Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

Hessen, Shrouk H; Abdul-Kader, Hatem; Khedr, Ayman E.; Salem, Rashed

doi:10.1155/2022/2941840

Cited by 14 publications

(12 citation statements)

References 29 publications

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“…These features play an important action to propose a multi-agent framework based on load balancing to enhance the cloud environment. The Random Forest algorithm outperforms other machine learning algorithms [37]. Fig.…”

Section: Proposed Load Balancing Algorithmmentioning

confidence: 94%

“…2 illustrates the flowchart of the load balancing agent. The feature selection method is used in the classification stage based on machine learning techniques [37] before the main phase of proposing a load-balancing algorithm for the cloud environment. More results will be illustrated in the result section for more clarification of the load-balancing algorithm.…”

Section: The Main Phase For Proposing a Load Balancing Algorithmmentioning

confidence: 99%

“…It contains three main modules: activity, presentation, and demographics which include data about students and their activities in Virtual Learning Environment (VLE). Different machine learning algorithms are used: Decision Trees, Random Forest, Linear Regression, Naïve Bayes, and K-Nearest Neighbor, which are applied to select the high-ranked and relevant features [37]. Extra Trees Classifier feature selection methods are used to select more relevant features of student data.…”

Section: Proposed Load Balancing Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Hessen¹,

Abdul-Kader²,

Khedr³

et al. 2023

Computers, Materials &Amp; Continua

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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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Section: Proposed Load Balancing Algorithmmentioning

confidence: 94%

Section: The Main Phase For Proposing a Load Balancing Algorithmmentioning

confidence: 99%

Section: Proposed Load Balancing Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Hessen¹,

Abdul-Kader²,

Khedr³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A feature selection algorithm (4) proposes a new feature subset based on the combination of search techniques and evaluation measure depending on the feature. This algorithm is used to minimize the error rate.…”

Section: Feature Selection Algorithmmentioning

confidence: 99%

Feature Selection Techniques in Learning Algorithms to Predict Truthful Data

Usha¹,

Anuradha²

2023

IJST

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Objectives: This review focuses on various feature selection process, strategy, and methods such as filter, wrapper and embedded algorithms and its advantages and disadvantages are presented. Methods: The algorithms such as Mutual Information Gain (MIG), Chi-Square (CS) and Recursive Feature Elimination (RFE) are used to select features. In this review, two benchmark datasets: Breast cancer and Diabetes are used. Findings: To improve the efficiency, selection of appropriate feature selection methods and algorithms are most important. To measure the performance of these selected features Random Forest model used as classifiers and compared with Support Vector Machine and Decision Tree models. Filter method and algorithm selects up to 15 features out of 17 for diabetes dataset with 89 % to 98 % of accuracy. For breast cancer dataset, up to 28 features out of 31 features selected with 98.5 % of accuracy. Wrapper method RFE selects 14 features from 17 for diabetes and 10 out of 31 features selected for breast cancer. This RFE method shows up to 98.25 % of accuracy for diabetes and 99.20% of accuracy for breast cancer. Novelty: Feature selection techniques help to improve the performance, efficiency and decrease the storage and processing time and build a better model for further process in prediction. The proper feature selection helps to diagnose diseases at an earlier stage and improve the survival of human beings.

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“…It has become essential for the educational environment to improve learning systems, particularly e-learning systems. Adaptable strategies to meet student needs have emerged due to ongoing developments in the e-learning process [1]. Organizations and educators have noted some difficulties with elearning.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of Student Performance Prediction Techniques in Virtual Learning Environment

Thilagavathi,

Anuratha,

Elamparithi

2023

ijmst

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The virtual learning environment (VLE) is essential today and widely used globally for information exchange. Compared to in-person lectures, a VLE aids distant learning, although it might be challenging to maintain constant student interest. Academic activities are not actively pursued by students, which has an impact on their learning curves. The primary goal of this review is to impart a thorough knowledge and comprehension of various techniques, including machine learning (ML) and deep learning (DL), which are utilized for predicting student progress and performance and, consequently, how these prediction techniques help to find the most crucial student attribute for prediction. Additionally, this analysis reveals a rising trend in the volume and diversity of this field’s research. At the same time, the assessment revealed several problems with research quality that highlight the need for the community to strengthen efforts to validate and replicate work and to describe methods and outcomes in greater detail. It can help teachers, parents, students, and tutors decide on the appropriate learning support for their charges when taking online courses.

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Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

Cited by 14 publications

References 29 publications

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

Feature Selection Techniques in Learning Algorithms to Predict Truthful Data

A Systematic Literature Review of Student Performance Prediction Techniques in Virtual Learning Environment

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