This study evaluated the knowledge and practice of breast self-examination (BSE), among a sample of educated Iraqi women. The study sample comprised 858 women aged 18–62 years affiliated to 6 major Iraqi universities, categorized according to occupation as teaching staff (11.5%), administrative staff (18.0%) and students (70.5%). Data were collected by a self-completed questionnaire. In all, 93.9% of the women had heard about BSE, the main source of information was television (39.9%), doctors (18.4%) and the awareness campaign of the Iraqi National Breast Cancer Research Programme (11.6%). Only 53.9% of the women practised BSE; the most common excuses by those that did not were lack of knowledge of the significance of BSE (42.0%) and lack

Mobile-based human emotion recognition is very challenging subject, most of the approaches suggested and built in this field utilized various contexts that can be derived from the external sensors and the smartphone, but these approaches suffer from different obstacles and challenges. The proposed system integrated human speech signal and heart rate, in one system, to leverage the accuracy of the human emotion recognition. The proposed system is designed to recognize four human emotions; angry, happy, sad and normal. In this system, the smartphone is used to   record user speech and send it to a server. The smartwatch, fixed on user wrist, is used to measure user heart rate while the user is speaking and send it, via Bluetooth,

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

The problem of poverty and deprivation constitute a humanitarian tragedy and its continuation may threaten the political achievements reached by the State. Iraq, in particular, and although he is one of the very rich countries due to availability of huge economic wealth, poverty indicators are still high. In addition, the main factor in the decline in the standard of living due to the weakness of the government's performance in the delivery of public services of water, electricity and sanitation. Thus, the guide for human development has been addressed which express the achievements that the state can be achieved both on a physical level or on the human level, so in order to put appropriate strategies and policies aimed at elimin

            The variation in wing morphological features was investigated using geometric morphometric technique of the Sand Fly from two Iraqi provinces Babylon and Diyala . We distributed eleven landmarks on the wings of Sand Fly species. By using the centroid size and shape together, all species were clearly distinguished.  It is clear from these results that the wing analysis is an essential method for future geometric morphometry studies to distinguish the species of Sand Flies in Iraq.

Let f and g be a self – maps of a rational exterior space . A natural number m is called a minimal coincidence period of maps f and g if f^m and g^m have a coincidence point which is not coincidence by any earlier iterates. This paper presents a complete description of the set of algebraic coincidence periods for self - maps of a rational exterior space which has rank 2 .

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

English allows for consonant sequences at the beginning of words, which poses a challenge for speakers of other languages, especially those whose native languages do not contain such sequences, such as Arabic. Therefore, this study aims to investigate students’ recognition and production of initial consecutive consonants in relation to gender, and the significance of the statistical differences between students in recognition and pronunciation of consecutive consonants. Consequently, a cohort of fifty-two Iraqi college students (18 males and 34 females) were selected as a sample. A multiple-choice test of two phases was used as a research instrument. The results showed that females outperformed males in recognizing and producing c