label), this approach is preferred, as our results show, information in the unlabeled data is used by the fitness function to guide feature selection. The advantages of incorporating transverse learning into the fitness function of the pattern recognition GA have been evaluated in two recently published studies by our group. In one study, Raman spectroscopy and the pattern recognition GA were used to develop a potential method to discriminate hardwoods, softwoods and tropical woods. In a second study, biopsy material of small round blue cell tumors analyzed by cDNA microarrays was identified as to type (Ewings sarcoma, Burkitt's lymphoma, neuroblastoma and rhabdomyosarcoma) through supervised learning implemented by the pattern recognition GA.

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Pattern recognition analysis of differential mobility spectra with classification by chemical family

Cited by 41 publications

References 31 publications

Forensic analysis of automotive paints using a pattern recognition assisted infrared library searching system: Ford (2000–2006)

Forensic analysis of automotive paints using a pattern recognition assisted infrared library searching system: Ford (2000–2006)

Synergistic Reconfiguration of Adaptive Precision Chemical Classifiers

One stop shopping: feature selection, classification and prediction in a single step

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