The current research aims to identify the stressful life events among middle school students in terms of gender (male-female), academic branch (scientific-literary), and the Life Skills of the students of the preparatory stage in terms of gender (male-female), and academic branch (scientific-literary). Additionally, the study aims to identify the relationship between stressful life events and life skills in middle school students. A sample of (200) students who were selected randomly from the Directorate of education of Baghdad Karkh/ III was used in this study. To achieve the objectives of the current research, the researcher has adopted two scales, the stressful life events scale for the researcher Al-Sultan (2008), and the life skills

Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader u

     تعد الملابس وسيلة هامة لكل مايقوم به الانسان في حياته العامة ، فهي الانطباع والكلمة الخارجية عن ذاته الداخلية فهي تعكس فكرة الفرد عن ذاته وعن شخصيته , كما تعد وسيلة تعبير جمالية وفنية , فهي تساعد على اخفاء عيوب الجسد وابراز محاسنه . ويتوقف اختيار الفرد لملابسه على مجموعة  عوامل منها احتياجه , قدراته المالية , سنه , مركزه الاجتماعي , طبيعة عمله ,الظروف الجوية التي يعيش فيها وعلى مايُؤمن به من قيم و

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

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims