Current search targeted to:
  1 - Measurement of double-thinking among university students.
  2 - Identify the significant differences in the degrees of double-thinking members of the sample according to the variables of sex (male - female), specialty (I know - a human).
      To achieve the objectives of the research chose researcher samples from the community of the first search for statistical analysis, has reached 400 students, and the second sample of the application of the final, has reached (480) students were selected randomly with simple check of equal value, and the researcher building a search tool double think, After the completion of the scale-building measures think

The current research was aimed at the following:
1. Measurement the Ambivalence among University students.
2. Identify the differences in Ambivalence among University students according to variable of Specialization (scientific / literary).
To achieve this aims of the research, the researcher set up the instrument is scale of Ambivalence that consistent (19) item. And the researchers applying this scale on the sample amounted to (200) among University students. Then after data processing statistically, the researchers reached the following results:
1. University students have Ambivalence.
2. There is no is differences in Ambivalence among University students according to variable of Specialization (scientific / literary).<

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

The current research aims to identify the problems and needs for both college of political science and college of engineering’s students. The sample was (100) male and female student. The results showed bunch of problems which could be organized descendingly, the scientific domain ranged between (2 - 2.42), the mean of the psychological domain was (2.85), the moral domain ranged between (2.2 – 2.28)m the problems of study earned (2.30), the material domain got (1.95), the medical and social domain obtained (1.925), and finally, the family domain received (1.887).

The current research aims to detect the level of suicidal tendencies among secondary school students in terms of gender and educational stage (intermediate school students and high school students). The researcher adopted Al Hafeez's (2017) scale for suicidal tendencies, it consists of (57) items including six domains, namely: suicidal ideation, social motives for suicide, tendency to self-harm, desire for death, indifference and pessimism about life, willingness to commit suicide. The scale was modified to be (42) items after it was exposed to a group of experts. The scale was applied to a sample of (200) male and female students from secondary schools in Baghdad Governorate (Karkh - Rusafa) for the academic year 2021-2022. The results

This study aims at identifying the extent of SMS usage and understanding the role it plays in satisfying users' needs and motivations. In order to achieve this aim, an analytical descriptive method was adopted by conducting a field survey among students at Petra University.

The study resulted in many conclusions, the most important of which is that using SMS meets the students' cognitive, social and communicational needs and desires, the highest being communicating with friends at 75%, followed by exchanging songs and videos at 52%, as well as exchanging photos at 45%. In regards to their motivation for using text messaging, forgetting daily problems scored highest at 81.4% and spending free time followed at 77.4%. This proves th

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep