دراسة حاجة الجامعة للأنترنت مع زيادة التفاعل في التعليم الالكتروني في ظل جائحة كورونا
جامعة الكوفة - مركز البحث والتأهيل المعلوماتي - قسم الشبكات
Ahmed Yaseen Mjhool obtained his Bachelor’s degree in Computer Engineering from the University of Technology in Iraq in 2008. He then pursued his Master’s degree in Computer Engineering from the Florida Institute of Technology in the USA, where he graduated with distinction in 2017. While pursuing his Master's degree, he gained industrial experience working with large companies such as GE and Alstom at the Florida-Melbourne site.
Upon completing his Master's degree, he returned to the Information Technology Research and Rehabilitation Center at the University of Kufa to work in research, development, teaching, and training. He also joined the faculties of Engineering, Computer Science, and Math as a teacher within his field of specialization.
Mjhool has actively participated in various domestic and international workshops, courses, and conferences on topics related to his research interests, such as Big Data Analytics, Data Mining, Complex Embedded Systems, Internet of Things, Wireless Sensor Networks, and E-learning Technologies. He can be contacted via phone 07802557544 or email at [email protected]
جامعة الكوفة - مركز البحث والتأهيل المعلوماتي - قسم الشبكات
Nowadays, educational data have been increased rapidly because of the students' online activities provided for both students and staff. University of Kufa (UoK) generates a massive amount of data annually due to the use of e-learning web-based systems, network servers, Windows applications, and Students Information System (SIS). This data wasted as traditional learning management systems not capable of analysis it. As a result, the Big Data concept rises to help education sectors by providing new e-learning methods, allowing to meet individual demands and reach the learners' goals, supporting the students and teacher’s interaction. This paper focuses on proposing design Big Data analysis architecture, based on the Hadoop in the UoK and same case for other Iraqi universities. The impact of this work, help the students learn, emphasizing the need of academic researchers and data specialist for learning Big Data analytics in practice and support e-learning management system analysis and set the first step toward developing data repository and data policy in UoK.
ABSTRACT Nowadays, society has witnessed a paradigm shift in wireless technology, such as smartphones, tablets, GPS, sensors, to mention a few. Yet with the advent of the so-called Internet of Things (IoT), the possibility that these devices are able to communicate with each other becomes true. Many technologies (e.g., Wi-Fi, 4G, Bluetooth, etc.) are incorporated into the IoT framework. These technologies are important insofar as they are the key factors to developing and improving the IoT. This paper explores the IoT literature in terms of the communication technology involved: 4G-LTE-A. In addition, we present an outline of future keys in the current cellular systems for improving the IoT. Furthermore, this work exhibits how the current 4G LTE-A frameworks can contribute to the design of smart cities.
ABSTRACT Parking challenges have been a part of our community for a long time and old-fashioned parking management approaches have come a long way. The problems related with parking are common to all drivers. This paper tries to review globally implemented parking management systems that influence innovative technologies. Moreover, by testing a range of parking management solutions from around the world, this literature aims to test the shift in focus of modern parking management strategies. Finally, the paper does a simulation analysis of a case study for a big number of vehicles that are searching for a parking space. Furthermore, this paper gives a quick review about the most common parking techniques.
ABSTRACT The Internet of Things (IoT) is a very large number of objects, with intensive connections and allows these smart objects to be sensed and controlled remotely across efficient network, which is breathing new capability into anything anywhere in the life. This paper presents an overview of about 4G and 5G with their important features and how will drive IoT in future to build smart cities and smart objects. Scientists are concentrates about the advance implementation of these technologies especially the 5G is the term which is not formally used for any current technology and it is still early to provide an exact definition of 5G. We also present an overview of future keys in cellular system to get better IoT such as D2D, M2M, NOMA and Massive MIMO. This work presents the exact steps in 4G and 5G which important towards IoT and the ability to establish the smart cities. Nowadays 4G LTE-A systems are getting maturity and have been installed, the challenge now is how to improve 4G to get a smooth development from 4G to 5G. Among these steps and challenges are considered as the pieces of the 5G using massive MIMO, new access techniques, millimeter wave (mm Wave) and M2M, D2D communication. The future fifth generation (5G) cellular networks have drawn great attention from scientists and companies around the world. 5G cellular networks should accomplish 1,000 times higher mobile data per geographic area. Plus 10-100 times higher number of connecting devices, with low latency and long battery life.
ABSTRACT Wireless sensor networks (WSN) is an important future technology, in several applications in military, health, environment and industries. Currently the integration of social and sensor is very important by considering the characteristics of social networks in designing wireless sensor networks WSN for improvement such as (number of messages from source to destination, radius of coverage, connectivity, and spreading). This area has not received much attention and few researches focus on the performance evaluation. In this paper we have studied the impact of different mobility and distribution models which is a variable one should define which model is best for the infrastructure given their differences, also study include the exact effect of nodes distribution and analyzed by calculation the number of messages of 12 cases to get a real performance evaluation under different conditions and same routing techniques. This work provides us a greater understanding and clear an idea of the effect of mobility plus distribution.
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
With the enormous evolution of the smartphone, especially with the appearance of the fourth generation (4G) cellular networks, the demand for high-speed data rate, low latency, and video streaming have been increased. This rising demand for network utilization has demonstrated the need for more service improvement. Furthermore, with rising demand and complexity, traditional network management techniques are inadequate, necessitating an autonomous calibration to reduce system parameter usage and processing time. Therefore, real network monitoring and performance analysis should be applied by utilizing various models. Because Downlink Throughput (DL-Throughput) holds significant importance factors for network performance, DL-Throughput prediction can be used to evaluate the quality of cellular networks. Various Machine Learning (ML) models utilized Long-Term Evolution (LTE) data measurements for the prediction process. In this article, the selected ML models Support Vector Regression (SVR), Linear Regression (LR), K Nearest Neighbors (KNN), and Decision Tree Regression (DTR) have been used for forecasting DL-Throughput from three different cellular network operators in an urban area. The parameters with high correlation on throughput and are used as feature selection with ML are the GPS coordinates, RSRP, RSRQ, SINR, and RSSI. The statistical analysis has been utilized to determine the accuracy of the ML models. As a result, the KNN and DTR obtain the best accuracy in the three operators compared with other ML models. For instance, the accuracy for R^2 of DTR is 99%, 93%, and 98% with operator 1 (OPR1), operator 2 (OPR2), and Operator 3 (OPR3), respectively.
الكترونيك
انكليزي
الرسم بالحاسوب
الذكاء الاصطناعي
Computer skills
Information Technology
Information Technology