A Multi-Agent System for Handling Adaptive E-Services

2010 ◽  
pp. 1346-1351
Author(s):  
Pasquale De Meo ◽  
Giovanni Quattrone ◽  
Giorgio Terracina ◽  
Domenico Ursino
Keyword(s):  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Electronic Service ◽  
E Learning ◽  
Multi Agent ◽  
Software Programs ◽  
Internet Revolution ◽  
Health Applications

An Electronic-Service (E-Service) can be defined as a collection of network-resident software programs that collaborate for supporting users in both accessing and selecting data and services of their interest present in a provider site. Examples of e-services are e-commerce, e-learning, e-government, e-recruitment and e-health applications. E-Services are undoubtely one of the engines presently supporting the Internet Revolution. Indeed, nowadays, a large number and a great variety of providers offer their services also or exclusively via the Internet.

A Multi-Agent System for Handling Adaptive E-Services

2010 ◽  
pp. 518-524
Author(s):  
Pasquale De Meo ◽  
Giovanni Quattrone ◽  
Giorgio Terracina ◽  
Domenico Ursino
Keyword(s):  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Electronic Service ◽  
E Learning ◽  
Multi Agent ◽  
Software Programs ◽  
Internet Revolution ◽  
Health Applications

An Electronic-Service (E-Service) can be defined as a collection of network-resident software programs that collaborate for supporting users in both accessing and selecting data and services of their interest present in a provider site. Examples of e-services are e-commerce, e-learning, e-government, e-recruitment and e-health applications. E-Services are undoubtely one of the engines presently supporting the Internet Revolution. Indeed, nowadays, a large number and a great variety of providers offer their services also or exclusively via the Internet.

Agent-Based Mining of User Profiles for E-Services

2011 ◽  
pp. 23-27
Author(s):  
Pasquale De Meo ◽  
Giovanni Quattrone ◽  
Giorgio Terracina ◽  
Domenico Ursino
Keyword(s):  
The Internet ◽  
User Profiles ◽  
Agent Based ◽  
Electronic Service ◽  
E Learning ◽  
Software Programs ◽  
Internet Revolution

An electronic service (e-service) can be defined as a collection of network-resident software programs that collaborate for supporting users in both accessing and selecting data and services of their interest present in a provider site. Examples of e-services are e-commerce, e-learning, and e-government applications. E-services are undoubtedly one of the engines presently supporting the Internet revolution (Hull, Benedikt, Christophides & Su, 2003). Indeed, nowadays, a large number and a great variety of providers offer their services also or exclusively via the Internet.

scholarly journals Implementasi Sistem Load Balancing Menggunakan Metode Least Time First Byte Dan Multi-Agent System Pada Lingkungan Virtual

2019 ◽  
Vol 4 (2) ◽  
pp. 63-70
Author(s):  
Dyah Ayu Wiranti ◽  
Kurnia Siwi Kinasih ◽  
Shinta Rizki Firdina Sugiono
Keyword(s):  
Information System ◽  
Load Balancing ◽  
Response Time ◽  
Service Provider ◽  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Time Value ◽  
Multi Agent ◽  
Modern Era

In this modern era, the technology is growing rapidly, the Internet is misled. This condition will be related to the service provider or commonly referred to as a server. Increasing the number of clients, the server also has to work heavier so that it often occurs overload. The Load Balancing mechanism uses the Least Time First Byte and Multi Agent system methods. This mechanism allows the server to overcome the number of users who perform service requests so that the load from the server can be resolved. This solution is considered efficient and effective because the request process on the information system will be shared evenly on multiple server back ends. The results of the research that can be proved if using this mechanism the server can work well when the request is from a user or client dating, this method successfully distributes the balancer evenly through the server backend. So the server is no longer experiencing overload. This can be proved when a system that has used the load balancing method with 300 connections generates a throughput of 123.1 KB/s as well as response time value of 4.72 MS and a system that does not use the load balancing method has a throughput of 108.4 KB/s as well as a response time value of 120.3 Ms. Therefore by implementing load balancing the performance of the system can always be improved.

Adaptive E-Learning Environments

2013 ◽  
Vol 11 (3) ◽  
pp. 1-11 ◽  
Author(s):  
Pierpaolo Di Bitonto ◽  
Teresa Roselli ◽  
Veronica Rossano ◽  
Maria Sinatra
Keyword(s):  
Distance Learning ◽  
Learning Environments ◽  
Recommender System ◽  
Adaptive Learning ◽  
Multi Agent System ◽  
Agent System ◽  
Educational Environments ◽  
E Learning ◽  
Multi Agent ◽  
Learning Research

One of the most closely investigated topics in e-learning research has always been the effectiveness of adaptive learning environments. The technological evolutions that have dramatically changed the educational world in the last six decades have allowed ever more advanced and smarter solutions to be proposed. The focus of this paper is to depict the three main dimensions that have driven research in the e-learning field and the evolution of the technological approaches adopted for the purposes of building advanced educational environments for distance learning. Then, the three different approaches adopted by the authors are discussed; these consist of a multi-agent system, an adaptive SCORM compliant package and an e-learning recommender system.

scholarly journals A Multi-Agent System for Data Fusion Techniques Applied to the Internet of Things Enabling Physical Rehabilitation Monitoring

2020 ◽  
Vol 11 (1) ◽  
pp. 331
Author(s):  
Héctor Sánchez San Blas ◽  
André Sales Mendes ◽  
Francisco García Encinas ◽  
Luís Augusto Silva ◽  
Gabriel Villarubia González
Keyword(s):  
Internet Of Things ◽  
Recovery Process ◽  
Feedback System ◽  
Easy Access ◽  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Healthcare Facilities ◽  
Multi Agent ◽  
The Internet Of Things

There are more than 800 million people in the world with chronic diseases. Many of these people do not have easy access to healthcare facilities for recovery. Telerehabilitation seeks to provide a solution to this problem. According to the researchers, the topic has been treated as medical aid, making an exchange between technological issues such as the Internet of Things and virtual reality. The main objective of this work is to design a distributed platform to monitor the patient’s movements and status during rehabilitation exercises. Later, this information can be processed and analyzed remotely by the doctor assigned to the patient. In this way, the doctor can follow the patient’s progress, enhancing the improvement and recovery process. To achieve this, a case study has been made using a PANGEA-based multi-agent system that coordinates different parts of the architecture using ubiquitous computing techniques. In addition, the system uses real-time feedback from the patient. This feedback system makes the patients aware of their errors so that they can improve their performance in later executions. An evaluation was carried out with real patients, achieving promising results.

scholarly journals A Proposal of a Multi-Agent System for Adapting Learning Contents to User Competences, Context and Mobile Device

2013 ◽  
Vol 21 (Special-Issue) ◽  
pp. 18-23 ◽  
Author(s):  
Antonio Garcia-Cabot
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Learning Process ◽  
Multi Agent System ◽  
Agent System ◽  
E Learning ◽  
Multi Agent

Abstract E-learning has been a revolution in recent years in the training field. This, combined with the increased use of the mobile devices has caused the emergence of the m-learning. Hence new problems have appeared in the training field, such as showing correctly some learning contents in a mobile device that has restricted features or taking into account the learner’s context in the learning process, because the learner can be anywhere. Because of this, this paper proposes a new multi-agent system for adapting the learning contents to the learner’s competences, to the learner’s context and to his/her mobile device. The paper also describes in detail the prototype developed for testing the proposed design.

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