Herman Akdag

The evaluation of hospital service quality by fuzzy MCDM

This study applies the Multiple Criteria Decision Making (MCDM) to evaluate the service quality of some Turkish hospitals. In general the service quality has abstract properties, which mean that using the previously known measurement approach is insufficient. It is for this reason that the fuzzy set theory is adopted as a research template. In Istanbul, Turkey, there are four B class hospitals classed as private hospitals that are covered by the Social Security Institution (SSI) and for which we propose to represent the service performance measurement using triangular fuzzy numbers. In this study, importance weights of performance criteria are found with AHP. Then, the Multiple Criteria Decision Making methods TOPSIS and Yagers min-max approach are applied to find and rank the crisp performance values. In a second step, an aggregation of performance criteria with OWA and Compensatory AND operators are looked at instead of the TOPSIS method and min-max approach. Thereby numerical applications are supplied by the four methods and the obtained results are compared.

Actuator and sensor fault detection and isolation of an actuated seat via nonlinear multi-observers

In this paper, a model-based approach to detect and to isolate sensors and actuators faults using nonlinear sliding mode observers is proposed for an actuated seat. The goal is to ensure the comfort and the security of the users in simulators applications. The principle is to reconstruct the state vector of the system by sliding mode observers and to compare the estimated outputs with those measured as residuals. In this work, a multi-observers technique is used. It consists of designing multiple observers such that each observer must be robust to noises and to other uncertainties but sensitive to each actuator or sensor fault. Simulation results are given to show the effectiveness of the approach.

A tool for aggregation with words

The need of computing with words has become an important topic in many areas dealing with vague information. The aim of this paper is to present different tools which support computing with words. Especially, we are concerned with the weighted aggregation of linguistic term sets, whose results are just the words themselves without using the fuzzy numbers that represent the semantics of their linguistic terms. We propose a new aggregation operator, referred to as the symbolic weighted median that computes the most representative element from an ordered collection of weighted linguistic terms. This operator aggregates the linguistic labels such that its result is expressed in terms of the initial linguistic term set though is modified by using dedicated tools called the generalized symbolic modifiers. One advantage of this proposal is that the expression domain does not change: we increase or decrease the granularity only where it becomes necessary. Additionally this new operator exhibits several interesting mathematical properties.

Linguistic modifiers in a symbolic framework

In common language, as well as in knowledge–based systems, the truth of a proposition can be evaluated in a qualitative manner using adverbs usually represented on a scale of symbolic degrees. To combine or aggregate such symbolic degrees, we may need scales of different precision levels. We propose to model small variations inside a degree scale using linguistic modifiers in a symbolic framework. We formally define such modifiers and we distinguish three families: reinforcing, weakening and central modifiers. We also introduce the original notion of intensity rate associated to a linguistic degree on a scale base. After that, we propose a generalization of our modifiers, so we obtain more sophisticated tools. These have been used, in particular, in an application on colorimetry that allows us to alter the color slightly. The importance of our work is that most of our linguistic modifiers verify some interesting properties on their intensity rate: notably, they assume a certain order relation.

Image Retrieval using Fuzzy Representation of Colors

The problem addressed in this article is image indexing and retrieval according to the color. Indeed we propose a classification based on the dominant color(s) of the images. The process consists in two steps: first, assigning a colorimetric profile to the image in HLS space (Hue, Lightness, Saturation) and then, handling the query for the retrieval. To achieve the first step, the definition of hue is done using a fuzzy representation that takes into account the non-uniformity of color distribution. Then, lightness and saturation are represented through linguistic qualifiers also defined in a fuzzy way. Finally, the profile is built through fuzzy functions representing the membership degree of the image to different classes. Thus, the query for image retrieval is a pair (hue, qualifier). The second step looks for a match between the query and the profiles. In order to improve the software and to make it more flexible, the user can re-define the fuzzy representation of Hue, Lightness and Saturation, according to his own perception.

Knowledge Based Supervised Fuzzy-Classification: An Application to Image Processing

In this paper, we take an interest in representation and treatment of imprecision and uncertainty in order to propose an original approach to approximate reasoning. This work has a practical application in supervised learning pattern recognition. Production rules whose conclusions are accompanied by belief degrees, are obtained by supervised learning from a training set. The proposed learning method is multi-featured, it allows to take into account the possible predictive power of a simultaneously considered feature conjunction. On the other hand, the feature space partition allows a fuzzy representation of the features and data imprecision integration. This uncertainty is managed in the learning phase as well as in the recognition one. To introduce more flexibility and to overcome the boundary problem due to the manipulations of membership functions of fuzzy sets, we propose to use a context-oriented approximate reasoning. For this purpose, we introduce an adequate distance to compare neighbouring facts. This distance, measuring imprecision, combined with the uncertainty of classification decisions represented by belief degrees, drives the approximate inference.The proposed method was implemented in a software called SUCRAGE and confronted with a real application in the field of image processing. The obtained results are very satisfactory. They validate our approach and allow us to consider other application fields.

THE LABELED SYSTEMS OF MULTIPLE NEURAL NETWORKS

This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.

Generalized Modifiers as an Interval Scale: Towards Adaptive Colorimetric Alterations

In this article, new tools to represent the different states of a same knowledge are described. These states are usually expressed through linguistic modifiers that have been studied in a fuzzy framework, but also in a symbolic context. The tools we introduce are called generalized symbolic modifiers: they allow linguistic modifications. A first beginning of this work on modifiers has been done by Akdag & al and this paper is the continuation. Our tools are convenient and simple to use; they assume interesting mathematical properties as order relations or infinite modifications and, moreover, they can be seen as an interval scale. Besides, they are used in practice through a colorimetric application and give very good results. They act as a link between modifications expressed with words and colorimetric alterations.

Proposition of a semi-automatic possibilistic information scoring process

This paper proposes a semi-automatic three step information scoring process that starts from constructs representing structured pieces of information and a user query. It first identifies the constructs relevant to answer the user question, based on their similarity to the query. The relevant items are then individually scored, taking into account both the reliability of their source and the certainty the latter expresses through its choice of linguistic terms. Lastly, these individual scores are fused, modeling a corroboration process that takes into account information obsolescence and source relations. This procedure is performed in the framework of possibility theory, relying on the definition of the appropriate aggregation operators.

Through a Fuzzy Spatiotemporal Information System for Handling Excavation Data

This article presents an approach for dealing with archaeological excavation data with its imperfection (imprecision) from modeling to querying. It introduces a new archaeological data model in PVL and extends it in Imperfect PVL. Since archaeological data are mostly imprecise, a fuzzy set approach is used for the storage and the querying. From the modeling, it proposes a way to store fuzzy data into a multivalued form, and then it exposes the link between the classic (non imperfect) data and the multivalued data. Finally, it illustrates the approach using some simple requests for spatial, temporal and spatiotemporal imperfect information extraction.