J. I. Estévez

Cytological image analysis with a genetic fuzzy finite state machine

The objective of this research is to design a pattern recognition system based on a Fuzzy Finite State Machine (FFSM). We try to find an optimal FFSM with Genetic Algorithms (GA). In order to validate this system, the classifier has been applied to a real problem: distinction between normal and abnormal cells in cytological breast fine needle aspirate images and cytological peritoneal fluid images. The characteristic used in the discrimination between normal and abnormal cells is a texture measurement of the chromatin distribution in cellular nuclei. Furthermore, the effectiveness of this method as a pattern classifier is compared with other existing supervised and unsupervised methods and evaluated with Receiver Operating Curves (ROC) methodology.

Patient-centered simulation tool for aiding in hospital management

Abstract The study of a particular complex system by means of computer simulation is described in this paper. Hospitals are chosen as target systems where the proposed methodology is applied. In order to choose the right decisions, hospital managers need all the information about the functioning of the organization. This research project presents a simulation tool that allows virtual societies such as hospitals to be implemented. In this way, the study of emergent behaviors in these systems can be carried out. The methodology used to model the hospital is process oriented. This approach allows us to implement a patient-centered simulation tool.

Using KADS methodology in a simulation assisted knowledge based system: application to hospital management

Abstract This paper presents a knowledge-based system for aiding in the decision-making process that is carried out in hospital management. There are a number of reasons that have led us to choose a tool such as this one: the amount of information generated in a hospital, its great interrelation and the need of heuristic knowledge for its processing. The KBS has been designed following the KADS methodology. KADS has allowed us to obtain a structured representation of the knowledge, which makes easier both the construction and the debugging of the knowledge base. As a starting point, the decision-making task has been decomposed in four subtasks: monitoring; diagnosis; prediction of the possible solutions for the stated problem; and design of the solution. The prediction task can only be performed through a simulation program where the dynamics of the hospital is modeled. This allows the system to detect the consequences of the application of different possible solutions. The co-operation between simulation and artificial intelligence has proven to be an adequate technique for dealing with the decision-making tasks that are involved with the management of complex organizations.

Patient-Centered Simulation to Aid Decision-Making in Hospital Management:

Computer simulation has eased the study of complex systems. A hospital is a complex sys tem that is formed by a large number of units with strong interrelationships. Even though resources are limited, patients must be effi ciently treated. This paper presents simulation as a tool to aid hospital management. In the first phase we present an introduction to the problem and its motivation. The next step is the description of how the system functions. The choice of the simulation model and the approach in dealing with it are described. Finally, the implementation of the simulation tool is pre sented. This tool is used for supporting the deci sion-making processes in hospital management.

Cytological breast fine needle aspirate images analysis with a genetic fuzzy finite state machine

A system based on a fuzzy finite state machine (FFSM) has been developed for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. The system uses computer vision techniques to analyse cell nuclei in order to extract determinate features and to try to find, by means of genetic algorithms (GA), the ideal FFSM that is able to classify them. This application to breast cancer diagnosis uses the characteristics of individual cells to discriminate benign from malignant breast lumps. In our system, we try to find a texture measurement that can be included in the feature set in order to improve the classifier performance: a complexity measurement of the structural pattern is used to discriminate between benign and malign cells. With this measure and the technique described, we have observed that not only is the absolute complexity of the image relevant, but also the way in which the complexity is distributed at different scales.

Automatic analysis of signals with symbolic content

This paper presents a set of methods for helping in the analysis of signals with particular features that admit a symbolic description. The methodology is based on a general discrete model for a symbolic processing subsystem, which is fuzzyfied by means of a fuzzy inference system. In this framework a number of design problems have been approached. The curse of dimensionality problem and the specification of adequate membership functions are the main ones. In addition, other strategies, which make the design process simpler and more robust, are introduced. Their goals are automating the production of the rule base of the fuzzy system and composing complex systems from simpler subsystems under symbolic constrains. These techniques are applied to the analysis of wakefulness episodes in the sleep EEG. In order to solve the practical difficulty of finding remarkable situations from the outputs of the symbolic subsystems an unsupervised adaptive learning technique (FART network) has been applied.

Using an induced relational decision tree for rule injection in a learning classifier system

Transfer learning, using systems with rich and general representations, to improve adaptive rule based systems designed to efficiently react in changing environments is the idea behind the problem studied in this paper. In this framework, the aim of this research is studying the benefits of using relational learning in combination with an evolutionary propositional learning system as XCS. The proposed method starts by learning a first order relational decission tree using a set of simplified instances of a problem. The learned relational model is then used to help a learning classifier system to deal with a more complex instance of the task. The researched strategy is based on injecting rules derived from the relational model in the discovering subsystem of the XCS. Results show that this method can be used to automatically adapt the behaviour of a learning rule based system when the environment increases its complexity.

Integrating Multimedia Technology, Knowledge Based System and Speech Processing for the Diagnostic and Treatment of Developmental Dyslexia

SICOLE is a research effort to develop a software environment to help tutors of dyslexic children with the diagnostic and treatment tasks. This paper describes the architecture of the package already implemented where three important elements interact: a multimedia interface, an inference module and a database. This architecture provides the system with the flexibility to support a large variety of tasks, dynamic presentations, and complex teaching strategies. The application of speech recognition technology is also researched as an important part of the evaluation of the dyslexic children improvements. This package is being used at the present moment in several Spanish Schools as part of its validation process.

TWO PROBLEMS IN FUZZY STATE MACHINE DESIGN: STRUCTURE DEFINITION AND RULE BASE SIMPLIFICATION

Abstract A Fuzzy Finite State Machine (FFSM) is an extension of the classical Finite Deterministic Automaton (FDA). This algorithm finds many applications in several fields as pattern recognition, intelligent agent modeling, human - machine interfaces, and finally but not less important control engineering. This paper proposes novel strategies to approach two of the main problems in designing FFSMs: structure definition from a “seed” FDA (basic prototype), and simplification of the resulting model according to the statistical features of both external and feedback inputs.

Tools for Acquisition, Processing and Knowledge-Based Diagnostic of the Electroencephalogram and Visual Evoked Potentials

The objective of our research is to develop computer-based tools to automate the clinical evaluation of the electroencephalogram (EEG) and visual evoked potentials (VEP). This paper describes a set of solutions to support all the aspects regarding the standard procedures of the diagnosis in neurophysiology, including: (1) acquisition and real-time processing and compression of EEG and VEP signals, (2) real-time brain mapping of spectral powers, (3) classifier design, (4) automatic detection of morphologies through supervised neural networks. (5) signal analysis through fuzzy modelling, and (6) a knowledge based approach to classifier design.