The aim of this study is to investigate the main causes of the family violence and its
social effects.
The study reveals that main factors causes of the violence are interaction between
multi-factors.
The study shows that the poverty, low educational level, house crowded, large family
size, all of these variables causes of the violence, and then causes of family disorganization
and juvenile delinquency.
The study contain of three sections:
First: Focused on the causes of the violence.
Second: The interaction between multi factors.
Third: Social effects of family violence.

The great raise and development of residential buildings in modern cities worldwide as a result of urban extends leads to environmental and social problems, that make the designers looking for more complicated and innovative solutions. To encounter these, most advanced technologies in construction had been used resulting buildings had become higher, which was moved away from the land called residential housing. And with the development of these buildings, increase in the inhabitants inside; generate distant from nature, which increased the need for interactive outdoor recreational spaces open green in its high sections, was an alternative or complementary option to outer space at the ground level. Therefore, the research problem has emer

The current research aims to know the measurement of the fifth- secondary stage female  students' affliction with the social phobia . The research is confined to General directorate of Education in Baghdad / Al-Karkh/2 / for the academic year 2015-2016 and fifth-secondary stage female students. It has been used descriptive method and  exposed theoretical sides and previous studies concerning with the  social phobia.The procedures of the research include the application of the  social phobia scale on random sample of  fifth- secondary stage female  students ( both  branches scientific and literary). The current study has concluded  that the  fifth- secondary stage female  students have&nbs

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show

     Structure of network, which is known as community detection in networks, has received a great attention in diverse topics, including social sciences, biological studies, politics, etc. There are a large number of studies and practical approaches that were designed to solve the problem of finding the structure of the network. The definition of complex network model based on clustering is a non-deterministic polynomial-time hardness (NP-hard) problem. There are no ideal techniques to define the clustering. Here, we present a statistical approach based on using the likelihood function of a Stochastic Block Model (SBM). The objective is to define the general model and select the best model with high quality. Therefor

A new simple sensitive and selective spectrophotometric method has been
developed for the analysis of vanadium(V) in three randomly chosen samples from
river water at different locations by continuous flow injection analysis. The method
based on the oxidation of pyrogallol by vanadium(V) in acidic solution to form color
species and the same species was determined using homemade Ayah 6SX1-T-2D
solar cell analyser . Chemical and physical parameters were investigated using the
high intensity of snow white light emitted diode as a source. The linear dynamic
range for the instrument response versus vanadium(V) concentration was 1-200
mg.L-1 with correlation coefficient r = 0.9920. The limit of detection (S/N=3) was 70<

A newly developed analytical method characterized by its speed and sensitivity for
the determination of cadmium (II) in aqueous solution in three randomly chosen
samples from river water at different locations via turbidimetric measurement by
Ayah 6SX1-T-2D Solar - CFI analyser. The method is based on the formation of
yellowish white precipitate for the complex Cd3[Fe(CN)6]2 by direct reaction of the
cadmium (II) with potassium hexacyano ferrate (III) in aqueous medium. Turbidity
was measured via the reflection of incident light that collides on the surfaces
precipitated particles at 0-180o. Chemical and physical parameters were investigated.
Linear dynamic of cadmium (II) is ranged from 0.05-12 mmol.L-1, with cor