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Showing papers in "Expert Systems With Applications in 2003"


Journal ArticleDOI
Seewon Ryu, Seung Hee Ho1, Ingoo Han1
TL;DR: Choi et al. as discussed by the authors investigated the factors affecting physician's knowledge sharing behavior within a hospital department by employing existing theories, such as the theory of reasoned action (TRA) and planned behavior (TPB), and found that the TPB model appeared to be superior to TRA in explaining physicians' intention to share knowledge.
Abstract: Recently, there has been much interest for knowledge sharing within professional group, especially physicians in hospital. This study investigates the factors affecting physician's knowledge sharing behavior within a hospital department by employing existing theories. The research models under investigation are the theory of reasoned action (TRA) and the theory of planned behavior (TPB). These models are empirically examined and compared based on the survey results on physicians' knowledge sharing behavior collected from 286 physicians practicing in 28 types of subunits in 13 tertiary hospitals in Korea. The TPB model exhibited good fit with the data and appeared to be superior to the TRA in explaining physicians' intention to share knowledge. In the modified TPB model, subjective norms were found to have the strongest total effects on behavioral intentions to share knowledge of physicians through direct and indirect path by attitude. Attitude was found to be the second important factor influencing physicians' intentions. Perceived behavioral control was also found to affect the intention to share knowledge, though in a lesser degree than subjective norms or attitudes. Implications are also discussed for physician's knowledge sharing activities.

535 citations


Journal ArticleDOI
TL;DR: This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period.
Abstract: This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works.

523 citations


Journal ArticleDOI
TL;DR: This research envisions a knowledge management-enabled health care management system that would help integrate clinical, administrative, and financial processes in health care through a common technical architecture; and provides a decision support infrastructure for clinical and administrative decision-making.
Abstract: The health care industry is increasingly becoming a knowledge-based community that is connected to hospitals, clinics, pharmacies, and customers for sharing knowledge, reducing administrative costs and improving the quality of care. Thus, the success of health care depends critically on the collection, analysis and seamless exchange of clinical, billing, and utilization information or knowledge within and across the above organizational boundaries. This research envisions a knowledge management-enabled health care management system that would help integrate clinical, administrative, and financial processes in health care through a common technical architecture; and provides a decision support infrastructure for clinical and administrative decision-making. Hence, the objective of this research is to present and describe the knowledge management capabilities, the technical infrastructure, and the decision support architecture for such a health care management system. The research findings would immensely help the health care information technology (IT) managers and knowledge based system developers to identify their IT needs and to plan for and develop the technical infrastructure of the health care management system for their organizations.

274 citations


Journal ArticleDOI
TL;DR: From the computational results, NNs have emerged as a computational tool that is well-matched to the problem of credit classification, and creditors can suggest the conditional acceptance, and further explain the conditions to rejected applicants.
Abstract: The credit industry is concerned with many problems of interest to the computation community. This study presents a work involving two interesting credit analysis problems and resolves them by applying two techniques, neural networks (NNs) and genetic algorithms (GAs), within the field of evolutionary computation. The first problem is constructing NN-based credit scoring model, which classifies applicants as accepted (good) or rejected (bad) credits. The second one is better understanding the rejected credits, and trying to reassign them to the preferable accepted class by using the GA-based inverse classification technique. Each of these problems influences on the decisions relating to the credit admission evaluation, which significantly affects risk and profitability of creditors. From the computational results, NNs have emerged as a computational tool that is well-matched to the problem of credit classification. Using the GA-based inverse classification, creditors can suggest the conditional acceptance, and further explain the conditions to rejected applicants. In addition, applicants can evaluate the option of minimum modifications to their attributes.

243 citations


Journal ArticleDOI
TL;DR: The framework presented in this paper was developed from an analysis of environmental management practices in a number of companies along with a thorough literature survey and how the most important parts of the framework were computerised using KBS techniques.
Abstract: This paper outlines the development of a knowledge-based system (KBS) which integrates environmental factors into the supplier selection process. The system employs both case-based reasoning (CBR) and decision support components including multi-attribute analysis (MAA). Traditionally, when evaluating supplier performance, companies have considered factors such as price, quality, flexibility etc. However, with environmental pressures increasing, many companies have begun to consider environmental issues and the measurement of their suppliers' environmental performance. The framework presented in this paper was developed from an analysis of environmental management practices in a number of companies along with a thorough literature survey. The paper then outlines how the most important parts of the framework were computerised using KBS techniques. An evaluation of the system implemented in a multinational company is presented along with proposals for future system enhancements.

205 citations


Journal ArticleDOI
TL;DR: A new concept named “way of achievement” is proposed as a key concept for capturing functional knowledge representing achievement relations among functions and development of a design supporting system using the systematized knowledge, called a functional way server.
Abstract: In conceptual design of engineering devices, a designer decomposes a required function into sub-functions, so-called functional decomposition, using a kind of functional knowledge representing achievement relations among functions. However, such knowledge about functionality of engineering devices is usually left implicit because each designer possesses it. Even if such knowledge is found in documents, it is often scattered around technical domains and lacks consistency. Aiming at capturing such functional knowledge explicitly and sharing it in design teams, we discuss its systematic description based on functional ontologies which provide common concepts for its consistent and generic description. We propose a new concept named “way of achievement” as a key concept for capturing such functional knowledge. Categorization of typical representations of the knowledge and its organization as is-a hierarchies are also discussed. The generic concepts representing functionality of a device in the functional knowledge are provided by the functional concept ontology, which makes the functional knowledge consistent and applicable to other domains. We also discuss development of a design supporting system using the systematized knowledge, called a functional way server. It helps human designers redesign an existing engineering device by providing a wide range of alternative ways of achievement of the required function in a manner suitable for the viewpoint of each designer and then facilitates innovative design.

178 citations


Journal ArticleDOI
TL;DR: This work proposed an effective method, a fuzzy rough set system to predict a stock price at any given time, and used it to predict the stronger rules of stock price and achieved at least 93% accuracy after 180 trials.
Abstract: In this study of mining stock price data, we attempt to predict the stronger rules of stock prices. To address this problem, we proposed an effective method, a fuzzy rough set system to predict a stock price at any given time. Our system has two agents: one is a visual display agent that helps stock dealers monitor the current price of a stock and the other is a mining agent that helps stock dealers make decisions about when to buy or sell stocks. To demonstrate that our system is effective, we used it to predict the stronger rules of stock price and achieved at least 93% accuracy after 180 trials.

173 citations


Journal ArticleDOI
TL;DR: This work develops two efficient algorithms for mining time-interval sequential patterns, based on the conventional Apriori algorithm and the PrefixSpan algorithm, which outperforms the former not only in computing time but also in scalability with respect to various parameters.
Abstract: Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, in an important data-mining problem with broad applications. Although conventional sequential patterns can reveal the order of items, the time between items is not determined; that is, a sequential pattern does not include time intervals between successive items. Accordingly, this work addresses sequential patterns that include time intervals, called time-interval sequential patterns. This work develops two efficient algorithms for mining time-interval sequential patterns. The first algorithm is based on the conventional Apriori algorithm, while the second one is based on the PrefixSpan algorithm. The latter algorithm outperforms the former, not only in computing time but also in scalability with respect to various parameters.

168 citations


Journal ArticleDOI
Myoung-Jong Kim1, Ingoo Han1
TL;DR: A genetic algorithm-based data mining method for discovering bankruptcy decision rules from experts' qualitative decisions is proposed and shows that the genetic algorithm generates the rules which have the higher accuracy and larger coverage than inductive learning methods and neural networks.
Abstract: Numerous studies on bankruptcy prediction have widely applied data mining techniques to finding out the useful knowledge automatically from financial databases, while few studies have proposed qualitative data mining approaches capable of eliciting and representing experts' problem-solving knowledge from experts' qualitative decisions. In an actual risk assessment process, the discovery of bankruptcy prediction knowledge from experts is still regarded as an important task because experts' predictions depend on their subjectivity. This paper proposes a genetic algorithm-based data mining method for discovering bankruptcy decision rules from experts' qualitative decisions. The results of the experiment show that the genetic algorithm generates the rules which have the higher accuracy and larger coverage than inductive learning methods and neural networks. They also indicate that considerable agreement is achieved between the GA method and experts' problem-solving knowledge. This means that the proposed method is a suitable tool for eliciting and representing experts' decision rules and thus it provides effective decision supports for solving bankruptcy prediction problems.

165 citations


Journal ArticleDOI
TL;DR: By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order, as well as the delay in delivery of goods of suppliers after order allocation, are greatly reduced.
Abstract: The design of supplier relationship management to facilitate supplier selection using an integrative case based supplier selection and help desk approach to select the most appropriate suppliers together with their past performance records from a case base warehouse has become a promising solution for manufacturers to identify preferred suppliers and trading partners to form a supply network on which they depend for products, services and distribution. In this paper, an intelligent supplier relationship management system (ISRMS) integrating a company's customer relationship management system, supplier rating system and product coding system by the case based reasoning technique to select preferred suppliers during new product development process is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order, as well as the delay in delivery of goods of suppliers after order allocation, are greatly reduced. In addition, performance of suppliers can be monitored effectively.

164 citations


Journal ArticleDOI
TL;DR: This model combines a CF algorithm with two machine learning processes, Self-Organizing Map and Case Based Reasoning, by changing an unsupervized clustering problem into a supervized user preference reasoning problem, which is a novel approach for the CF recommendation field.
Abstract: Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model, which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, Self-Organizing Map (SOM) and Case Based Reasoning (CBR) by changing an unsupervized clustering problem into a supervized user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

Journal ArticleDOI
TL;DR: An intelligent supplier relationship management system (ISRMS) using hybrid case based reasoning and artificial neural networks (ANNs) techniques to select and benchmark potential suppliers is discussed, and the outsource cycle time from searching for potential suppliers to the allocation of order is greatly reduced.
Abstract: In today's accelerating world economy, the drive to continually cut costs and focus on core competencies has driven many to outsource some or all of their production. In this environment, improving supply chain execution and leveraging the supply base through effective supplier relationship management (SRM) has become more critical than ever in achieving competitive advantage. It was found that the use of artificial intelligence in the outsourcing function of SRM to identify appropriate suppliers to form a supply network has become a promising solution on which manufacturers depend for products, services and distribution. In this paper, an intelligent supplier relationship management system (ISRMS) using hybrid case based reasoning (CBR) and artificial neural networks (ANNs) techniques to select and benchmark potential suppliers is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, the outsource cycle time from searching for potential suppliers to the allocation of order is greatly reduced.

Journal ArticleDOI
TL;DR: This paper presents a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities called MALCR, which is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering.
Abstract: Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailor-made campaigns targeting users according to where they are, their needs of the moment and the devices they are using (ie contextualized mobile advertising) Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users' inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users' interests in the values of features of advertisements; (3) having understood the users' interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising

Journal ArticleDOI
TL;DR: A knowledge strategy planning methodology, called P2-KSP methodology, which places its emphasis on improving organizational performance by identifying and leveraging knowledge directly related to business processes and performance.
Abstract: This study aims at suggesting an integrative methodology for planning knowledge management initiatives. First, four major underpinning assumptions which should be addressed in knowledge management are identified through literature reviews on strategic information systems planning and knowledge management. Based on these assumptions, we introduce a knowledge strategy planning methodology, called P2-KSP methodology. The P2-KSP methodology places its emphasis on improving organizational performance by identifying and leveraging knowledge directly related to business processes and performance. The methodology consists of five phases: business environment analysis, knowledge requirements analysis, knowledge management strategy establishment, knowledge management architecture design, and knowledge management implementation planning. After its detailed procedures and related features are explained, results of applying it to a large semiconductor manufacturer's knowledge management project are discussed.

Journal ArticleDOI
TL;DR: A system developed for the region of Galicia in NW Spain, one of the regions of Europe most affected by fires, that acts as a preventive tool by predicting forest fire risks, backs up the forest fire monitoring and extinction phase, and assists in planning the recuperation of the burned areas.
Abstract: Over the last two decades in southern Europe, more than 10 million hectares of forest have been damaged by fire. Due to the costs and complications of fire-fighting a number of technical developments in the field have been appeared in recent years. This paper describes a system developed for the region of Galicia in NW Spain, one of the regions of Europe most affected by fires. This system fulfills three main aims: it acts as a preventive tool by predicting forest fire risks, it backs up the forest fire monitoring and extinction phase, and it assists in planning the recuperation of the burned areas. The forest fire prediction model is based on a neural network whose output is classified into four symbolic risk categories, obtaining an accuracy of 0.789. The other two main tasks are carried out by a knowledge-based system developed following the CommonKADS methodology. Currently we are working on the trail of the system in a controlled real environment. This will provide results on real behaviour that can be used to fine-tune the system to the point where it is considered suitable for installation in a real application environment.

Journal ArticleDOI
TL;DR: The design of some major functions used in the classifier is different from the existing ones, including how to select the next splitting attribute, when to stop the splitting of a node, how to determine a node's labels, and how to predict the labels of a new data.
Abstract: Most decision tree classifiers are designed to classify the objects whose attributes and class labels are single values. However, many practical classification problems need to deal with multi-valued and multi-labeled data. For example, a customer data in a tour company may have multi-valued attributes such as the cars, the hobbies and the houses of the customer and multiple labels corresponding to the tours joined before. If the company intends to use customers' data to build a classifier to predict what kinds of customers are likely to participate in what kinds of tours; then a requirement arises immediately is how to design a new classification algorithm to classify the multi-valued and multi-labeled data. Therefore, this research has engaged in developing such a new classifier. We found that the design of some major functions used in our classifier is different from the existing ones, including how to select the next splitting attribute, when to stop the splitting of a node, how to determine a node's labels, and how to predict the labels of a new data. In this paper, all these issues are addressed and the problems are solved. The simulation result shows that the proposed algorithm performs well both in computing time and in accuracy.

Journal ArticleDOI
TL;DR: The fuzzy expert system approach is proposed for the classification of different types of welding flaws and outperforms all others in terms of classification accuracy.
Abstract: The fuzzy expert system approach is proposed for the classification of different types of welding flaws. The fuzzy rules are generated from available examples using two different methods. The classification accuracy of fuzzy expert systems using fuzzy rules generated by the two methods is evaluated and compared. In addition, the fuzzy expert system approach is also compared with two other approaches: the fuzzy k-nearest neighbors algorithm and multi-layer perceptron neural networks, based on the bootstrap method. The results indicate that the fuzzy expert system approach outperforms all others in terms of classification accuracy.

Journal ArticleDOI
TL;DR: An Ontology-based Fuzzy Event Extraction (OFEE) agent for Chinese e- news summarization that can summarize the Chinese weather e-news effectively is proposed and by the simulation, the proposed method can summarized the ChineseWeather e-News effectively.
Abstract: An Ontology-based Fuzzy Event Extraction (OFEE) agent for Chinese e-news summarization is proposed in this article. The OFEE agent contains Retrieval Agent (RA), Document Processing Agent (DPA) and Fuzzy Inference Agent (FIA) to perform the event extraction for Chinese e-news summarization. First, RA automatically retrieves Internet e-news periodically, stores them into the e-news repository, and sends them to DPA for document processing. Then, the DPA will utilize the Chinese Part-of-speech (POS) tagger provided by Chinese knowledge information processing group to process the retrieved e-news and filter the Chinese term set by Chinese term filter. Next, the FIA and Event Ontology Filter (EOF) extract the e-news event ontology based on the Chinese term set and domain ontology. Finally, the Summarization Agent (SA) will summarize the e-news by the extracted-event ontology. By the simulation, the proposed method can summarize the Chinese weather e-news effectively.

Journal ArticleDOI
TL;DR: An efficient algorithm, called Goal-oriented sequential pattern, is proposed, which can provide enterprises warning signs soon before they are losing valuable customers and give them reference for decision making.
Abstract: Discovering sequential patterns is one of the most important task in data mining. In this paper we propose an efficient algorithm, called Goal-oriented sequential pattern. It can provide enterprises warning signs soon before they are losing valuable customers and give them reference for decision making. Experiments comparing Apriori showed that Goal-oriented is more efficient, and performs reasonably well for the rules.

Journal ArticleDOI
TL;DR: The results of the empirical experiment and the simulation results show that the effectiveness of intrusion detection can be enhanced by considering the asymmetric costs of false negative and false positive errors.
Abstract: This paper investigates the asymmetric costs of false positive and negative errors to enhance the IDS performance. The proposed method utilizes the neural network model to consider the cost ratio of false negative errors to false positive errors. Compared with false positive errors, false negative errors incur a greater loss to organizations which are connected to the systems by networks. This method is designed to accomplish both security and system performance objectives. The results of our empirical experiment show that the neural network model provides high accuracy in intrusion detection. In addition, the simulation results show that the effectiveness of intrusion detection can be enhanced by considering the asymmetric costs of false negative and false positive errors.

Journal ArticleDOI
TL;DR: The OVER Project was a collaboration between West Midlands Police, UK, the Centre for Adaptive Systems, and Psychology Division, from the University of Sunderland, and some of the design decisions were based upon the forensic psychology and criminology literature, including the graphical representation of geographic data and presentation of results of analyses.
Abstract: The OVER Project was a collaboration between West Midlands Police, UK, the Centre for Adaptive Systems, and Psychology Division, from the University of Sunderland. The Project was developed primarily to assist the Police with the high volume crime, burglary from dwelling houses. A developed software system enables the trending of historical data, the testing of ‘short term’ hunches, and the development of ‘medium’ and long term’ strategies to burglary and crime reduction, based upon victim, offender, location and details of victimisations. The software utilises mapping and visualisation tools and is capable of a range of sophisticated predictions, tying together statistical techniques with theories from forensic psychology and criminology. The statistical methods employed (including multi-dimensional scaling, binary logistic regression) and ‘data-mining’ technologies (including neural networks) are used to investigate the impact of the types of evidence available and to determine the causality in this domain. The final predictions on the likelihood of burglary are calculated by combining all of the varying sources of evidence into a Bayesian belief network. This network is embedded in the developed software system, which also performs data cleansing and data transformation for presentation to the developed algorithms. It is important that derived statistics from the software and predictions are interpretable by the intended users of the decision support system, namely Police sector managers, and this paper includes some of the design decisions based upon the forensic psychology and criminology literature, including the graphical representation of geographic data and presentation of results of analyses.

Journal ArticleDOI
TL;DR: A multi-perspective knowledge-based system (MPKBS) is proposed for CSM which incorporates various artificial intelligence technologies such as case-based reasoning (CBR) and adaptive time-series model which are used for decision analysis, performance measurement and monitoring.
Abstract: The e-business arena is a dynamic, complex and demanding environment. It is essential to make optimal reuse of knowledge of customer services across various functional units of the enterprise. On the other hand, it is also important to ensure that the customer service staff can access and be trained up with dynamically updated knowledge that meets the changing business environment of an enterprise in customer services. However, conventional way of customer service management (CSM) is inadequate to achieve the multi-perspective of an enterprise for achieving knowledge acquisition, knowledge diffusion, business automation and business performance measurement so as to drive the continuous improvement of the customer service quality. In this paper, a multi-perspective knowledge-based system (MPKBS) is proposed for CSM. The MPKBS incorporates various artificial intelligence technologies such as case-based reasoning (CBR) and adaptive time-series model which are used for decision analysis, performance measurement and monitoring. A prototype customer service portal has been built based on the MPKBS and implemented successfully in a consultancy business.

Journal ArticleDOI
TL;DR: Industrial applications to the fluid catalystic cracking process in refinery indicate that the expert system diagnoses abnormal events efficiently and promptly.
Abstract: This paper presents the development and implementation of an expert system for real-time fault diagnosis of chemical processes. The expert system is applied as a real-time computer aided decision support system, providing operation suggestions to help field operators when abnormal situations occur. The knowledge base structure, representation of knowledge, and access to expertise are technically considered. Industrial applications to the fluid catalystic cracking process in refinery indicate that the expert system diagnoses abnormal events efficiently and promptly.

Journal ArticleDOI
TL;DR: A new approach to extract plausible rules, which consists of the characterization of decision attributes is extracted from databases and the classes are classified into several groups with respect to the characterization, and two kinds of sub-rules are induced.
Abstract: One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts' decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts' rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts' decision processes.

Journal ArticleDOI
TL;DR: This paper investigates the relationship among corporate strategies, environmental forces, and the Balanced Scorecard (BSC) performance measures and proposes a decision support system to help retrieve the BSC weights of the companies with similar characteristics.
Abstract: The choice of performance measures is critical to formulating strategies. This paper investigates the relationship among corporate strategies, environmental forces, and the Balanced Scorecard (BSC) performance measures. Corporate strategies are explored within the framework of Miles and Snow's taxonomy, where they are categorized into prospectors, defenders, analyzers, and reactors. The relative weights for each performance measure are calculated by the use of the Analytic Hierarchy Process. A sample of 219 companies can confirm the link between corporate strategies, environmental forces, and the weights of the BSC performance measures. These weights shift depending on the nature of challenges companies face. In the light of this empirical evidence, a decision support system is proposed to help retrieve the BSC weights of the companies with similar characteristics. In order to measure the proximity between companies, a k-nearest neighbor technique is employed. This system can help find the weights of the performance measures for particular strategies.

Journal ArticleDOI
TL;DR: Important factors influencing the inpatient mortality were identified using a decision tree method for data mining based on 8405 patients who were discharged from the study hospital during the period of December 1, 2000 and January 31, 2001.
Abstract: This study presents an analysis of healthcare quality indicators using data mining for developing quality improvement strategies. Specifically, important factors influencing the inpatient mortality were identified using a decision tree method for data mining based on 8405 patients who were discharged from the study hospital during the period of December 1, 2000 and January 31, 2001. Important factors for the inpatient mortality were length of stay, disease classes, discharge departments, and age groups. The optimum range of target group in inpatient healthcare quality indicators were identified from the gains chart. In addition, a decision support system (DSS) was developed to analyze and monitor trends of quality indicators using Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. In the future, other quality indicators should be analyzed to effectively support a hospital-wide continuous quality improvement (CQI) activity and the DSS should be well integrated with the hospital order communication system (OCS) to support concurrent review.

Journal ArticleDOI
TL;DR: In this study, internal carotid arterial Doppler signals were obtained from 130 subjects and Multilayer perceptron neural network employing backpropagation training algorithm was used to predict the presence or absence of internalCarotid artery stenosis and occlusion.
Abstract: Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. Doppler ultrasound has proven to be a valuable technique for investigation of artery conditions. Therefore, Doppler ultrasonography is known as reliable technique, which demonstrates the flow characteristics and resistance of internal carotid arteries in stenosis and occlusion conditions. In this study, internal carotid arterial Doppler signals were obtained from 130 subjects, 45 of them had suffered from internal carotid artery stenosis, 44 of them had suffered from internal carotid artery occlusion and the rest of them had been healthy subjects. Multilayer perceptron neural network employing backpropagation training algorithm was used to predict the presence or absence of internal carotid artery stenosis and occlusion. Spectral analysis of internal carotid arterial Doppler signals was done by Burg autoregressive method for determining the neural network inputs. The network was trained, cross validated and tested with subject's internal carotid arterial Doppler signals. Performance indicators and statistical measures were used for evaluating the neural network. By using the network, the classifications of healthy subjects, subjects having internal carotid artery stenosis, and subjects having internal carotid artery occlusion were done with the accuracy of 95.2, 91.3, and 91.7%, respectively.

Journal ArticleDOI
Kun Chang Lee, Sangjae Lee1
TL;DR: One of the noticeable practical advantages of this study is that decision makers can identify the most relevant design factors and thereby allocate limited resources to them reasonably by performing the cognitive map simulation in advance before doing design adjustment to the EC sites in actuality.
Abstract: The electronic commerce (EC) has been widely studied in the academic as well as practical fields. Especially, a lot of special topics regarding the EC such as B2C and B2B have been investigated in literature. However, there are much less studies about the EC sites themselves. Besides, only a few studies exist about the issues regarding how to adjust the design factors of the EC sites. The main objective of this study is to fill this research void by employing two techniques: (1) cognitive map and (2) linear structural relationship (LISREL). The cognitive map was used to operationalize the causal relationships among design factors of the EC sites, and investigate the simulation to find the optimal strategy of adjusting the design factors. The LISREL was performed to prove the proposed research model, where original Technology Acceptance Model (TAM) [Davis MIS Q. 13 (1989) 319] is adopted as a basic framework for providing causal relationships. Usable questionnaires were collected from 114 respondents who are proved to be qualified for this study. They were educated to surf two typical EC sites appropriately and tested before answering the questionnaires. Those respondents who completed questionnaires successfully were given a book coupon of 5$ equivalent. After LISREL experiments, the proposed research model was tested, and an adjacency matrix was induced which is to be used for the cognitive map simulation. With the adjacency matrix and 15 hypothetical market situations, the cognitive map simulations were successfully performed yielding that the proposed two techniques could be used for successfully adjusting the design factors of the EC sites under consideration in line with the changes in customers' tastes and market situations. One of the noticeable practical advantages of this study is that decision makers can identify the most relevant design factors and thereby allocate limited resources to them reasonably by performing the cognitive map simulation in advance before doing design adjustment to the EC sites in actuality.

Journal ArticleDOI
TL;DR: A modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph, and the results obtained following a preliminary validation of the developed system are presented.
Abstract: The sleep apnea syndrome (SAS) is a respiratory disorder, which is characterised by the occurrence of five or more apneic events (apnea or hypopnea) per hour of sleep. Diagnosis of the SAS is a process that is markedly heuristic by nature, in that doctors handle information that is both numerical and symbolic, and employ qualitative descriptive terminology. An expert draws up a contextualised clinical interpretation that relates a patient's sleep process and respiratory physiology, involving a detailed analysis of the polysomnograph corresponding to a night's sleep. This task, implying a great deal of work on the part of clinical staff and a high economic cost, can in fact be partially automated. Our paper describes a modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph. The main tasks of our system are the identification and classification of respiratory events, the construction of the patient's hypnogram and the correlation of all the information obtained so as to arrive at a final diagnosis with respect to the existence of the syndrome. Finally our article presents and discusses the results obtained following a preliminary validation of the developed system.

Journal ArticleDOI
TL;DR: This work has developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization, and can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy.
Abstract: Enterprise Resource Planning systems tend to deploy Supply Chain Management and/or Customer Relationship Management techniques, in order to successfully fuse information to customers, suppliers, manufacturers and warehouses, and therefore minimize system-wide costs while satisfying service level requirements. Although efficient, these systems are neither versatile nor adaptive, since newly discovered customer trends cannot be easily integrated with existing knowledge. Advancing on the way the above mentioned techniques apply on ERP systems, we have developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization. The system can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system can be periodically retrained as new information is added to the ERP. In this paper, we present the architecture and development details of the system, and demonstrate its application on a real test case.