Applying Classification Techniques in E-Learning System: An Overview

The aim of this paper is to provide an overview of application of data mining methods in e-learning process. E-learning is concerned with web-based learning which is totally depending upon internet. Use of data mining algorithms can help to discover the relevant information from database obtained from web based education system. This paper focused on e-learning problems to which data mining techniques have been applied, including: student’s classification based on their learning performance, detection of irregular learning behavior of students. This paper shows types of various modeling techniques used which includes: neural network, fuzzy logic, graph and trees, association rules and multi agent systems

Using Food Web as an evolution computing model for Internet-based multimedia agents

[[abstract]]The ecosystem is an evolutionary result of natural laws. Food Web (or Food Chain) embeds a set of computation rules of natural balance. Based one the concepts of Food Web, one of the laws that we may learn from the natural besides neural networks and genetic algorithms, we propose a theoretical computation model for mobile agent evolution on the Internet. We define an agent niche overlap graph and agent evolution states. We also propose a set of algorithms, which is used in our multimedia search programs, to simulate agent evolution. Agents are cloned to live on a remote host station based on three different strategies: the brute force strategy, the semi-brute force strategy, and the selective strategy. Evaluations of different strategies are discussed. Guidelines of writing mobile agent programs are proposed. The technique can be used in distributed information retrieval which allows the computation load to be added to servers, but significantly reduces the traffic of network communication.[[conferencedate]]19990607~19990611[[conferencelocation]]Florence, Ital

Biomedic Organizations: An intelligent dynamic architecture for KDD

The application of information technology in the field of biomedicine has become increasingly important over the last several years. This study presents the Intelligent Biomedic Organizations (IBOs) model, an intelligent dynamic architecture for knowledge discovery in biomedical databases. It involves an organizational model specially designed to support medical personnel in their daily tasks and to establish an innovative intelligent system to make classifications and predictions with huge volumes of information. IBO is based on a multi-agent architecture with Web service integration capability. The core of the system is a type of agent that integrates a novel strategy based on a case-based planning mechanism for automatic reorganization. This agent proposes a new reasoning agent model, where the complex processes are modeled as external services. In this sense, the agents act as coordinators of Web services that implement the four stages of the case-based planning cycle. The multi-agent system has been implemented in a real scenario to classify leukemia patients, and the classification strategy includes services such as a novel ESOINN neural network and statistical methods to analyze patient data. The results obtained are presented within this paper and demonstrate the effectiveness of the proposed organizational model

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

A multimodal restaurant finder for semantic web

Multimodal dialogue systems provide multiple modalities in the form of speech, mouse clicking, drawing or touch that can enhance human-computer interaction. However, one of the drawbacks of the existing multimodal systems is that they are highly domain-specific and they do not allow information to be shared across different providers. In this paper, we propose a semantic multimodal system, called Semantic Restaurant Finder, for the Semantic Web in which the restaurant information in different city/country/language are constructed as ontologies to allow the information to be sharable. From the Semantic Restaurant Finder, users can make use of the semantic restaurant knowledge distributed from different locations on the Internet to find the desired restaurants