Lisa Fan

An enhanced support vector machine model for intrusion detection

Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate

Web-Based Learning Support System

Web-based learning support system offers many benefits over traditional learning environments and has become very popular. The Web is a powerful environment for distributing information and delivering knowledge to an increasingly wide and diverse audience. Typical Web-based learning environments, such as Web-CT, Blackboard, include course content delivery tools, quiz modules, grade reporting systems, assignment submission components, etc. They are powerful integrated learning management systems (LMS) that support a number of activities performed by teachers and students during the learning process [1]. However, students who study a course on the Internet tend to be more heterogeneously distributed than those found in a traditional classroom situation. In order to achieve optimal efficiency in a learning process, an individual learner needs his or her own personalized assistance. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. This chapter demonstrates how to realize personalized learning support in dynamic and heterogeneous learning environments by utilizing Adaptive Web technologies. It focuses on course personalization in terms of contents and teaching materials that is according to each student’s needs and capabilities. An example of using Rough Set to analyze student personal information to assist students with effective learning and predict student performance is presented.

A web personalization system based on users’ interested domains

The information on the Web is growing dramatically. Without a help system, the users may spend lots of time on the Web finding the information they are interested in. Today, many Web recommendation systems cannot give users enough personalized help but provide the user with lots of irrelevant information. In this paper, we present a Web personalization system, which can find out userspsila potential interested domains and recommend the Web pages relevant to the interests of these Web users. This system will also helps users to get the relevant Web pages based on their selection from the domain list. Thus, users can obtain a set of interested domains and the Web pages from the system.

Blog-based online social relationship extraction

With the number of participants in online social networks increasing dramatically, an intelligent and efficient social relationship management system is necessary for online users. The traditional linear social entity list representation would become problematic while the network grows larger. In this paper, we present a system - visoLink which is a social network based on “friends ranking” by using our proposed user interest similarity measure. A unique approach combining content analysis and usage analysis for user interest mining has been proposed. It measures users interests based on both users writing and reading interests. This similarity measure between online actors provides the fundamental support for personal social network visualization and the personalized recommendation.

Reports the current weather conditions on cell phones using web services

Mobile technology applications have proven more interesting and more capable with each passing year, and continue to be a technology with new surprises. In this paper, we present a software development for weather report which is motivated as a cell phone programme application for web service. Users can get the latest weather information without accessing to the specific weather website. In addition, weather information can be deployed on any cell phone with internet explorer. Web Services, MIDlet programming is the technology applied in this paper. Weather report is designed as a two-tier structure, web application server and client. Web services are generated and published on the web. On client side, user, on the MIDP platform, can interact with web services with the agreement on SOAP protocol. In this paper, how to deploy web services, and the differences and constraints of MIDlet programming are presented.

Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection

With the explosion of available data mining algorithms, a method for helping user to select the most appropriate algorithm or combination of algorithms to solve a given problem and reducing users’ cognitive overload due to the overloaded data mining algorithms is becoming increasingly important. In this article, we have presented a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The article discusses the meta-learning method in details and presents some empirical results that show the improvement we can achieve with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the dataset can be found. Thus, we can speed up the ranking process and increase the accuracy by using the reduct of the properties of the dataset. With the reduced searching space, users’ cognitive load is reduced.

A User-Driven Ontology Guided Image Retrieval Model

The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has an exact goal in mind in searching a set of images whereas users normally do not know what they want, or the user faces with a repository of images whose domain is less known and content is semantically complicated. In these cases it is difficult to decide what keywords to use for the query. In this paper, we propose a user-centered image retrieval method that is based on the current Web, keyword-based annotation structure, and combining Ontology guided knowledge representation and probabilistic ranking. A Web application for image retrieval using the proposed approach has been implemented. The model provides a recommendation subsystem to support and assist the user modifying the queries and reduces the users cognitive load with the searching space. Experimental results show that the image retrieval recall and precision rates increased and therefore demonstrates the effectiveness of the model.

Granular Computing Application to Web-based Learning Support System

This paper presents a potential application of granular computing approach to the Web-based learning support system (WLSS). In particular, we examine the main functions and characteristics of such systems and the granulation aspects associated with the systems. Issues of contents granulation, domain knowledge granulation, interface granulation, and student modeling granulation are discussed in detail. We report our experience in the design and implementation of a prototype of granular computing approach to a Web-based learning support system.