Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Security reflects a permanent and complex movement that complies with international and societal needs and developments in all its dimensions, interactions and levels. To constitute a universal demand for all States, communities and individuals. The question of security is one of the most important motivations and motivations that govern the behavior, and even the objectives of those societies and States. These groups or individuals have always sought to avoid fear and harm, and to provide stability, safety and security. In the light of this, security studies have been among the important fields of study in the field of international and strategic relations. The field witnessed many theoretical efforts, from the traditional perspective,

Abstract

             This paper follows the growing interest and continuity of Islamic finance products worldwide, which has encouraged the formulation of financial institutions based on the concepts of Islamic Sharia in many countries of the world and is no longer limited to Islamic countries only, and  Not exclusive to Muslims which is due to Islamic finance services and their ability to apply in non-Islamic societies, and perhaps what encouraged the development and progress of this industry Islamic history, which was attended by many different models With the development of trade's share between different countries as well as trips carried out by Muslims

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an