In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N

The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint P

Image Fusion Using A Convolutional Neural Network

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time.  In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D

The present research aimed to test the imagination of children, and may build sample consisted of (400) a baby and child, selected by random way of four Directorates (first Resafe, second Resafe ,first alkarkh , second alkarkh), in order to achieve the objective of research the tow researchers have a test of imagination and extract the virtual and honesty plants distinguish paragraphs and paragraphs and difficulty factor became the test consists of (32), statistical methods were used (Pearson correlation coefficient, coefficient of difficult passages, highlight paragraphs, correlation equation, an equation wrong Standard) the tow researchers have a number of recommendations and proposals.

SummaryBackground: Rotavirus infection is the most commoncause of watery viral diarrhea in children younger than 5 years of age; it is a major cause of childhood morbidity and mortality.Objective:The aim of the study is todetermine the clinical picture, age distribution of patients with rotavirus infection and their maternal educational background.Patients &methods: A total of 202 patients suffering from diarrhea were included in this study, over 6 months period( from 1stof March 2011to 30th of August 2011),in Children Welfare Teaching hospital. History and physical examinationwere carried out, anthropometrics measures were done and plotted on Centers for Disease Control& World Health Organization charts to determine the nut

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff