Abstract: From time to time, we hear in the news about a breach or attack on some well-known companies as just news, but it is a serious problem because it is the privacy of citizens, their money in trade and managing their businesses and projects. In this paper, we offer a review of the honey encryption planner. Honey Encryption is the encryption system that ensures flexibility versus the brute-force attack through the provision of plain reasonable text, but false for each key is invalid utilized by a trespasser to decrypt a message, two key areas are open it is difficult to create a compelling message trap that's perfect enough to deceive the striker even when he believes that he has the message in its original form.  The next problem, the typo issue, where a valid phony plain text seem to a lawful user when he accidentally enters the wrong key. Our goal is to have more satisfaction disguised tricks that are perfect enough to prevent a trespasser from getting the original message, We also need new security methods because the attackers are looking for new ways to attack the systems, so we proposed a new way to protect messages and passwords well and difficult to break and take all the possibilities of attack, including the brute-force, and then the data is hidden in an image with a public secret key.

Abstract: Cloud computing provides facilities. These facilities increased demand for its using as institutions and individuals moved to the cloud service. Therefore, cloud service providers must provide services to users based on the expected quality. One of the main challenges presented by the cloud computing is the Quality of Service management. QoS management is defined as allocating resources to applications to ensure service based on reliability, performance and availability. It is necessary to allocate resources based on load balancing that allows avoiding overloading or low loading in virtual machines, and this is a challenge for researchers in the field of cloud computing. This research highlights the importance of cloud computing, its types and importance, It also reviews some researches in the field of quality assurance of service in computing.

Abstract:  Recently,The Automated Teller Machines (ATM) and Point of Sale (POS) are based on the Europay, MasterCard and VisaCard (EMV) protocol. The goal of the EMV protocol is to enhance and improve the level of transaction security at both ATMs and Points of Sale. Despite the high performance of electronic payment systems, they suffer from attacks that can lead to unauthorized disclosure of cardholder data. This paper describes the EMV protocol and its features, and common attacks that threaten EMV card users in transactions at both ATMs and Points of Sale. The study will document the vulnerabilities that threaten EMV card holders and provide countermeasures against various potential attacks. It also describes the proposed methods that have been introduced in recent years to overcome these attacks and enhance the security level of the EMV protocol. The results of the comparison showed that biometrics has the highest performance in card security based on the EMV protocol with additional improvements in the encryption phase against all types of attacks.

Abstract: The Cuckoo Search (CS) algorithm is an effective swarm intelligence optimization algorithm whose important developments were presented by Yang and Deb in 2009. The CS algorithm has been used in many applications to solve optimization problems. This paper describes an overview of the applications of CS in the scope of image processing to solve optimization problems for the image during the years 2015-2021. The main categories reviewed that used CS in the field of image processing are: image segmentation, image optimization, image noise removal, image classification, feature extraction in images, image clustering and edge detection. The aim of this paper is to provide an overview and summarize the literature review of applying CS algorithm in these categories in order to extract which categories that applied this algorithm more than others. From this review we conclude that CS was mostly applied in the image segmentation category to optimize the threshold search.

Abstract: We prove that a positiveasolution to a given boundaryaproblem exists and is unique. This new boundaryacondition  relates theanon-local unknown value of unknownafunction at λ with its influenceadue to aasup-strip (µ,1), 0<λ<µ<1 .Our resultsaare aobtainted by using " Banach andaKrasnoselskii’satheorems"a linked to anywhere. Some classicalatheorems of fixedapoints assistance to achieve the greatestaresults.

Abstract: Nowadays, due to the rapid increment of the pandemic COVID-19 which affects the teaching environment, a need appeared to use a new alternative education style, which helps to decrement the injuries and the continuation of the education. This study designs an application to measure the teachers’ readiness to use e-learning. A questionnaire was designed to collect as much information as possible to measure the teachers' readiness to adopt e-learning in Iraqi schools after pandemic COVID-19 by analyzing the factors that affect the education process. The questionnaire consists of thirty-three questions in the Arabic language and includes three sections (background information, attitude toward e-learning, and computer skills). The collected responses are taken from Mosul’s primary and secondary schools’ teachers and the sample size is (261). A test of reliability was carried out on the study instrument, and the value of Cronbach’s alpha was 0.913. The MATLAB R2014a was used to build an application in order to do the analyzing process and determine the readiness of e-learning.

Abstract: Deep learning approaches have shown to be useful in assisting physicians in making decisions about cancer, heart disease, degenerative brain disorders, and eye disease. In this work, a deep learning model was proposed for the diagnosis of retinal diseases utilizing optical coherence tomography X-ray pictures (OCT) to identify four states of retina disease. The proposed model consists of three different convolutional neural network (CNN) models to be used in this approach and compare the results of each one with others. The models were named respectively as 1FE1C, 2FE2C, and 3FE3C according to the design complexity. The concept uses deep CNN to learn a feature hierarchy from pixels to layers of classification retinal diseases. On the test set, the classifier accuracy is 65.60 % for a (1FE1C) Model, 86.81% for (2FE2C) Model, 96.00% for (3FE3C) Model, and 88.62% for (VGG16) Pre-Train Model. The third model (3FE3C) achieves the best accuracy, although the VGG16 model comes close. Also, this model improves the results of previous works and paves the way for the use of state-of-the-art technology of neural network in retinal disease diagnoses. The suggested strategy may have a bearing on the development of a tool for automatically identifying retinal disease.

Abstract:  Images captured at night with low-light conditions frequently have a loss of visible details, inadequate contrast, low brightness, and noise. Therefore, it is difficult to perceive, extract, and analyze important visual information from these images, unless they were properly processed. Different algorithms exist to process nighttime images, yet most of these algorithms are highly complex, generate processing artifacts, over-smooth the images, or do not improve the illumination adequately. Thus, the single scale retinex (SSR) algorithm is adopted in this study to provide better processing for nighttime images. The proposed algorithm starts by converting the color image from the RGB model to the HSV model and enhancing the V channel only while preserving the H and S channels. Then, it determined the image’s illuminated version somewhat like the SSR, computes the logarithms of the illuminated and original images, then subtracts these two images by utilizing an altered procedure. Next, a modified gamma-adjusted Rayleigh distribution function is applied, and its outcome is processed once more by an automatic linear contrast stretching approach to produce the processed V channel that will be utilized with the preserved H and S channels to generate the output RGB image. The developed algorithm is assessed using a real dataset of nighttime images, evaluated using three dedicated image evaluation methods, and compared to ten dissimilar contemporary algorithms. The obtained results demonstrated that the proposed algorithm can significantly improve the perceptual quality of nighttime images and suppress artifact generation rapidly and efficiently, in addition to showing the ability to surpass the performance of different existing algorithms subjectively and objectively.

Abstract:  Low-contrast images are viewed with obscured details and are unfavorable to the observer. Hence, it is a necessity to process such an effect efficiently to get images with lucid details as the need for clear images become a global demand. Therefore, a statistics-based algorithm of simple complexity is introduced in this research to process color and grayscale images with low contrast. The proposed algorithm consists of five stages, where the first and second stages include the use of two different statistical s-curve transformations, the third stage combines the outputs of the aforesaid stage, the fourth stage improves the brightness, and the fifth stage reallocates the pixels to the natural interval. The proposed algorithm is compared with six modern algorithms, and the outputs are evaluated using two no-reference methods. The obtained results show that the proposed algorithm performed the best, providing the highest image evaluation readings and it was the fastest among the comparison methods.

Abstract: Video conferencing has become a critical need in today’s world due to its importance in education and business to mention a few; also, recent years have witnessed a great revolution in communication technologies. However, there still exist limitations in these technologies in terms of the quality of communication established between two peers. Therefore, many solutions have been suggested for a variety of video conferencing applications. One of these technologies is Web Real-Time Communication technology (WebRTC). WebRTC provides the ability to efficiently perform peer-to-peer communication, which improves the quality of the communication. This work tries to propose a WebRTC bi-directional video conferencing for many-to-many (mesh topology) peers. In this work, signaling was obtained using Socket.io Library. The performance evaluation of the proposed approach was performed in terms of CPU performance, and Quality of Experience (QoE). Moreover, to validate the simulations results, a real implementation was achieved based on the following scenarios a) involving several peers, b) at the same time, opening several video rooms, c) a session will still be active even when the room initiator leaves, and d) new users can be shared with currently involved participants.

Abstract: Multilevel Security (MLS) is one of the ways that protects the stored information in the computer and mobile devices. It classifies users and information into levels of security; thus, the user can access information within its level or less. A smartphone is used in managing some of businesses, controlling the home and car devices within the smart city environment by using a set of data stored in the database. The database is used by more than one authorized user some of this data is confidential and important that requires protection from un authorized users. In this research a proposed system to implement the MLS principle within three levels of security is presented. The first level gives the user its own security level. The second level transfers users through the system parts according to their security level (system administrator or regular user). The third level allows users to manipulate the stored encrypted data in SQLite database by using a simple and quick cryptographic algorithm. The proposed system is implemented in the smart mobile devices which are supported by the Android operating system. The experimental result showed that the proposed system has the ability to protect the data in the database and prevents users to view the data at upper levels. Also, the inability of users to change the security level of data that prevents the leak of data from the upper security levels to the lower level. Moreover, the proposed system works quickly and needs a little storage space.

Abstract: This study introduce a new type of closed sets in topology called Generalized h-closed sets (briefly, gh-closed) define as follow: E  ⊆ χ  be gh-closed set if CLh (E)⊆ U  whenever E ⊆ U and U is open set in (χ,τ). The relation between gh-closed set and other classes of closed sets ( h-closed, g-closed, g -closed, g-closed and αg-closed) are studied. Also, the notion of gh-continuous mapping on topological space is introduce and some properties are proved. Finally, the separation axioms have been studied.

Abstract: Vehicle ad hoc networks are considered mobile networks where the nodes are mobile objects and can change their positions within an environment over time. These objects can be connected at any time according to a predefined strategy. Simulating this kind of network needs high attention to many details. Moreover, the literature lacks works that describe the requirements of simulating such networks. Therefore, this work tries to describe the requirements of simulating vehicle networks (VANETs). Moreover, the goal is to determine what is needed to simulate vehicle networks in terms of the distribution of vehicles, the movement patterns, and the routing protocols used. The simulation results show interesting facts about the VANET networks and the best strategies to minimize the consumption of network resources. Finally, this work considers two communication technologies among network nodes; Wi-Fi 5 and Wi-Fi 6.

Abstract: Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing.  TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework), a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework- for disease classification is discussed.

Abstract: Cost management is one of the performance standards in computer networks and routing strategies through which we can get effective paths in the computer network, reach the target and perform highly in the network by improving the routing table (jumps). This paper is an attempt to propose a new H design mixed algorithm (ACO-GA) that includes the best features of both ACO and GA with a new application that combines both previous algorithms called( H- Hybrid (ACO-GA) hybrid algorithm technology, which differs in its parameters. In order to research and find the optimal path, the improved ant algorithm was used to explore the network, using smart beams, getting the paths generated by ants and then using them as inputs into the genetic algorithm in the form of arranged pairs of chromosomes. Experimental results through extensive simulations showed that H (ACO-GA) improves the routing schedule, represented by the pheromone values that ants leave when following their path in the network. The values given in the table( 3.2) vary according to the quality of the pheromone concentration. In this case, it is possible to give the greatest opportunity to choose the best quality according to the concentration of the pheromone. For this purpose, a network consisting of four nodes (1), (2), (3), (4) was used starting with node (1) which is the source node and the destination node (2), by calling the selection technique to update the pheromone table by choosing the path to node (1 ). For this case and for selecting the destination node (2), the pheromone table for the nodes visited by the ant is updated. We calculated the final destination )2) by dividing the ratio. Thus, we get to reduce the search area, speed up search time, and improve the quality of the solution by obtaining the optimum set of paths.

Abstract: In this paper we use the separation of variables method and L24c transform method to solve three-dimensional conduction heat equation in cylindrical coordinate and results plotted by using Matlab. It was concluded that L24c transform method is better than the method of separating the variables because it is a method that reaches the solution with fewer steps

Abstract: The main goal of this work is to find a spread of PG(3,13). By construct a complete span which represents applications of algebraic geometry in 3-dimensional projective space PG(3,q). We prove that the maximum (Ƙ,b)-span in PG(3,13) is (170,b)-sp -span, which is a spread.                                                         

Abstract: This study aims at recognizing the role of neural networks in deciding administrative decisions in banks. To achieve the aims, the study developed a suggested model that depends on artificial neural networks as a stabilization tool to support loans management decisions. The Descent Conjugate Gradient algorithm is adopted to build the suggested model through checking loan demands according to the various banking instructions. The results showed that using such techniques in administrative business was a success through evaluating loan demands and deciding the most appropriate ones, with the possibility of refusing or accepting the agent’s demand, and also the possibility of deciding the loans which were demanded more than the other types.