Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Singing has significant importance being a major basis for the expressive and cultural production of the societies and a real companion that reflects their artistic career and is strongly connected to the reality of the peoples and the production of the individuals, who are geniuses of arts and culture.
Rural singing represents one of the most well-known artistic singing styles in Iraq, which truly embodied the Iraqi national identity. However, it remained confined to the countryside and did not spread due to the lack of mass media and the recording technologies at that time. It has been pure virgin singing art. The theoretical framework is divided into three axes:

• The Iraqi singing heritage in the twentieth century, a hi

           Is one of the processes of educational guidance to help the individual to design educational plans that fit with the abilities and inclinations and goals.
And research aims the current instruction program heuristic therapeutic knowledge to deal with emotional disorders. And may the researcher instruct a program according to the theories of interested and competent guidance to education and has studied the large number of studies available in this field, as has been the program on a number of specialists in education and Psychology and took their views. And then was adopted the final version of the indicative program, consistent with the sample, which was built

Face recognition is a type of biometric software application that can identify a specific
individual in a digital image by analyzing and comparing patterns. It is the process of
identifying an individual using their facial features and expressions.
In this paper we proposed a face recognition system using Stationary Wavelet Transform
(SWT) with Neural Network, the SWT are applied into five levels for feature facial
extraction with probabilistic Neural Network (PNN) , the system produced good results
and then we improved the system by using two manner in Neural Network (PNN) and
Support Vector Machine(SVM) so we find that the system performance is more better
after using SVM where the result shows the performance o

        Throughout this paper, three concepts are introduced namely stable semisimple modules, stable t-semisimple modules and strongly stable t-semisimple. Many features co-related with these concepts are presented. Also many connections between these concepts are given. Moreover several relationships between these classes of modules and other co-related classes and other related concepts are introduced.

 

This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

Medical imaging is a technique that has been used for diagnosis and treatment of a large number of diseases. Therefore it has become necessary to conduct a good image processing to extract the finest desired result and information. In this study, genetic algorithm (GA)-based clustering technique (K-means and Fuzzy C Means (FCM)) were used to segment thyroid Computed Tomography (CT) images to an extraction thyroid tumor. Traditional GA, K-means and FCM algorithms were applied separately on the original images and on the enhanced image with Anisotropic Diffusion Filter (ADF). The resulting cluster centers from K-means and FCM were used as the initial population in GA for the implementation of GAK-Mean and GAFCM. Jaccard index was used to s

The current study was concerned to address these gaps in literature by identifying: first level and type of future visions among the next graduation student, second level and type of future visions among the sample according to gender (Male- Female), third level of the illegal immigration among the sample, fourth level of the illegal immigration according to gender (Male, Female), and finally the relationship between future visions and illegal immigration. To achieve the aims of the current study, the researchers created a questionnaire for the future visions. The psychometric properties (e.g. face validity, structure validity, and reliability) were tested. Base on the current sample, results showed that the questionnaire had sound psych