Siti Z. Z. Abidin

Extraction and classification of unstructured data in WebPages for structured multimedia database via XML

Nowadays, there is a vast amount of information available in the internet. The useful data must be captured and stored for future purposes. One of the major unsolved problems in the information technology (IT) industry is the management of unstructured data. The unstructured data such as multimedia files, documents, spreadsheets, news, emails, memorandums, reports and web pages are difficult to capture and store in the common database storage. The underlying reason is due to the tools and techniques that proved to be so successful in transforming structured data into business intelligence and actionable information, simply do not work when it comes to unstructured data. As a result, new approaches are necessary. Attempts have been undertaken by several researchers to deal with unstructured data, but, so far it is hard to find a tool that can store and retrieve the extracted and classified unstructured data into a structured database system. This paper is to present our research on unstructured data identification, extraction and classification of web pages, which is then transformed into structured format in Extensible Markup Language (XML) document, and later stored into a multimedia database. The contribution of this research is in the approach of capturing the unstructured data and the efficiency of a multimedia database to handle this kind of data. The stored data could give benefits to various communities such as students, lecturers, researchers and IT managers because it can be used for any planning, decision-making, day-today operations, and other future purposes.

Designing interaction protocols using noughts and crosses type games

Interaction management is concerned with the protocols that govern structured interactive activities among multiple users or agents in networked collaborative environments. It is an important aspect of networked software in many application domains such as online meetings, online groupware and online games. However, there is limited support in most programming languages and programming environments for implementing interaction management. High-level features, such as interaction protocols and management policies, are usually hard coded by skilled network programmers, who are often scarce in many applications such as e-learning. In this paper, we present an abstraction of various collaborative applications in the form of the noughts and crosses game and its variations. We examine the needs in these games for programming interaction protocols, and propose a comprehensive collection of program constructs for supporting interaction. We report our efforts for incorporating these new constructs into JACIE (Java-based Authoring language for Collaborative Interactive Environments), an existing scripting language designed to support rapid prototyping and implementation of collaborative applications. We demonstrate, through variations of the noughts and crosses game and an on-line bridge game, the usefulness of these language constructs.

Time-Varying mutation in particle swarm optimization

One of significant improvement for particle swarm optimization (PSO) is through the implementation of metaheuristics hybridization that combines different metaheuristics paradigms. By using metaheuristics hybridization, the weaknesses of one algorithm can be compensated by the strengths of other algorithms. Therefore, researchers have given a lot of interest in hybridizing PSO with mutation concept from genetic algorithm (GA). The reason for incorporating mutation into PSO is to resolve premature convergence problem due to some kind of stagnation by PSO particles. Although PSO is capable to produce fast results, particles stagnation has led the algorithm to suffer from low-optimization precision. Thus, this paper introduces time-varying mutation techniques for resolving the PSO problem. The different time-varying techniques have been tested on some benchmark functions. Results from the empirical experiments have shown that most of the time-varying mutation techniques have significantly improved PSO performances not just to the results accuracy but also to the convergence time.

Multimodal 3-D reconstruction of human anatomical structures using surlens visualization system

In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain’s blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom’s macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens’ functionality as a 3-D Multimodal Visualization System.

Hybridization of Particle Swarm Optimization with adaptive genetic algorithm operators

Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GA) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use adaptive parameterization when applying the GA operators. In this work, adaptively parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that an adaptive approach with position factor is more effective for the proposed PSO hybrids. Compared to single PSO with adaptive inertia weight, all the PSO hybrids with adaptive probability have shown satisfactory performance in generating near-optimal solutions for all tested functions.

Locating abnormalities in brain blood vessels using parallel computing architecture

CT and MRI scans are widely used in medical diagnosis procedures, but they only produce 2-D images. However, the human anatomical structure, the abnormalities, tumors, tissues and organs are in 3-D. 2-D images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require doctors to use their expert experiences in the interpretation of the possible location, size or shape of the abnormalities, even for large datasets of enormous amount of slices. Previously, the concept of reconstructing 2-D images to 3-D was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This paper proposes a volume visualization framework using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft.NET environment for easy interoperability with other emerging revolutionary tools. The framework was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing around 109 MRA datasets. Uniquely, at a reasonably cheaper cost, our framework achieves immediate reconstruction and obvious mappings of the internal features of human brain, reliable enough for instantaneous locations of possible blockages in the brain blood vessels.

Towards automatic column-based data object clustering for multilingual databases

The amount of data in all computer applications is growing tremendously. As a result, the organization of the huge data is crucial. Recently, many researchers consider clustering as one of the important approaches in handling data for wide range of research domains. The examples include Topic Detection and Tracking (TDT), Multilingual Document Clustering, Multilingual News Clustering, Text Clustering and Web Record. Normally, data clustering is time consuming and challenging since they involve heavy programming or scripting. In online news, data clustering analysis is very much needed as the nature of the news across the globe is dynamically changing in every second. The news can come from any web sources in the form of multilingual news. This paper proposes system architecture for an automatic data object clustering in multilingual database for online news, web record and text mining. The architecture provides an overview of a virtual scheme that handles data objects within the database tables as part of the database management system. The proposed technique architecture will provide the platform for quick extraction, data arrangement, data grouping based on pattern similarities. Thus, it will improve query processing performance in multilingual databases without the need to code or script for interface programming. This is the first attempt to apply the data clustering technique prior to data extraction in any database application in the form of semi-structured and structured data (web record).

Challenges and issues on online news management

Recently, the Internet usage spread in all areas of life. Online news is among the popular articles on the Internet, which occupies a large portion of online information. The online news will be viewed almost every second in order to follow the evolution of any desired global events. There are many organizations or political parties employ agents for tracking news by grouping the event. Therefore, news clustering is helpful and worthy for many researchers and online news readers in order to view events from multiple perspectives. Additionally, it can be used in online news summarization, topic detection and tracking for extracting and detecting new events or topics in the news articles. The news extraction can be applied on news articles in the form of monolingual or multilingual. On the other hand, news aggregation is the most important method for scrawling and collecting events based on topics or categorization. This paper investigates the challenges and issues that relate to online news research. The discussions include the overview of system architectures, online news techniques, and a few related computer applications for the above mentioned online news areas.

Introducing residual errors in accuracy assessment for remotely sensed change detection

Accuracy assessment for map comparison is commonly found in urban planning research, especially for detecting error in remotely sensed imagery data. It is to compare two sources of spatial information. In analyzing such information quantitatively, the two datasets are summarized in a confusion matrix, which is represented in a form of percentage of predicted value against its actual data (ground truth). The common acceptable percentage is eighty percent and above. In this paper, we present a new way of accuracy assessment by introducing an additional value called residual error (or predicted error). The residual error is the percentage of error exists when two sources of major errors called mis-classification and mis-location are integrated. Such residual error is incorporated into the assessment so that the results are more accurate and comprehensive. As a case study, we calculate the residual errors of five independent image classifications from six different datasets. Therefore, the accuracy assessment is performed with more details that include not only the confusion matrix, but also the residual errors. In this way, the results of the change detection process can help in doing further analysis for urban growth and land development, particularly for town area.

Application-based context-awareness in collaborative workspaces: A review

In this paper, we investigate the existing research based on context-awareness elements in various collaborative applications. The investigation is based on seven context awareness entities; application, media, method, tool, platform, framework and device. This work focuses on the application which involves eight context elements; domain, activity of user, context object, locations, type of communication, type of context, digital elements and models. In order to define the attributes for each of the context element, various domains of applications are selected that include education, business, mobile, multimedia and virtual reality. Based on the attributes, a context awareness structure in collaborative workspaces is proposed. The structure visualizes a general relationship between the clustered elements in handling context awareness. Thus, the relationship enables context-awareness to be used in a broader perspective of context-aware applications as opposed to the current practice that is limited for specific circumstances.