Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.
Cloud Computing (CC) becames the most promising technology to reach the advanced educational services, because it essentially provides a huge computing and storage capacities. Cloud computing provides reliable and tailored dynamic computing environments for education services. On the other side, e-learning has been realized as an efficient way of learning. The increasing number of students, services, education contents and resource as well as the way of adapting e-learning becomes problematic. As a potential technology to overcome the problems in e-learning, this study explores the potntial impacts and the measure of how the educational services can be benefited by cloud. For that purpose the study attampt to adapt a proposed framework for virtual learning system in an extended cloud computing environment. This framework can be applied everywhere where there is a need for intensive teaching and learning in higher education. The applied case study findings of implementing the proposed framework equate the study expectations, where the students satisfaction significantly increased compared with the existing system
According to the advances in users' service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources. Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload. A load balancing algorithm is developed to serve users' requests to improve the solution of workload problems with an efficient distribution. The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability of CC. Then, the server's availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance. Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and (low resources and large number) of IoT.
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