Violence is one of the most serious threats facing societies because it affects their internal structure and threatens the security and stability of society. It is classified as one of the types of security crises that are emerging in Arab and Islamic societies in particular, and in most countries of the world in general.

The threat of this crisis is increasing. Terrorism is considered as one of the most serious aspects of that all the countries of the world, currently, suffer from. The terrorism has begun to penetrate deep into society in one way or another starting from the Second World War, which led to the emergence of leftist movements in Western Europe, Japan, France, Italy and other countries as a result of emerging ideas

Many reasons combined behind the Standing of U.S. against Britain in its aggression against Egypt in 1956; the consensus of world opinion on the need to stop the aggression and the fear of the Soviet military intervention which mean a new world war.
United States desired to weaken British influences in the region in general to get new oil gains in the Arabian Gulf and Egypt at the expense of Britain. The exiting of Britain from the area served U.S. strategic interests in the Middle East in general and Egypt in particular to keep the flow of oil for U.S advantage.
The United States wanted to keep its image in the region to apply its future political projects including Eisenhower Project, which intended to take the position of Britis

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

Cloud computing (CC) is a fast-growing technology that offers computers, networking, and storage services that can be accessed and used over the internet. Cloud services save users money because they are pay-per-use, and they save time because they are on-demand and elastic, a unique aspect of cloud computing. However, several security issues must be addressed before users store data in the cloud. Because the user will have no direct control over the data that has been outsourced to the cloud, particularly personal and sensitive data (health, finance, military, etc.), and will not know where the data is stored, the user must ensure that the cloud stores and maintains the outsourced data appropriately. The study's primary goals are to mak

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method