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Background: Technology in education has given birth to online examination systems (OES), which provide a simple and risk-free testing technique across several networks. The OES overcomes concerns inherent in manual check techniques, such as processing delays, registration, and record filtering issues. Objective: This article introduces an automated, web-based Open Educational System (OES) that is adaptable to any educational level or discipline, explaining its essential features, functions, and the technology that underpins it. The current study investigates how the OES improves test administration efficiency while protecting students' scores' privacy and security. Methods: The OES has a client-server architecture, leveraging PHP and MySQL, and functions across many networks, allowing for remote access and exam management. It includes features such as automatic test production, an analytical system, and configurable tests, all while maintaining safe and reliable test administration. Results: The OES implementation has considerably decreased the time and effort necessary for giving exams and reporting results. The system's flexibility in test customization and quick result tabulation underlines its efficiency, while its security features protect the examination process's integrity. Conclusion: The OES provides a disruptive approach to digital exams, providing educational institutions with a safe, trustworthy, and efficient alternative to conventional testing techniques. The system's flexibility and automation capabilities highlight its promise to transform testing operations by solving difficulties related to dependability, productivity, and security inherent in manual testing methods.

Background: Cloud computing (CC) is a disruptive computing paradigm that uses networked systems, notably the Internet, to provide shared resources and services. Its use has been widespread, owing partly to its promise to reduce computation costs. Objective: This study aims to get into the critical security concerns that cloud computing poses, and to provide unique solutions to these difficulties. Methods: By examining the operational procedures and infrastructure of data centers - the key suppliers of cloud services - this article elaborates on the particular risks that cloud-based operations bring. The study underlines the need of increased security, particularly considering the interconnection and apparent control customers have over ostensibly devoted resources. Results: Cloud computing offers a variety of advantages, including on-demand service delivery, geographical independence, and pay-as-you-go pricing. However, its intrinsic structure creates threats to data integrity, availability, and privacy. In response, this article presents a Dynamic Secure Interconnection (DSI) architecture that divides the cloud into dynamic virtual trust zones, each with its own set of security protocols. Conclusion: As cloud computing continues to transform IT and business environments, implementing effective security measures becomes more important. The suggested DSI mechanism is a viable way to bolstering security inside cloud systems, aiming for a safer, more regulated data management environment.

Background: Telecommunication networks play a critical role in the smooth data flow in today's digital world, making their steady operation critical. Because of the increased dependence on these networks, sophisticated monitoring and management systems are required to ensure continuous online connection and data sharing. Objective: This article aims to investigate the use of quantile and logical regression in predicting the performance of telecommunication networks. It aims to build a prediction model based on real-time data to aid intelligent decision-making systems in network management. Methods: The study employs machine learning methods to design an intelligent control system based on the autocorrelation of time-based variables. This system combines a communications network, a database of system attributes, a machine learning-based data processing system, and a decision-making system. Results: The intelligent system demonstrates the telecoms network's real-time monitoring, analysis, and management capabilities. Its goal is to provide telecommunications operators with an efficient predictive model for improving network performance and resource allocation while addressing complex network dynamics, diverse data sources, forecasting accuracy, real-time decision-making, and model resilience. Conclusion: Constructing a trustworthy mathematical model using regression techniques and machine learning greatly improves the forecasting of telecommunication network conditions. This innovation is critical in allowing proactive management techniques and ensuring end-users get high-quality services.

The purpose of this paper is to provide a high-level overview of the most important non-orthogonal multiple access (NOMA) protocols in 5G and 6G networks that incorporate code division within the context of 3GPP standardization. The article’s objective is also to look into and compare the various strategies that have been proposed as a solution to the issue of resource distribution to achieve high performance. Many different NOMA plans for 5G and 6G systems have been suggested by a multitude of businesses. NOMA is currently developing in two primary directions: one of them is with power division, and the other is with code division. During the process of standardization carried out by the 3GPP, the attention of the developers was concentrated in the second direction for the application of NOMA schemes in 5G and 6G systems. Hardware communication, also known as D2D communication, performs a significant role in the process of communication between devices. This will increase the efficiency with which network resources are utilized. Devices are now able to interact directly with one another, avoiding the need for transmission nodes. It also serves as one of the approaches to the problem of limited network coverage, which can be improved by utilizing D2D, and as a result fees and energy can be reduced. Increasing the size of the network is one way to achieve this goal, the explained of NOMA technology as well as its primary benefits in wireless technology. The most common variants of code division NOMA and the characteristics of those variants are discussed, as well as the opportunities and challenges associated with implementing those variants. NOMA protocols allow continuous expansion of wireless communication networks, i.e., 5G and 6G, which leads to enhanced performance of the networks.

Abstract MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.