Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.
The aim of the present work, was measuring of uranium concentrations in 25 soil samples from five locations of Al-Kut city. The samples taken from different depths ranged from soil surface to 60cm step 15 cm, for this measurement of uranium concentrations .The most widely used technique SSNTDs was chosen to be the measurement technique. Results showed that the higher concentrations were in Hai Al- Kafaat which recorded 1.49 ± 0.054 ppm . The uranium content in soil samples were less than permissible limit of UNSCEAR(11.7ppm).
Twenty five vaginal swabs from outpatients' healthy women were collected from Kamal Al-Samarai Hospital, Baghdad, to isolate and identify of Lactobacillus acidophilus. Three isolates were diagnosed as L. acidophilus which represents 15% of the total number of lactic acid bacterial (LAB) isolates; other LAB types represent 65% (20 isolates).The ability of L. acidophilus to produce surlactin was detected after measuring its biological activity to inhibit the adhesion of biofilm formed by Pseudomonas aeruginosa to surfaces using test tube method. It was found that all isolates were able to produce surlactin but the activity of surlactin was varying in each isolate. Surlactin produced by isolates 1 and 13 was the most effective. Biological appl
... Show MoreBackground: Animal bite is one of the public health problems all over the world, especially in poor countries. Animal bites have an impact on human health due to rabies disease, which is a viral transmitted disease from animal to human with a high mortality rate.
Objective: To determine the epidemiological characteristics of animal bite cases by person, time, and place.
Method: Descriptive cross sectional study was done by reviewing cases caused by animal bites., Data including the demographic characteristics of age, gender, occupation, site of bite, and attending health institutions searching treatment were all included.
Results: There were 11600 animal bite cases. Most of bites caused by stray dogs 11577(99.8%), and the males
In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo
... Show MoreThe last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of
... Show MoreThe current study included bioremoval of chromium metal ions from aqueous solution by using seventeen Pseudomonas aeruginosa species isolated from different environments. The experimental results showed that isolates Pseudomonas aeruginosa have high efficiency in removal of chromium where the P. aeruginosa p.8 was the most efficient (P≥0.001) in bioremoval of chromium with a removal capacity reached 92.5 mg/L and removal index reached (96.5%). While P. aeruginosa p.4 was the least efficient (P≥0.001) in bioremoval of chromium from aqueous solutions reached 74.6 mg/L and removal index reached (79.8%). The REP-PCR detection using BOX-primer, showed genetic relatedness among the isolates of P.aeru
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