Classification of building electrical system faults based on Probabilistic Neural Networks

2016 ◽  
Author(s):  
Fucheng Zhang ◽  
Yahui Wang ◽  
Fangwen Chen
Keyword(s):  
Neural Networks ◽  
Electrical System ◽  
Probabilistic Neural Networks

Off-line classification of Polish vowel spectra using artificial neural networks

2004 ◽  
Vol 34 (1) ◽  
pp. 37-52
Author(s):  
Wiktor Jassem ◽  
Waldemar Grygiel
Keyword(s):  
Neural Networks ◽  
Numerical Data ◽  
Word List ◽  
Original Data ◽  
Data Matrix ◽  
Probabilistic Neural Networks ◽  
Independent Variable ◽  
The Individual ◽  
Vowel Phoneme

The mid-frequencies and bandwidths of formants 1–5 were measured at targets, at plus 0.01 s and at minus 0.01 s off the targets of vowels in a 100-word list read by five male and five female speakers, for a total of 3390 10-variable spectrum specifications. Each of the six Polish vowel phonemes was represented approximately the same number of times. The 3390* 10 original-data matrix was processed by probabilistic neural networks to produce a classification of the spectra with respect to (a) vowel phoneme, (b) identity of the speaker, and (c) speaker gender. For (a) and (b), networks with added input information from another independent variable were also used, as well as matrices of the numerical data appropriately normalized. Mean scores for classification with respect to phonemes in a multi-speaker design in the testing sets were around 95%, and mean speaker-dependent scores for the phonemes varied between 86% and 100%, with two speakers scoring 100% correct. The individual voices were identified between 95% and 96% of the time, and classifications of the spectra for speaker gender were practically 100% correct.

scholarly journals Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

2021 ◽  
Vol 11 ◽  
Author(s):  
Di Lu ◽  
Hongfeng Yu ◽  
Zhizhi Wang ◽  
Zhiming Chen ◽  
Jiayang Fan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Dielectric Properties ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Classification Accuracy ◽  
The Other ◽  
Probabilistic Neural Networks ◽  
Lung Cancer Patients

ObjectiveDielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.MethodsThe dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB.ResultsThe conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively.ConclusionsCompared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.

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