Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

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

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

The main focus of research is on how to achieve the internal and external dimensions of corporate social responsibility through human resources management strategies, which is a major of research aimed. The main problem of this research was confirmed, which confirms that there is an unclear role for social responsibility, lack of human resources management strategies, and ambiguity of roles in the municipality under study. The diagnose of the problem and determining the gap between the internal and external dimensions of social responsibility and human resources management was identified, which attacked the researcher's attention to navigate in this subject, look for the reasons for the gaps and try to reduce them. The case study

The aim of the study was to identify the use of the strategies of numbered heads and brainstorming in learning handspring on the Vault table in artistic gymnastics for Men for the third grade students in the collage of Physical Education and Sport Sciences, as well as to identify the best group among the three research groups (number of heads numbered,, brainstorming strategies And the traditional style group) to learn the skill under study. Using the experimental method, the research subject included the third grade students in the collage of Physical Education and Sports Sciences / University of Baghdad, and randomly by lot, 12 students were selected for each of the three research groups. The study consisted of the arithmetic mean

the Current research aims to identify the psychological stressors coping strategies and their relationship to the cognitive motivation among Al-Anbar University students through the following hypotheses: 1) no statistically significant differences at a level (0.05) among the sample according to the instrumental support strategy depending on the variable type and specialization, 2) No statistically significant differences at a level (0.05) among the sample in regard of coping avoiding strategy depending on the variable type and specialization, 3) There is no statistically significant difference at a level (0.05) in cognitive motivation level among Al-Anbar University students, 4) No statistically significant differences at a level (0.05)

The research aims to identify and diagnose the public relations strategies in its digital online communications by the United Nations High Commissioner for Refugees (UNHCR) in managing the crisis of Iraqi refugees in Turkey. A content analysis form was designed for the digital content of the UNHCR's website dedicated to topics and issues concerning Iraqi refugees that were covered by the site, adopting a comprehensive enumeration approach. The study covered the period from 01/03/2022, to 30/06/2022. The research yielded several key findings, including the predominant use of media, advertising, and education strategies in managing the crisis of Iraqi refugees in Turkey. News and reports ranked first among the media