Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and k-nearest neighbor (kNN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature

Metal-semiconductor-metal UV photodetector based on Ga doped ZnO/graphene interface

Fabrication and characterization of metal-semiconductor-metal (MSM) ultraviolet (UV) photodetector (PD) based on Ga doped ZnO (ZnO:Ga)/graphene is presented in this work. A low dark current of 8.68 nA was demonstrated at a bias of 1 V and a large photo to dark contrast ratio of more than four orders of magnitude was observed. MSM PD exhibited a room temperature responsivity of 48.37 A/W at wavelength of 350 nm and UV-to-visible rejection ratio of about three orders of magnitude. A large photo-to-dark contrast and UV-to-visible rejection ratio suggests the enhancement in the PD performance which is attributed to the existence of a surface plasmon effect at the interface of the ZnO:Ga and underlying graphene layer. © 2015 Elsevier Ltd. All rights reserved

TNF-α and IL-1 β Cytokine Gene Polymorphism in Patients with Nasal Polyposis

Objective: Nasal Polyp (NP) is a benign mass of the paranasal sinuses that protrudes into the nasal cavity. The exact underlying pathogenesis is not known. In this study we aimed to determine the genetic susceptibility of NP formation in relation to TNF-α-308 and IL-1β-511 promoter region gene polymorphisms.Methods: A total of 71 patients with NP with asthma (n=21) or without asthma (n=50) were taken as the study group, and 91 healthy volunteers were taken as the control group. Blood was gathered into EDTA-containing tubes, and patient DNA was extracted. The polymorphisms of the IL-β and TNF-α cytokine genes were analyzed using real time polymerase chain reaction.Results: The GG genotype in the TNF-α-308 region and the CC genotype in the IL-1β-511 region were found to be risk factors for NP formation (OR: 9.2, p=0.007 and OR: 33.3, p=0.001, respectively). Regarding allelic frequencies, the G allele at the TNF-α-308 promoter region was a risk factor for NP formation (OR: 6.06, p<0.001).Conclusion: TNF-α GG genotype in the -308 promoter region and the IL-1β CC genotype in the -511 region are genetic risk factors for NP formation

Volume CXIV, Number 4, November 7, 1996

Objective: Turner syndrome (TS) is a chromosomal disorder caused by complete or partial X chromosome monosomy that manifests various clinical features depending on the karyotype and on the genetic background of affected girls. This study aimed to systematically investigate the key clinical features of TS in relationship to karyotype in a large pediatric Turkish patient population.Methods: Our retrospective study included 842 karyotype-proven TS patients aged 0-18 years who were evaluated in 35 different centers in Turkey in the years 2013-2014.Results: The most common karyotype was 45,X (50.7%), followed by 45,X/46,XX (10.8%), 46,X,i(Xq) (10.1%) and 45,X/46,X,i(Xq) (9.5%). Mean age at diagnosis was 10.2±4.4 years. The most common presenting complaints were short stature and delayed puberty. Among patients diagnosed before age one year, the ratio of karyotype 45,X was significantly higher than that of other karyotype groups. Cardiac defects (bicuspid aortic valve, coarctation of the aorta and aortic stenosis) were the most common congenital anomalies, occurring in 25% of the TS cases. This was followed by urinary system anomalies (horseshoe kidney, double collector duct system and renal rotation) detected in 16.3%. Hashimoto's thyroiditis was found in 11.1% of patients, gastrointestinal abnormalities in 8.9%, ear nose and throat problems in 22.6%, dermatologic problems in 21.8% and osteoporosis in 15.3%. Learning difficulties and/or psychosocial problems were encountered in 39.1%. Insulin resistance and impaired fasting glucose were detected in 3.4% and 2.2%, respectively. Dyslipidemia prevalence was 11.4%.Conclusion: This comprehensive study systematically evaluated the largest group of karyotype-proven TS girls to date. The karyotype distribution, congenital anomaly and comorbidity profile closely parallel that from other countries and support the need for close medical surveillance of these complex patients throughout their lifespa

Kırgızistan'da Dinî Günler ve Bayramlar

This study is depends on an emprical method and it is substained by Kyrgyz's literal sources. In this study, We investigated to traditions and public beliefs concerning to religious days, nighths, and feasts in Kyrgyz society. There are three eves and different beliefs about ancestor souls on these days in Kyrgyz society. In Ramadan which believed in as month salvation from sins, there is a tradition “Caramazan”. Night recognized and celebrated as "Kandil night" is only “Kadir night". Because of the beliefs in ancestor's souls, Ramadan feast is called “feast of deads”; also Kurban feast is called “feast of alives” in terms of social characte

Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

In this paper, attribute weighting method based on the cluster centers with aim of increasing the discrimination between classes has been proposed and applied to nonlinear separable datasets including two medical datasets (mammographic mass dataset and bupa liver disorders dataset) and 2-D spiral dataset. The goals of this method are to gather the data points near to cluster center all together to transform from nonlinear separable datasets to linear separable dataset. As clustering algorithm, k-means clustering, fuzzy c-means clustering, and subtractive clustering have been used. The proposed attribute weighting methods are k-means clustering based attribute weighting (KMCBAW), fuzzy c-means clustering based attribute weighting (FCMCBAW), and subtractive clustering based attribute weighting (SCBAW) and used prior to classifier algorithms including C4.5 decision tree and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed method, the recall, precision value, true negative rate (TNR), G-mean1, G-mean2, f-measure, and classification accuracy have been used. The results have shown that the best attribute weighting method was the subtractive clustering based attribute weighting with respect to classification performance in the classification of three used datasets

Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering

This study presents the application of fuzzy c-means (FCM) clustering-based feature weighting (FCMFW) for the detection of Parkinson's disease (PD). In the classification of PD dataset taken from University of California - Irvine machine learning database, practical values of the existing traditional and non-standard measures for distinguishing healthy people from people with PD by detecting dysphonia were applied to the input of FCMFW. The main aims of FCM clustering algorithm are both to transform from a linearly non-separable dataset to a linearly separable one and to increase the distinguishing performance between classes. The weighted PD dataset is presented to k-nearest neighbour (k-NN) classifier system. In the classification of PD, the various k-values in k-NN classifier were used and compared with each other. Also, the effects of k-values in k-NN classifier on the classification of Parkinson disease datasets have been investigated and the best k-value found. The experimental results have demonstrated that the combination of the proposed weighting method called FCMFW and k-NN classifier has obtained very promising results on the classification of PD