Rung-Ching Chen

Web page classification based on a support vector machine using a weighted vote schema

Abstract Traditional information retrieval method use keywords occurring in documents to determine the class of the documents, but usually retrieves unrelated web pages. In order to effectively classify web pages solving the synonymous keyword problem, we propose a web page classification based on support vector machine using a weighted vote schema for various features. The system uses both latent semantic analysis and web page feature selection training and recognition by the SVM model. Latent semantic analysis is used to find the semantic relations between keywords, and between documents. The latent semantic analysis method projects terms and a document into a vector space to find latent information in the document. At the same time, we also extract text features from web page content. Through text features, web pages are classified into a suitable category. These two features are sent to the SVM for training and testing respectively. Based on the output of the SVM, a voting schema is used to determine the category of the web page. Experimental results indicate our method is more effective than traditional methods.

Using Rough Set and Support Vector Machine for Network Intrusion Detection System

The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy.

Merging domain ontologies based on the WordNet system and Fuzzy Formal Concept Analysis techniques

Many different contents and structures exist in constructed ontologies, including those that exist in the same domain. If extant domain ontologies can be used, time and money can be saved. However, domain knowledge changes fast. In addition, the extant domain ontologies may require updates to solve domain problems. The reuse of extant ontologies is an important topic for their application. Thus, the integration of extant domain ontologies is of considerable importance. In this paper, we propose a new method for combining the WordNet and Fuzzy Formal Concept Analysis (FFCA) techniques for merging ontologies with the same domain, called FFCA-Merge. Through the method, two extant ontologies can be converted into a fuzzy ontology. The new fuzzy ontology is more flexible than a general ontology. The experimental results indicate that our method can merge domain ontologies effectively.

Segmenting handwritten Chinese characters based on heuristic merging of stroke bounding boxes and dynamic programming

Abstract In handwritten Chinese characters, characters may be written to touch each other or to overlap with each other, therefore, the segmentation problem is not an easy one. In this paper, we present a novel method which uses strokes to build stroke bounding boxes first. Then, the knowledge-based merging operations are used to merge those stroke bounding boxes and, finally, a dynamic programming method is applied to find the best segmentation boundaries. A series of experiments show that our method is very effective for off-line handwritten Chinese character segmentation.

Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency

Ontology describes data about data and offers a group of glossaries with a definition that encompasses them in their entire. It not only transfers syntax of words but also accurately transfers semantic data between human users and the network. Hence, the usefulness of the semantic web depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the domain ontology. First, we collected domain-related web pages from the Internet. Secondly, we use the HTML tag labels to choose meaningful terms from the web pages. Next, we use these terms to construct the domain ontology by calculating a TF-IDF to find the weight of terms, using a recursive ART network (Adaptive Resonance Theory Network) to cluster terms. Each group of terms will find a candidate keyword for ontology construction. Boolean operations locate individual keywords in a hierarchy. Finally, the system outputs an ontology in a Jena package using an RDF format. The primary experiment indicates that our method is useful for domain ontology creation.

An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks

In sparse wireless sensor networks, a mobile robot is usually exploited to collect the sensing data. Each sensor has a limited transmission range and the mobile robot must get into the coverage of each sensor node to obtain the sensing data. To minimize the energy consumption on the traveling of the mobile robot, it is significant to plan a data collection path with the minimum length to complete the data collection task. In this paper, we observe that this problem can be formulated as traveling salesman problem with neighborhoods, which is known to be NP-hard. To address this problem, we apply the concept of artificial bee colony (ABC) and design an ABC-based path planning algorithm. Simulation results validate the correctness and high efficiency of our proposal.

An Isolation Intrusion Detection System for Hierarchical Wireless Sensor Networks

Normal 0 0 2 false false false MicrosoftInternetExplorer4 A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor environmental conditions, such as battlefield data and personal health information, and some environment limited resources. T o avoid malicious damage is important while information is transmitted in wireless network. Thus, Wireless Intrusion Detection Systems are crucial to safe operation in wireless sensor networks. Wireless networks are subject to very different types of attacks compare to wired networks. In this paper, we propose an isolation table to detect intrusion by hierarchical wireless sensor networks and to estimate the effect of intrusion detection. The primary experiment proves that isolation table intrusion detection can prevent attacks effectively.

RECOGNITION AND DATA EXTRACTION OF FORM DOCUMENTS BASED ON THREE TYPES OF LINE SEGMENTS

Abstract Almost all form documents contain line segments. In this paper, we propose an efficient method to recognize the form document that contains at least one line segment. Our method is based on an efficient representation model of the form. The representation model uses three types of line segments to represent a form. All line segments are normalized and sorted after they were extracted. The normalization and sorting not only solve the form scaling problem but also provide an unified and efficient way of matching between forms. To make the recognition method more robust, a fuzzy matching is used. Using the representation model, when recognizing a skew form, only the line segments and the data fields instead of the whole form image need to be rotated. Experimental results show the effectiveness and the efficiency of the method.

A new method for intrusion detection on hierarchical wireless sensor networks

Wireless Sensor Network (WSN) is a novel technology in wireless field. The main function of this technology is to use sensor nodes to sense important information, just like battlefield data and personal health information, under the limited resources. It is important to avoid malicious damage while information transmits in wireless network. So Wireless Intrusion Detection System (WIDS) becomes one of important topics in wireless sensor networks. The attack behavior of wireless sensor nodes is different to wired attackers. In this paper, we will propose an isolation table to detect intrusion in hierarchical wireless sensor networks and to estimate the effect of intrusion detection effectively. The primary experiment proves the isolation table intrusion detection can prevent attacks effectively.

A ROI image retrieval method based on CVAAO

A novel image feature called color variances among adjacent objects (CVAAO) is proposed in this study. Characterizing the color variances between contiguous objects in an image, CVAAO can effectively describe the principal colors and texture distribution of the image and is insensitive to distortion and scale variations of images. Based on CVAAO, a CVAAO-based image retrieval method is constructed. When given a full image, the CVAAO-based image retrieval method delivers the database images most similar to the full image to the user. This paper also presents a CVAAO-based ROI image retrieval method. When given a clip, the CVAAO-based ROI image retrieval method submits to the user a database image containing a target region most similar to the clip. The experimental results show that the CVAAO-based ROI image retrieval method can offer impressive results in finding out the database images that meet user requirements.