Recognizing speech emotions is an important subject in pattern recognition. This work is about studying the effect of extracting the minimum possible number of features on the speech emotion recognition (SER) system. In this paper, three experiments performed to reach the best way that gives good accuracy. The first one extracting only three features: zero crossing rate (ZCR), mean, and standard deviation (SD) from emotional speech samples, the second one extracting only the first 12 Mel frequency cepstral coefficient (MFCC) features, and the last experiment applying feature fusion between the mentioned features. In all experiments, the features are classified using five types of classification techniques, which are the Random Forest (RF),

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

The aim of current study is to analyze the content of intermediate stage biology books based on multiple intelligences. To do this, the researcher used the descriptive analytical approach. To analyze the three books of the intermediate stage, the author adopted content analysis tool and area unit. They were exposed to group of experts in methods of teaching biology, and measurement and evaluation. The findings of the study have shown a significant difference between what was expected and the observation of the multiple intelligences for the three biology books excluded the (social, musical, and kinetic intelligence), and there is no significant difference between the analysis' result of the biology books and the expectations of b

An oral bi layer  sustained release (SR) strips of Sodium  Montelukast SMLT , which is selective  leukotriene  antagonist  , used  for  patients  suffered from mid-night asthma , were prepared successfully ,using different polymers, like guar  gum , carrageenan , and xanthan gum , by solvent casting method .

The results obtained by this study revealed  ,that best  fast  dissolving  film of  SMLT was  loaded  in carrageenan   polymer 57%  w\w (30mg.) ,  with  acceptable  physical properties, like film thickness , elastic endurance and  surface pH  .

Besides  to that , the  disintegration  t

Interpreting  is a process adopted by a skillful and well qualified interpreter to convey orally the meaning from a source language into a target language simultaneously .In this process the interpreter has no time to think or check the exact meaning of the words, phrases and sentences. The main technique used by the interpreter is based on his/her competence .This type of translation is used in press conferences and political speeches of  high rank figures.

This paper deals with analyzing the interpretation  of Obama's farewell speech  adopted by two authentic TV Channels(Sky News and AL- Jazeera).The aim of this paper is to investigate the quality of each interpreting by adopting   Nida's (1996:164

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an