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Classification algorithms to identify changes in resistance

100%

Duraj A. , Korzeniewska E. , Krawczyk A.

Przegląd Elektrotechniczny

2015

tom R. 91, nr 12

80-82

In the article the basic method for measuring the resistance of medical electrodes, made based on a thin conductive layer formed during the PVD process, is described. The authors also briefly characterized two algorithms for data classification: k-nearest neighbors and Bayes classifier, which were used as algorithms to detect changes in the electrode resistance.

W artykule została opisana podstawowa metoda pomiaru rezystancji elektrod medycznych wykonanych w oparciu o cienkie warstwy przewodzące powstałe w procesie PVD. Scharakteryzowano również krótko dwa algorytmy klasyfikacji danych: algorytm k najbliższych sąsiadów oraz klasyfikator bayowski, które zostały wykorzystane jako algorytmy identyfikacji zmian rezystancji elektrod.

Polish emotional speech recognition based on the committee of classifiers

100%

Kamińska D. , Sapiński T.

tom R. 93, nr 6

101--105

This article presents the novel method for emotion recognition from polish speech. We compared two different databases: spontaneous and acted out speech. For the purpose of this research we gathered a set of audio samples with emotional information, which serve as input database. Multiple Classifier Systems were used for classification, with commonly used speech descriptors and different groups of perceptual coefficients as features extracted from audio samples.

Niniejsza praca dotyczy rozpoznawania stanów emocjonalnych na podstawie głosu. W artykule porównaliśmy mowę spontaniczną z mową odegraną. Na potrzeby zrealizowanych badań zgromadzone zostały emocjonalne nagrania audio, stanowiące kompleksową bazę wejściową. Przedstawiamy nowatorski sposób klasyfikacji emocji wykorzystujący komitety klasyfikujące, stosując do opisu emocji powszechnie używane deskryptory sygnału mowy oraz percepcyjne współczynniki hybrydowe.

Offline handwritten pre-segmented character recognition of Gurmukhi script

71%

Kumar M. , Jindal M. K. , Sharma R. K. , Jindal S. J.

tom Vol. 25, No. 1/4

45--55

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offline handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

Extracting mass concentration time series features for classifcation of indoor and outdoor atmospheric particulates

48%

Hussain L. , Aziz W. , Saeed S. , Rafque M. , Nadeem M. S. A. , Shim S.-O. , Aftar S. , Pirzada J.-u. R.

Acta Geophysica

2020

tom Vol. 68, no. 3

945--963

Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously afect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classifcation models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM2.5 (aerodynamics size up to 2.5 μ) and PM10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six diferent locations of the Muzafarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifers. The support vector machine (SVM) kernels, ensemble classifers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fne Gaussian and cubic SVM as well as with the cubic and weighted KNN.