Ahmed Hazim Jihad Alhilali

Epilepsy poses a significant challenge for people around the world, particularly in third-world countries. Hence, over 80 million people are suffering from this disease. Therefore, the detection of epileptic seizures plays a vital role in diagnosis by measuring the activity of the brain, like the Electroencephalography (EEG) instrument. Nowadays, Data variety, attributes and size are growing increasingly, causing high dimensionsand high preprocessing, which requires high computational resources. Therefore, reduce the data dimension to eliminate posing high computational resources in data preprocessing in the classification techniques. To address these issues, different feature selection techniques are used for high-dimensional data reduction by selecting the most relevant features in the classification process, which aim to detect disease faster and accurately. These techniques include Mean Decrease in Impurity (MDI), Correlation Coefficient, Sequential Forward Selection (SFS), and Sequential Backwards Selection (SBS). The results have registered diverse classification accuracy ratios for the mentioned method when using random forest classification, where MDI has 98.313% to 98.1% accuracy ranges for the selected features, whilst it achieved 98.504% in the case of using all the features. Moreover, the highest percentage, 98.4% accuracy, was achieved with the correlation method when extracting two features. On the other hand, SFS has an accuracy range from 98.6% to 98.212% after extracting nine features. A satisfactory classification accuracy was maintained by the SBS method, with accuracy from 98.6% to 98.2% after deleting ten

The laboratory organization is essential in the university to maximize system convenience, accuracy, appropriateness, and sustainability. The laboratory data system is an information supplier that aids in arranging data sources and decision-making processes. In this work, unified laboratories management system (ULMS) was presented in the educational field for managing and scheduling lab equipment information. There are two steps in the research process: the first is to examine the old approach for organizing and scheduling laboratory equipment and suggest a better one. The approach adopted in this system is the first come, first served rule governs task priority (FCFS). The second step is creating a computerized system for scheduling and managing laboratory equipment. For programming, C# and structured query language (SQL) are used. This system's design included user and administrative integration (manager, technicians, and researchers). An information application system plan was created because of this research to manage the scheduling information for lab equipment. As a result, it can help the management of the laboratory describe all operational tasks in a way that is considerably simpler and produces outcomes more quickly and accurately.

The research focuses on designing and implementing a system for managing incoming server traffic using the Simple Network Management Protocol (SNMP). The essence of the method is to provide a real-time monitoring system that empowers network managers to oversee all network activities efficiently. The key difference of the developed system lies in using the SNMP protocol to monitor and control the network without needing a third-party tool. The proposed approach addresses administrators' common network challenges, including inefficient bandwidth utilization and complex network resource monitoring. Upon implementation, the system gathered essential network equipment information from the Management Information Base (MIB). The research results indicate that the SNMP-based network management system prevents network devices from reaching their allotted bandwidth limits. The system notably reduces CPU utilization from 28 % to 8 % and decreases bandwidth usage from 1.7 MiB/s to 260 bytes/s. Additionally, the system's alerting capability enhances the network's resilience against incoming traffic anomalies that may otherwise interrupt server operations. An example of using the proposed system to manage incoming server traffic in small networks. It has several practical uses in managing and maintaining network infrastructure in different fields. Also, it enables real-time monitoring of network devices, resource management, fault detection, and performance optimization. The system can be used in small organizations to monitor network performance, track faults, and manage network resources efficiently. In addition, SNMP-based network monitoring systems can find applications in almost any field where networked devices and infrastructure require monitoring, management, and optimization

In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network

Attendance systems have been rated as amongst the critical elements or issues that reflects domain achievements, and their performances have contributed better to organisations, industries, enterprises and universities compared with traditional methods that are time-consuming and inefficient. Different automatic identification technologies have become trends, and extensive research has been conducted and many applications have been produced to maximise technology features. To address issues related to attendance system technologies, including the advantages, schemes and methods and obstacles, we present a structured review of attendance management systems, with high potential for managing, recording and tracking the presence of users in different domains. Additionally, this study introduces a detailed literature survey schema and highly granular model for article categorisation. Out of the 204 identified papers, 90 were found relevant in the context of this systematic literature review. The selected articles are comprehensively reviewed, criticised and assessed in accordance with Kitchenhams guidelines for systematic literature review. Lastly, we highlight the vital and significant direction required for future research.

A variety of internet-based applications are widely used in our animation activities because they provide free and useful services. These applications, such as Instant Messaging, can be run via the web, mobile, or computer-based devices. Therefore, the security and privacy of user data over these apps have been concerned in recent years because of sensitive and confidential information considerations. Consequently, many instant messaging applications, like Viber, have various security and privacy issues that need to be understood and resolved. Viber users reached 800 million, and they increased dramatically due to the efficient services that this app provides. Hence, a loophole in an application’s design may allow illegal access to the app and gain confidential and sensitive data. In this article, we proposed a security approach for Viber to safeguard user confidential data and sensitive information. The proposed approach involves two theoretical solutions: Short message service (SMS) authentication code and the physical hardware number to prevent illegal access to user data. Several scenarios are adopted to assess the proposed approach and achieve security and privacy for the user information.

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