<h2>Data security has become a paramount necessity and more obligation in daily life. Most of our systems can be hacked, and it causes very high risks to our confidential files inside the systems. Therefore, for various security reasons, we use various methods to save as much as possible on this data, regardless of its different forms, texts, pictures, videos, etc. In this paper, we mainly rely on storing the basic image which should be protected in another image after changing its formal to composites using the DWT wavelet transform. The process of zeroing sites and storing their contents technique is used to carry the components of the main image. Then process them mathematically by using the exponential function. The result of this process is to obtain a fully encrypted image. The image required to be protected from detection and discrimination is hidden behind the encrypted image. The proposed system contains two algorithms. the first algorithm is used for encryption and hiding, but the second algorithm is designed for returning and decoding the main image to its original state with very efficiently.</h2>

<p class="Abstract">This New learning educational methods, which depending on Learning Management System (LMS), have been used by universities in the top education universities in the world. However, most of the Iraqi universities use the traditional education methods in the classroom. The purpose of this study was to examine the benefit of using LMS in higher education.  This study shows how to implement and use modern educational techniques in Engineering College of Wasit University. This paper shows that using modern education tools in the class lead to increase the productivity of student, save time with less effort for instructors with high accuracy of exam results. In addition, using LMS system allows students to obtain more information in a short time. Moreover, this system gives students an opportunity to interact with the instructor and among themselves. The findings show the benefits of integrating LMS in higher education and recommend other institutions to implement it.</p>

<p>The global online communication channel made possible with the internet has increased credit card fraud leading to huge loss of monetary fund in their billions annually for consumers and financial institutions. The fraudsters constantly devise new strategy to perpetrate illegal transactions. As such, innovative detection systems in combating fraud are imperative to curb these losses. This paper presents the combination of multiple classifiers through stacking ensemble technique for credit card fraud detection. The fuzzy-rough nearest neighbor (FRNN) and sequential minimal optimization (SMO) are employed as base classifiers. Their combined prediction becomes data input for the meta-classifier, which is logistic regression (LR) resulting in a final predictive outcome for improved detection. Simulation results compared with seven other algorithms affirms that ensemble model can adequately detect credit card fraud with detection rates of 84.90% and 76.30%.</p>

<pre>One of the techniques used in information security is the concealment technique, where the information to be hidden within another information medium to be saved in the process of messaging between two sides without detection. In this paper, an algorithm was proposed to conceal and encrypt data using several means.in order to ensure its preservation from detection and hackers. Wavelet transformer was used to change the shape of a wave of information (one and two-dimensional data) and its different mathematical formulas. Two sets of data were used, the first group used in a hidden process. The second group was considered as a means of both embedding and encryption.  The data in the second group is reduced to the extent of sufficient for the modulation process, by extracting its high-value properties and then removing them from the mother's information wave. The process of encrypting of the two sets of data comes together using an exponential function. The result is undetectable information signals. Algorithms were built to hide and encrypt one and two-dimensional data. High-security signals and images were obtained. Decryption algorithms were built to return encrypted data to their original forms, and getting the replica data.</pre><p> </p>

The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.