Robust Iris Classification through a Combination of Kernel Discriminant Analysis and Parzen Based Probabilistic Neural Networks

Sharma, Rajendra; Singh, Ashutosh; Joshi, Akanksha; Gangwar, Abhishek

doi:10.1109/eait.2014.25

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Вероятностные нейронные сети широко применяются в задачах классификации и распознавания, таких как различные задачи в медицине [6], включая диагностику таких заболеваний, как аритмия [7], опухоли головного мозга [8], рак кожи [9] и прочие [10][11][12], диагностика в медицине [13,14], распознавание голоса [15], определение личности по голосу [16], радужной оболочке глаза [17], изображению лица [18] и геометрии рук [19], распознавание, обработка и классификация изображений [20,21]. В области биологии вероятностные нейронные сети находят применение в таких задачах, как классификация белков по суперсемействам [5].…”

Section: вероятностные нейронные сетиunclassified

Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

Nazin¹,

Готовцев²

2019

Math.Biol.Bioinf.

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This paper describes the use of probabilistic neural networks to solve problems of bioinformatics by the example of determining the localization of proteins according to their primary structure. The data used are sets of characteristics of amino acid sequences of proteins, obtained by various software tools aimed at finding specific signal sequences, as well as data on where these proteins are localized in cells of two microorganisms — E. coli and S. cerevisiae. The data source is the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets). The possibility of using probabilistic neural networks to solve this problem is shown, since the classification accuracy of 57.5% and 85.0% for yeast and for bacterial cells is obtained, respectively. The obtained indicators of the accuracy of the classification of the data used exceed those that, according to the literature, were achieved with the use of other recognition methods. It is noted that a high learning rate and the possibility of modification make probabilistic neural networks a promising tool for analyzing bioinformatics data. In silico

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Section: вероятностные нейронные сетиunclassified

Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

Nazin¹,

Готовцев²

2019

Math.Biol.Bioinf.

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“…In addition, a combination method to establish the ROC has been used in [5], where a Parzen PNN (PPNN) and a Gabor filter were employed for periocular recognition. Similarly, a PPNN and kernel discriminant analysis were used together to produce the ROC for the iris classification purpose [6]. Furthermore, in [7] a combination of multiple neural networks was used to collect the ROC parameters, where a voting process was applied for the decision of three neural networks.…”

Section: A Related Workmentioning

confidence: 99%

“…It is just for an MLP with a bias and only two classes Using commercial software [3] Yes for PNN with only two classes. The curve was not smooth The results were averaged [4] Yes after running the PNN many times A combination method [5] Yes using a PPNN and a Gabor filter A combination method [6] Yes using a PPNN and kernel discriminant analysis A combination of multiple [7] Yes neural networks then applying a voting process [8], [9], [10], [11], [12], [13], [14], [15], No - [16], [17] and [18]…”

Section: A Related Workmentioning

confidence: 99%

A novel biometric approach to generate ROC curve from the Probabilistic Neural Network

Al-Nima

Dlay

Woo

et al. 2016

2016 24th Signal Processing and Communication Application Conference (SIU)

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The aim of this paper is to present a new method to produce a Receiver Operating Characteristic (ROC) curve from a Probabilistic Neural Network (PNN). Traditionally, an ROC curve has been used widely to report the recognition system measurements. Two main problems arise when using the PNN. Firstly, the PNN outputs are always logical (zeros and one); secondly, a PNN is considered as a multi-class classifier, because it usually has more than one output class. To solve these problems, we suggest a new approach to acquire the score values from the PNN, establish the relationship between the ROC parameters for each class and fusing them to generate one main ROC curve. Personal authentication based on the Finger Texture (FT) biometric has been used to collect the ROC parameters, where three feature extraction methods have been implemented and evaluated: Coefficient of Variance (CV) statistics, Gabor filter followed by the CV calculations and Local Binary Pattern (LBP) followed by the CVs. The results show the accuracy of the Equal Error Rates (EERs) recorded for each ROC graph compared with the actual practical values.

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Robust Iris Classification through a Combination of Kernel Discriminant Analysis and Parzen Based Probabilistic Neural Networks

Cited by 2 publications

References 22 publications

Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

A novel biometric approach to generate ROC curve from the Probabilistic Neural Network

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