METEX: An expert system for metamorphic petrography

Classification of metamorphic rocks is normally carried out using a poorly defined, subjective classification scheme making this an area in which many undergraduate geologists experience difficulties. An expert system to assist in such classification is presented which is capable of classifying rocks and also giving further details about a particular rock type. A mixed knowledge representation is used with frame, semantic and production rule systems available. Classification in the domain requires that different facets of a rock be classified. To implement this, rocks are represented by 'context' frames with slots representing each facet. Slots are satisfied by calling a pre-defined ruleset to carry out the necessary inference. The inference is handled by an interpreter which uses a dependency graph representation for the propagation of evidence. Uncertainty is handled by the system using a combination of the MYCIN certainty factor system and the Dempster-Shafer range mechanism. This allows for positive and negative reasoning, with rules capable of representing necessity and sufficiency of evidence, whilst also allowing the implementation of an alpha-beta pruning algorithm to guide question selection during inference. The system also utilizes a semantic net type structure to allow the expert to encode simple relationships between terms enabling rules to be written with a sensible level of abstraction. Using frames to represent rock types where subclassification is possible allows the knowledge base to be built in a modular fashion with subclassification frames only defined once the higher level of classification is functioning. Rulesets can similarly be added in modular fashion with the individual rules being essentially declarative allowing for simple updating and maintenance. The knowledge base so far developed for metamorphic classification serves to demonstrate the performance of the interpreter design whilst also moving some way towards providing a useful assistant to the non-expert metamorphic petrologist. The system demonstrates the possibilities for a fully developed knowledge base to handle the classification of igneous, sedimentary and metamorphic rocks. The current knowledge base and interpreter have been evaluated by potential users and experts. The results of the evaluation show that the system performs to an acceptable level and should be of use as a tool for both undergraduates and researchers from outside the metamorphic petrography field. .

A process framework for selecting supply system architecture in manufacturing supply chains and networks

The process framework comprises three phases, as follows: scope the supply chain/network; identify the options for supply system architecture and select supply system architecture. It facilitates a structured approach that analyses the supply chain/network contextual characteristics, in order to ensure alignment with the appropriate supply system architecture. The process framework was derived from comprehensive literature review and archival case study analysis. The review led to the classification of supply system architectures according to their orientation, whether integrated; partially integrated; co-ordinated or independent. The classification was combined with the characteristics that influence the selection of supply system architecture to encapsulate the conceptual framework. It builds upon existing frameworks and methodologies by focusing on structured procedure; supporting project management; facilitating participation and clarifying point of entry. The process framework was initially tested in three case study applications from the food, automobile and hand tool industries. A variety of industrial settings was chosen to illustrate transferability. The case study applications indicate that the process framework is a valid approach to the problem; however, further testing is required. In particular, the use of group support system technologies to support the process and the steps involving the participation of software vendors need further testing. However, the process framework can be followed due to the clarity of its presentation. It considers the issue of timing by including alternative decision-making techniques, dependent on the constraints. It is useful for ensuring a sound business case is developed, with supporting documentation and analysis that identifies the strategic and functional requirements of supply system architecture.

A smart sensing platform for the classification of ambulatory patterns

This paper describes an innovative sensing approach allowing capture, discrimination, and classification of transients automatically in gait. A walking platform is described, which offers an alternative design to that of standard force plates with advantages that include mechanical simplicity and less restriction on dimensions. The scope of the work is to investigate as an experiment the sensitivity of the distributive tactile sensing method with the potential to address flexibility on gait assessment, including patient targeting and the extension to a variety of ambulatory applications. Using infrared sensors to measure plate deflection, gait patterns are compared with stored templates using a pattern recognition algorithm. This information is input into a neural network to classify normal and affected walking events, with a classification accuracy of just under 90 per cent achieved. The system developed has potential applications in gait analysis and rehabilitation, whereby it can be used as a tool for early diagnosis of walking disorders or to determine changes between pre- and post-operative gait.

An opportunity for system dynamics in manufacturing system modelling

The computer simulation of manufacturing systems is commonly carried out using discrete event simulation (DES). Indeed, there appears to be a lack of applications of continuous simulation methods, particularly system dynamics (SD), despite evidence that this technique is suitable for industrial modelling. This paper investigates whether this is due to a decline in the general popularity of SD, or whether modelling of manufacturing systems represents a missed opportunity for SD. On this basis, the paper first gives a review of the concept of SD and fully describes the modelling technique. Following on, a survey of the published applications of SD in the 1990s is made by developing and using a structured classification approach. From this review, observations are made about the application of the SD method and opportunities for future research are suggested.

On the 'classification' of neurodegenerative disorders:discrete entities, overlap or continuum?

The traditional method of classifying neurodegenerative diseases is based on the original clinico-pathological concept supported by 'consensus' criteria and data from molecular pathological studies. This review discusses first, current problems in classification resulting from the coexistence of different classificatory schemes, the presence of disease heterogeneity and multiple pathologies, the use of 'signature' brain lesions in diagnosis, and the existence of pathological processes common to different diseases. Second, three models of neurodegenerative disease are proposed: (1) that distinct diseases exist ('discrete' model), (2) that relatively distinct diseases exist but exhibit overlapping features ('overlap' model), and (3) that distinct diseases do not exist and neurodegenerative disease is a 'continuum' in which there is continuous variation in clinical/pathological features from one case to another ('continuum' model). Third, to distinguish between models, the distribution of the most important molecular 'signature' lesions across the different diseases is reviewed. Such lesions often have poor 'fidelity', i.e., they are not unique to individual disorders but are distributed across many diseases consistent with the overlap or continuum models. Fourth, the question of whether the current classificatory system should be rejected is considered and three alternatives are proposed, viz., objective classification, classification for convenience (a 'dissection'), or analysis as a continuum.

Psychological cue use and implications for a clinical decision support system

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training. © 2002 Informa UK Ltd All rights reserved.

Automated Response to Query System

SMS (Short Message Service) is now a hugely popular and a very powerful business communication technology for mobile phones. In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question at a level that allows determining some of constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. In this paper we focus on various attempts to overcome the major contradiction: the technical limitations of the SMS standard, and the huge number of found information for a possible answer.

Intelligent Search and Automatic Document Classification and Cataloging Based on Ontology Approach

This paper presents an approach to development of intelligent search system and automatic document classification and cataloging tools for CASE-system based on metadata. The described method uses advantages of ontology approach and traditional approach based on keywords. The method has powerful intelligent means and it can be integrated with existing document search systems.

Application of the Artificial Intelligence Algorithms for System Analysis of Multi Dimension Physiological Data for Developing Polyparametric Information System of Public Health Diagnostics

The polyparametric intelligence information system for diagnostics human functional state in medicine and public health is developed. The essence of the system consists in polyparametric describing of human functional state with the unified set of physiological parameters and using the polyparametric cognitive model developed as the tool for a system analysis of multitude data and diagnostics of a human functional state. The model is developed on the basis of general principles geometry and symmetry by algorithms of artificial intelligence systems. The architecture of the system is represented. The model allows analyzing traditional signs - absolute values of electrophysiological parameters and new signs generated by the model – relationships of ones. The classification of physiological multidimensional data is made with a transformer of the model. The results are presented to a physician in a form of visual graph – a pattern individual functional state. This graph allows performing clinical syndrome analysis. A level of human functional state is defined in the case of the developed standard (“ideal”) functional state. The complete formalization of results makes it possible to accumulate physiological data and to analyze them by mathematics methods.

The Information-analytical System for Diagnostics of Aircraft Navigation Units

The operation of technical processes requires increasingly advanced supervision and fault diagnostics to improve reliability and safety. This paper gives an introduction to the field of fault detection and diagnostics and has short methods classification. Growth of complexity and functional importance of inertial navigation systems leads to high losses at the equipment refusals. The paper is devoted to the INS diagnostics system development, allowing identifying the cause of malfunction. The practical realization of this system concerns a software package, performing a set of multidimensional information analysis. The project consists of three parts: subsystem for analyzing, subsystem for data collection and universal interface for open architecture realization. For a diagnostics improving in small analyzing samples new approaches based on pattern recognition algorithms voting and taking into account correlations between target and input parameters will be applied. The system now is at the development stage.

A Knowledge-Oriented Technology of System-Objective Analysis and Modelling of Business-Systems

A new original method and CASE-tool of system analysis and modelling are represented. They are for the first time consistent with the requirements of object-oriented technology of informational systems design. They essentially facilitate the construction of organisational systems models and increase the quality of the organisational designing and basic technological processes of object application developing.

Classification-Based Method of Linear Multicriteria Optimization

The paper describes a classification-based learning-oriented interactive method for solving linear multicriteria optimization problems. The method allows the decision makers describe their preferences with greater flexibility, accuracy and reliability. The method is realized in an experimental software system supporting the solution of multicriteria optimization problems.

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.

Neural-Like Growing Networks in Intelligent System of Recognition of Images

The neural-like growing networks used in the intelligent system of recognition of images are under consideration in this paper. All operations made over the image on a pre-design stage and also classification and storage of the information about the images and their further identification are made extremely by mechanisms of neural-like networks without usage of complex algorithms requiring considerable volumes of calculus. At the conforming hardware support the neural network methods allow considerably to increase the effectiveness of the solution of the given class of problems, saving a high accuracy of result and high level of response, both in a mode of training, and in a mode of identification.

Ontology-based Classification of News in an Electronic Newspaper

This paper deals with the classification of news items in ePaper, a prototype system of a future personalized newspaper service on a mobile reading device. The ePaper system aggregates news items from various news providers and delivers to each subscribed user (reader) a personalized electronic newspaper, utilizing content-based and collaborative filtering methods. The ePaper can also provide users "standard" (i.e., not personalized) editions of selected newspapers, as well as browsing capabilities in the repository of news items. This paper concentrates on the automatic classification of incoming news using hierarchical news ontology. Based on this classification on one hand, and on the users' profiles on the other hand, the personalization engine of the system is able to provide a personalized paper to each user onto her mobile reading device.