Use Of Case-based Reasoning Research Articles

Case-based reasoning and adaptation in hydraulic production machine design

Case-based reasoning (CBR) can support hydraulic circuit design. Existing expert systems for hydraulic system design use production rules as its source of knowledge. However, this leads to problems of knowledge acquisition and knowledge base maintenance. This paper describes the application of CBR to hydraulic circuit design for production machines, which helps solving problems using past experience. A technique Case-based adaptation (CBA) is implemented in the adaptation stage of CBR so that adaptation becomes much easier. A prototype system has been developed to verify the usefulness of CBR and CBA in hydraulic production machines.

Web hypermedia cost estimation: further assessment and comparison off cost estimation modelling techniques

Research into Web cost estimation is relatively new, where few studies have compared cost estimation modelling techniques for Web development, with an emphasis placed on techniques such as Case-based Reasoning (CBR), linear and stepwise regression. Although in a large subset of these studies CBR has given the best predictions, results were based on a simple type of CBR, where no adaptation rules were used to adjust the estimated effort obtained. In addition, when comparing the prediction accuracy of estimation models, analysis has been limited to a maximum of three training/validation sets, which according to recent studies, may lead to untrustworthy results. Since CBR is potentially easier to understand and apply (two important factors to the successful adoption of estimation methods within Web development companies), it should be examined further. This paper has therefore two objectives: i) to further investigate the use of CBR for Web development effort prediction by comparing effort prediction accuracy of several CBR techniques; ii) to compare the effort prediction accuracy of the best CBR technique against stepwise and multiple linear regression, using twenty combinations of training/validation sets. Various measures of effort prediction accuracy were applied. One dataset was used in the estimation process. Stepwise and multiple linear regression showed the best prediction accuracy for the dataset employed.

A paradigm for the non-routine design of electromagnetic devices using a case based reasoning approach

The use of optimization techniques to satisfy a set of specifications Is becoming more common. However, these processes may fail if the initial device, when considered with the specifications is over-constrained. This paper proposes an approach to identifying this form of failure through the use of Case-Based Reasoning and three inter-related models of the device (structural, behavioral and mathematical). When a potential failure is found, the system makes structural changes to the device to bypass the failure.

Comparison of Case-Based Reasoning and Artificial Neural Networks

The outcome of construction litigation depends on a large number of factors. To predict the outcome of such litigation is difficult because of the complex interrelationships between these many factors. Two attempts are reported in the literature that use, respectively, case-based reasoning (CBR) and artificial neural networks (ANN) to overcome this difficulty. These studies were conducted by using the same 102 Illinois circuit court cases; an additional 12 cases were used for testing. Prediction rates of 83% in the CBR study and 67% in the ANN study were obtained. In this paper, CBR and ANN are compared, and their advantages and disadvantages are discussed in light of these two studies. It appears that CBR is more flexible when the system is updated with new cases, has better explanation facilities, and handles missing data and a large number of features better than ANN in this domain. If the use of CBR and ANN is understood better and if, as a result, the outcome of construction litigation can be predicted with reasonable accuracy and reliability, all parties involved in the construction process could save considerable money and time.

Data mining applications to dermatological diseases, colorectal cancer, diabetes, cardiology and medical audit systems

Diagnosis of Dermatological Diseases This project aims to develop tools that would provide doctors with the ability to discover the typical characteristics of certain inflammatory dermatological diseases. It is hoped that this will aid doctors in accurately diagnosing the dermatological disorder, especially between two skin diseases with very similar characteristics such as psoriasis and atopic dermatitis. Prognostic Models for Colorectal Cancer Patients The goal of this project is to build a robust prognostic model that will predict length of survival for patients with colorectal cancer. Techniques employed for this purpose include Neural Networks, Case-based reasoning (CBR), Cox's Regression and Regression Tree Induction. Accomplishments have been the development of point estimates of survival from Cox's regression to allow direct comparison of Artificial Intelligence techniques with Cox's results and the development of techniques for building more perspicuous models that are intuitive to medical practitioners. Pre-empting Complications in Diabetic Patients Data, provided by Data Retrieval in General Practice, is currently being used to discover common trends that appear among diabetic patients. These trends can then be used to predict complications in a diabetic's life cycle. This will allow patients who are not at risk to be filtered out and those who are at risk to be carefully monitored. Data mining techniques such as Sequential Pattern Discovery are being employed for this purpose. Risk Assessment in Cardiology This project has been investigating the use of case-based reasoning and the incorporation of fuzzy logic to provide insight into the understanding of the underlying mechanisms of coronary heart disease. By identifying patients at risk early on, it is easier to stop the progression of this disease. A series of qualitative and quantitative information was gathered from middle aged men, such as weight, blood pressure, body fat and family medical histories over a three year time span, to help identify which factors contribute to coronary heart disease. Integrating Data Mining into Medical Audit Systems The aim of this project is to bring Data Mining into mainstream medical audit by integrating it with Medical statistics techniques already in use. This will encourage the practice of Evidence-based medicine, that is becoming a norm in western societies, providing IT support for it. The motivation behind this project is the realization that current Medical statistics techniques fall short of certain objectives and requirements of medical audit.

Disease diagnosis validation in TROPIX using CBR

1 A PhD research project of the author at the Federal University of Technology, Owerri (FUTO), Nigeria, with bench work at The Center for Advanced Computer Studies, The University of Southwestern Louisiana, Lafayette, LA, USA. 1 TROPIX is a practical application project initially designed to help improve health care delivery in the rural/semi urban clinics and public hospitals in Nigeria due largely to limited laboratory facilities, medical doctors, and expertise. This paper is devoted to the use of case-based reasoning (CBR) paradigm in concert with statistical association-based reasoning (ABR) for disease diagnosis, validation and therapy selection components of the research. Essentially, tentative disease diagnosis arrived at by some classification method using similarity and dissimilarity aggregate functions, the matched vector functions ( MVF), aided by the application of evidence ratio factors ( ERF) for tied match cases is passed to the CBR model for validation by reusing past similar cases. The design and organization of the case-library using singular value decomposition ( SVD) technique on the disease-attribute decision matrix to generate primary/secondary storage key clusters, as well as the use of domain-specific case-object properties that help to build a good case-base are described in some detail. The paper presents a disease case validation algorithm for appropriate data filtering and therapy selection enhancement from the new case-base.

Case-based reasoning system for mixing equipment selection

The use of case-based reasoning in the mixing equipment selection is promising. Its main advantage is use of high quality, verified knowledge and easiness of updating. The main functions of the system are a systematic documentation of the cases and an accumulation of experience.Case-based reasoning (CBR) is an alternative approach to rule-based and object-oriented systems. CBR is based on the retrieval and adaptation of the old solutions to the new problems. There are five steps in CBR: presentation of a new problem, retrieval of the most similar cases, adaptation of the most similar old solutions, validation of the current solution and updating of the system by adding the verified solution to the database of cases. The main advantages of CBR are the easy knowledge acquisition and simple explanatory system. Other important features are easy maintenance and learning capabilities.Determination of the most important features in mixing equipment selection and their importance is studied in this work. The main features discussed are liquid volume and rheological parameters, content of particles and/or gas bubbles as well as the type of application and the aim of the process.The retrieval search has been organized by using flat memory and serial search. The following data are given as the output values of the system: impeller and entering type, scale of agitation, mixing power, number of impellers and geometry of tank. An example of CBR applications to the mixing equipment selection in wastewater industry is given.

CASE-BASED REASONING IN ENVIRONMENTAL MONITORING APPLICATIONS

Environmental monitoring is usually based on large volumes of data, while in general, environmental decision making is a complex problem, has a high degree of uncertainty, and involves diverse areas of expertise. Environmental decision-support systems are therefore good candidates for application of artificial intelligence (AI) techniques. In this paper it is argued that a suitable approach for building these systems is the use of case-based reasoning or analogical reasoning techniques, which offer more adaptability and better explanation facilities than other AI paradigms. As an example, the development stages, the architecture, and the operational characteristics of the expert system Air Quality Predictor (AIRQUAP), developed to predict air pollution levels in Athens, Greece, are described. AIRQUAP helps users retrieve historical data intelligently and can predict air pollution levels, useful for management of air pollution episodes. The performance of the system is also compared with other techniques used in this class of applications.

Applying case-based reasoning to manufacturing systems design

The object of this paper is to demonstrate the usefulness of case-based reasoning in manufacturing systems design, and to consider the characteristics of such a system. A CASE-based expert system called XBE (eXpert system By Examples) under development at Helsinki University of Technology is presented as an example. Two definitions (expert systems and CASE-based reasoning) are first explained before going into detail. The current understanding of the research group is presented. The main scope is technological, as opposed to applicative, i.e. case studies of applying CASE-based reasoning.

A Case-based Expert System For SchedulingProblems With Sequence Dependent Set Up Times

Scheduling is an important application area for expert systems. However, the development of expert systems in scheduling poses many problems and many different approaches have been attempted in prototype systems. This paper discusses the use of case-based reasoning (CBR) in the development of expert scheduling systems. The design and development of an expert system using CBR is described. The application area is an important class of scheduling problems those involving sequence dependent set up times. This type of problem is important in many production environments and arises frequently in the process industries. The paper addresses general problems and research issues related to the application of CBR to scheduling problems.