scholarly journals E-Learning Recommendation System for Big Data Based on Cloud Computing

2021 ◽  
Vol 16 (21) ◽  
pp. 177
Author(s):  
Mounia Rahhali ◽  
Lahcen Oughdir ◽  
Youssef Jedidi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Academic Success ◽  
Recommendation System ◽  
Learning Experience ◽  
Research Work ◽  
Cloud Services ◽  
Data Sets ◽  
E Learning ◽  
Processing Optimization

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.

Cloud Computing for On-Demand Virtual Desktops and Labs

2012 ◽  
pp. 111-125
Author(s):  
Christoph Reich ◽  
Sandra Hübner ◽  
Hendrik Kuijs
Keyword(s):  
Cloud Computing ◽  
Learning Experience ◽  
Cloud Services ◽  
The Internet ◽  
It Infrastructure ◽  
On Demand ◽  
E Learning ◽  
Collaboration Platforms ◽  
Computer Resources ◽  
Support Research

Cloud computing is used to provide users with computer resources on-demand any time over the Internet. At the Hochschule Furtwangen University (HFU) students, lecturers, and researchers can leverage cloud computing to enhance their e-learning experience. This chapter presents how cloud computing provides on-demand virtual desktops for problem solving, on-demand virtual labs for special courses, and on-demand collaboration platforms to support research groups. The focus is how cloud services can be used, how they can be integrated into the existing HFU-IT infrastructure, and how new didactic models could look.

scholarly journals A Personalized Web Based E-Learning Recommendation System To Enhance and User Learning Experience

2020 ◽  
Vol 9 (1) ◽  
pp. 1186-1195
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
User Behavior ◽  
Search Algorithm ◽  
Learning Experience ◽  
Research Work ◽  
Web Based ◽  
E Learning

The key aim of the data mining techniques is to help the user by reducing the effort for exploring the data, recovering the patterns, and implementing applications that help to find the knowledge specific contents, decision making, and predictions. This research work develops a recommendation system by using the merits of data mining algorithms. They are used for designing web-based e-learning recommendation systems. This model aims to understand the user behavior and contents requirements of the learner. This purpose is solved by obtaining the information from the data source and producing the suggestions of suitable content to the learner. The concept of web content mining and web usage mining has been combined together for performing the required work. This technique involves the genetic algorithm and k-means clustering algorithm for designing the presented model. In this work the k-means clustering algorithm has been used to track user behavior and the genetic algorithm has been used as a search algorithm to find the necessary resources in the database. Finally, the presented system is implemented and its performance is measured. The estimated results demonstrate that the presented model enhances the accuracy of recommendations and also speeds up the computations. A related performance calculation has also provided to justify this conclusion. The obtained results demonstrate that this technique is acceptable for new generation application designs

A Study on E-learning and Recommendation System

2020 ◽  
Vol 13 ◽  
Author(s):  
Madhavi Alli ◽  
A Nagesh ◽  
A Govardhan
Keyword(s):  
Education System ◽  
Mass Communication ◽  
Recommendation System ◽  
Learning Experience ◽  
Research Work ◽  
Information Access ◽  
Learning System ◽  
Future Research ◽  
Vital Functions ◽  
E Learning

Introduction: Today’s technology and the internet are proliferating due to which information access is becoming easier for many people, and creating new challenges and opportunities in all fields, especially when working with education. For example, the e-learning education system can personalized in order to acquire knowledge level and learner’s requirements in a learning process. The learning experience, as per the individual learner’s goals, should be adopted. Background: In the current educational environment, e-learning is playing a significant role. For many researchers, it has become one of the most important subjects, as using e-learning whole education system would revolutionize. There are many areas of e-learning in which research work is going on, such as Mass Communication, Information and Technology (IT), Education and Distance Education. Objective: To meet the various needs of the learners such as talents, interests, goals and needs, an e-learning system has to design a personalized learning system by considering various educational experiences. Many methods such as ontologies, clustering, classification and association rules have used along with filtering techniques to enhance the personalization and performance of the learner. Methods: This paper presents a detailed review of literature of previous work that has done in e-learning area especially on recommendation system. Current research works on e-learning to discover the research developments in this discipline have discussed in this work. Conclusion: One of the vital functions of the current e-learning system is creating a personalized resource recommendation system. In this paper, we reviewed some crucial papers on both e-learning and recommendation systems. Future research work of this paper would be designing efficient and precise e-learning and recommendation system to deal with the problem of substantial personalized information resources and further e-Learning plays vital role in preventing virus spread during COVID-19 pandemic.

scholarly journals Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhengyou Xia ◽  
Shengwu Xu ◽  
Ningzhong Liu ◽  
Zhengkang Zhao
Keyword(s):  
Bayesian Model ◽  
Recommendation System ◽  
Research Work ◽  
Joint Probability ◽  
Real Data ◽  
Online News ◽  
City Government ◽  
Data Sets ◽  
News Websites ◽  
News Recommendation

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

Mobile Cloud Gaming and Today's World

2018 ◽  
pp. 282-295
Author(s):  
Hallah Shahid Butt ◽  
Sadaf Jalil ◽  
Sajid Umair ◽  
Safdar Abbas Khan
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Research Work ◽  
Cloud Services ◽  
Software As A Service ◽  
Mobile Cloud ◽  
Infrastructure As A Service ◽  
Platform As A Service ◽  
Research Areas ◽  
Generic Architecture

Mobile cloud computing is the emerging field. Along-with different services being provided by the cloud like Platform as a Service, Infrastructure as a Service, Software as a Service; Game as a Service is new terminology for the cloud services. In this paper, we generally discussed the concept of mobile cloud gaming, the companies that provide the services as GaaS, the generic architecture, and the research work that has been done in this field. Furthermore, we highlighted the research areas in this field.

Novel Taxonomy to Select Fog Products and Challenges Faced in Fog Environments

2021 ◽  
pp. 2558-2571
Author(s):  
Akashdeep Bhardwaj
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
High Speed ◽  
Low Cost ◽  
Fog Computing ◽  
Research Work ◽  
Cloud Services ◽  
Smart Devices ◽  
Viable Solution ◽  
Last Mile

This article describes how the rise of fog computing to improve cloud computing performance and the acceptance of smart devices is slowly but surely changing our future and shaping the computing environment around us. IoT integrated with advances in low cost computing, storage and power, along with high speed networks and big data, supports distributed computing. However, much like cloud computing, which are under constant security attacks and issues, distributed computing also faces similar challenges and security threats. This can be mitigated to a great extent using fog computing, which extends the limits of Cloud services to the last mile edge near to the nodes and networks, thereby increasing the performance and security levels. Fog computing also helps increase the reach and comes across as a viable solution for distributed computing. This article presents a review of the academic literature research work on the Fog Computing. The authors discuss the challenges in Fog environment and propose a new taxonomy.

IoT Based Agriculture as a Cloud and Big Data Service

2020 ◽  
pp. 1499-1521
Author(s):  
Sukhpal Singh Gill ◽  
Inderveer Chana ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Cloud Environment ◽  
Big Data Technologies ◽  
Sensor Networking ◽  
New Applications

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

The Application of Hybrid Recommendation Algorithm in Education Cloud

2014 ◽  
Vol 551 ◽  
pp. 670-674 ◽  
Author(s):  
Gai Zhen Yang
Keyword(s):  
Cloud Computing ◽  
Recommendation System ◽  
Research Work ◽  
Operating Efficiency ◽  
Cloud Platform ◽  
Recommendation Algorithm ◽  
Development Platform ◽  
Recommendation Algorithms ◽  
Technology Applications ◽  
Cloud Development

When we face large amounts of data, how can we find the most suitable educational resources quickly has become a pressing issue. In this paper, on the basic of comparative study on traditional recommendation algorithms, we use the cloud computing to solve the traditional collaborative filtering algorithms suffer from scalability issues, the proposed algorithm is applied to the combination of recommended teaching cloud platform program, the program according to different recommended by demand different recommendation strategies; open source project Hadoop as a cloud development platform of the algorithm; recommendation algorithm, algorithm on top of Hadoop to achieve improved operating efficiency is relatively high, ideal parallel performance, fully proved the cloud platform and recommended algorithm combining the advantages. The research work on the recommendation system and teaching cloud computing technology applications to provide a useful reference.

Sign in / Sign up

Export Citation Format

Share Document