Technological development in all areas of life has played an effective role in the development of electronic systems. They have become one of the necessary modern building systems used by the government in order to facilitate the work of employees and reduce the effort and time of the employee and citizen worker. This research suggested searching for an electronic platform for private property matters between departments so that all specializations would be transferred from the basic system to the electronic system. This system is considered a platform and does not require submitting ownership transfer requests easily and quickly via the Internet. The system provides a great opportunity to progress their transactions on a regular and continuous basis, providing accurate information about the progress of their transactions and the specific time for completing them.

Software applications have become widely spread in an unprecedented manner in our daily lives, controlling some of the most sensitive and critical aspects within institutions. Examples include automated systems such as traffic control, aviation, and self-driving cars, among many others. Identifying software defects in these systems poses a challenge for most software-producing companies. In order to develop high-quality and reliable software, companies have turned to defect prediction using machine learning, relying on historical project datasets. This study aims to classify software defect prediction using machine learning techniques, specifically classification techniques. One of the classification techniques employed is eXtreme Gradient Boosting (XGBoost), a useful method for regression and classification analysis based on a gradient boosting decision tree (GBoost). XGBoost incorporates several hyperparameters that can be fine-tuned to enhance the model's performance. The employed hyperparameter tuning method is grid search, validated thereafter using 10-fold cross-validation. The hyperparameters configured for XGBoost include n_estimators, max_depth, subsample, gamma, colsample_bylevel, min_child_weight, and learning_rate. Based on the results of this study, it has been demonstrated that the utilization of algorithms with hyperparameter tuning can improve the performance of the XGBoost algorithm in accurately classifying software defects with high precision.

Internet of Thing (IoT)" is a major and emerging technology where different devices are associated together to operate smartly deprived of human’s intervention. IoT has a great effect on economic, social and commercial life. The extremely large number of associated devices together through different protocols make it vulnerable to be threatened by several types of attacks, such as; Unauthorized Access and Authentication Attacks, Network Attacks, Data Privacy and Integrity Attacks, and Supply Chain Attacks. Building and developing lightweight authentication systems for IoT devices are indeed crucial due to several compelling reasons, including; Resource Constraints, Security Concerns, Scalability, Bandwidth Efficiency, Latency Considerations, Cost Reduction, Long Lifecycle and the overall User Experience. Blockchain and “Machine Learning (ML)” are emerging technologies that may be exploited in solving different security problems. This paper presents a survey of lightweight authentication protocols and schemes that are adopted and applied for IoT environment. The security issues in IoT environment are also discussed besides the different types of attacks that may face the network. The paper held a comparison between the selected works and studies in terms of different criteria including the benefits, the results, the used applications and technologies or methods.

In this paper, we study a reducible method which is called linearization(Lineartransform) for some non-linear stochastic differential equations (SDEs) to linear by using the Ito-integrated formula. And then finding their analytic solution, we compare the obtained solution for the nonlinear SDEs with the approximate solution by using numerical (Euler -Maruyama and Milstein) Methods.

Restricted detour distance between two vertices and of a graph is the length of a longest path for the induced condition 〈 ( )〉 . The restricted detour polynomial depends on restricted detour distance and is denoted by ( ) and defined by where the summation is taken over all unordered pairs ( ) of distinct vertices of and of ( ). In this paper, we investigate the restricted detour polynomial of the straight chain of wheel graphs ( ) and compute the restricted detour index of ( ).

Under a perturbed triple product defined on three semisimple commutative Banach algebras with the influence of two homomorphisms defined on two of them and that carry certain characteristics, we proved that the spectral extension property is stable.

Melanoma is considered a serious health disease and one of the most dangerous and deadly types of skin cancer, due to its unlimited spread. Therefore, detection of this disease must be early and sound due to the high mortality rate. It is driven by researchers' desire to use computers to obtain accurate diagnostic systems to help diagnose and detect this disease early. Given the growing interest in cancer prediction, we have presented this paper, a systematic review of recent developments, using artificial intelligence focusing on melanoma skin lesion detection, particularly systems designed on neural networks. Using the neural networks for melanoma detection could be part of system of assistance for dermatologists who must make the final decision on whether to recommend a biopsy if at least one of the dermatologist's diagnoses and the support system (a helpful method) indicate melanoma or to investigate if another type of cancerous lesion exists. In the latter situation, the system can be trained to recognize distinct types of cancerous skin lesions. On the other hand, the system is incapable of making final decisions. Given neural networks' evolutionary patterns, updated, changed, and integrated networks are expected to increase the performance of such systems. Based on the decision fusion, theoretical and applied contributions were studied using traditional classification algorithms and multiple neural networks. The period 2018-2021 has been focused on new trends. Also for the detection of melanomas, the most popular datasets and how they're being used to train neural network models were presented. Furthermore, the field of research emphasized in order to promote better the subject during different directions. Finally, a research agenda was highlighted to advance the field towards the new trends.

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.

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.

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.