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Domain Experts (domain + expert)

Distribution by Scientific Domains

Information Science and Computing	70%

Selected Abstracts

A Time Model for Time-Varying Visualization

COMPUTER GRAPHICS FORUM, Issue 6 2009
M. Wolter
I.3.6 [Computer Graphics]: Methodology and Techniques; I.6.6 [Simulation and Modelling]: Simulation Output Analysis Abstract The analysis of unsteady phenomena is an important topic for scientific visualization. Several time-dependent visualization techniques exist, as well as solutions for dealing with the enormous size of time-varying data in interactive visualization. Many current visualization toolkits support displaying time-varying data sets. However, for the interactive exploration of time-varying data in scientific visualization, no common time model that describes the temporal properties which occur in the visualization process has been established. In this work, we propose a general time model which classifies the time frames of simulation phenomena and the connections between different time scales in the analysis process. This model is designed for intuitive interaction with time in visualization applications for the domain expert as well as for the developer of visualization tools. We demonstrate the benefits of our model by applying it to two use cases with different temporal properties. [source]

Supporting user-subjective categorization with self-organizing maps and learning vector quantization

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 4 2005
Dina Goren-Bar
Today, most document categorization in organizations is done manually. We save at work hundreds of files and e-mail messages in folders every day. While automatic document categorization has been widely studied, much challenging research still remains to support user-subjective categorization. This study evaluates and compares the application of self-organizing maps (SOMs) and learning vector quantization (LVQ) with automatic document classification, using a set of documents from an organization, in a specific domain, manually classified by a domain expert. After running the SOM and LVQ we requested the user to reclassify documents that were misclassified by the system. Results show that despite the subjective nature of human categorization, automatic document categorization methods correlate well with subjective, personal categorization, and the LVQ method outperforms the SOM. The reclassification process revealed an interesting pattern: About 40% of the documents were classified according to their original categorization, about 35% according to the system's categorization (the users changed the original categorization), and the remainder received a different (new) categorization. Based on these results we conclude that automatic support for subjective categorization is feasible; however, an exact match is probably impossible due to the users' changing categorization behavior. [source]

A CASE-BASED DECISION SUPPORT SYSTEM FOR INDIVIDUAL STRESS DIAGNOSIS USING FUZZY SIMILARITY MATCHING

COMPUTATIONAL INTELLIGENCE, Issue 3 2009
Shahina Begum
Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty-nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process. [source]

DTI in Context: Illustrating Brain Fiber Tracts In Situ

COMPUTER GRAPHICS FORUM, Issue 3 2010
Pjotr Svetachov
Abstract We present an interactive illustrative visualization method inspired by traditional pen-and-ink illustration styles. Specifically, we explore how to provide context around DTI fiber tracts in the form of surfaces of the brain, the skull, or other objects such as tumors. These contextual surfaces are derived from either segmentation data or generated using interactive iso-surface extraction and are rendered with a flexible, slice-based hatching technique, controlled with ambient occlusion. This technique allows us to produce a consistent and frame-coherent appearance with precise control over the lines. In addition, we provide context through cutting planes onto which we render gray matter with stippling. Together, our methods not only facilitate the interactive exploration and illustration of brain fibers within their anatomical context but also allow us to produce high-quality images for print reproduction. We provide evidence for the success of our approach with an informal evaluation with domain experts. [source]

Applying domain knowledge and social information to product analysis and recommendations: an agent-based decision support system

EXPERT SYSTEMS, Issue 3 2004
Wei-Po LeeArticle first published online: 24 JUN 200
Abstract: The advance of Internet and Web technologies has boosted the development of electronic commerce. More and more people have changed their traditional trading behaviors and started to conduct Internet shopping. However, the exponentially increasing product information provided by Internet enterprises causes the problem of information overload, and this inevitably reduces the customer's satisfaction and loyalty. To overcome this problem, in this paper we propose a multi-agent system that is capable of eliciting expert knowledge and of recommending optimal products for individual consumers. The recommendations are based on both product knowledge from domain experts and the customer's preferences from system,consumer interactions. In addition, the system also uses behavior patterns collected from previous consumers to predict what the current consumer may expect. Experiments have been conducted and the results show that our system can give sensible recommendations, and it is able to adapt to the most up-to-date preferences for the customers. [source]

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

EXPERT SYSTEMS, Issue 2 2001
William P. Wagner
The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the ,knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the ,paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM. [source]

A fuzzy preference-ranking model for a quality evaluation of hospital web sites

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2006
R. Ufuk Bilsel
This article presents a quality evaluation model for measuring the performance of hospital Web sites. The model is developed on the basis of a conceptual framework, which consists of seven major e-service quality dimensions, including tangibles, reliability, responsiveness, confidence, empathy, quality of information, and integration of communication issues of Web sites. The dimensions and their associated attributes are first obtained from published articles in the health care and information technology literature and then adapted according to the suggestions of related domain experts. Two multicriteria decision-making methods are used in the evaluation procedure. Determined Web site evaluation dimensions and their relevant attributes are weighted using the Analytic Hierarchy Process (AHP) method. Vagueness in some stages of the evaluation required the incorporation of fuzzy numbers in the assessment process. Both fuzzy and crisp data are then synthesized using the fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) ranking method. The model is applied initially to measure the performance of the Web sites of Turkish hospitals. This study should be of interest to health care and technology practitioners and researchers, as the findings shed light on the further development of performance measurements for hospital Web sites. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1181,1197, 2006. [source]

Cost-sensitive learning and decision making for massachusetts pip claim fraud data

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2004
Stijn Viaene
In this article, we investigate the issue of cost-sensitive classification for a data set of Massachusetts closed personal injury protection (PIP) automobile insurance claims that were previously investigated for suspicion of fraud by domain experts and for which we obtained cost information. After a theoretical exposition on cost-sensitive learning and decision-making methods, we then apply these methods to the claims data at hand to contrast the predictive performance of the documented methods for a selection of decision tree and rule learners. We use standard logistic regression and (smoothed) naive Bayes as benchmarks. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1197,1215, 2004. [source]

From data to knowledge and back again: understanding the limitations of KMS

KNOWLEDGE AND PROCESS MANAGEMENT: THE JOURNAL OF CORPORATE TRANSFORMATION, Issue 3 2003
Tom ButlerArticle first published online: 21 JUL 200
Researchers in the field of information systems (IS) view IT-enabled knowledge management solutions as novel approaches to the stimulation of creativity and innovation in post-industrial organizations; hence, the focus by researchers on the role of information and communication technologies (ICT) in enabling and supporting knowledge work. However, despite some success stories, recent research indicates that the majority of knowledge management systems (KMS) have been unsuccessful. This situation has led some to voice deep-seated concerns about the knowledge management paradigm and its influence on the IS field,particularly the belief that IT can help capture, store and transfer knowledge. This paper's objective is to deepen the IS field's understanding of the limitations and capabilities of knowledge management systems. A case study of an Irish software vendor's experiences in developing KMS using case-based reasoning technologies is undertaken to help achieve this objective. The findings of this study illustrate that: (a) the KMS developed in the organization studied did not meet the claims of their creators, as the applications provided a poor approximation of the ,horizons of understanding' of domain experts whose knowledge these systems purported to capture, store and transfer; (b) the ontological and epistemological perspectives of developers were overtly functionalist in orientation and were insensitive to the socially constructed and institutional nature and context of knowledge. The findings lend weight to the claim that information technology deals with data only, and knowledge management requires social as opposed to technical support, in that appropriate institutional mechanisms, rather that technological solutions, constitute the corporate memory. Copyright © 2003 John Wiley & Sons, Ltd. [source]