Samia Nefti

Bio: Samia Nefti is an academic researcher from University of Salford. The author has contributed to research in topics: Multi-agent system & Fuzzy clustering. The author has an hindex of 13, co-authored 46 publications receiving 513 citations. Previous affiliations of Samia Nefti include Manchester Metropolitan University.

A fuzzy trust model for e-commerce

Abstract: It is argued that e-commerce has not reached its full potential and trust was often cited as the main reason why many customers are still skeptical about some online vendors. Many trust models have been developed, but most are subjective and did not take into account the vagueness and ambiguity of the domain and the specificity of customers. We have developed a model that attempts to identify the information customers expect to find on a vendors website to increase their trust and hence the likelihood of a transaction to take place. In this paper, we present a method based on fuzzy logic to evaluate trust in e-commerce. We argue that fuzzy logic is suitable for trust evaluation as it takes into account the uncertainties within e-commerce data and like human relationships, trust is often expressed by linguistics terms rather then numerical values. We validated the system using two case studies.

Intelligent Adaptive Mobile Robot Navigation

Abstract: This paper deals with the application of a neuro-fuzzy inference system to a mobile robot navigation in an unknown, or partially unknown environment. The final aim of the robot is to reach some pre-defined goal. For this purpose, a sort of a co-operation between three main sub-modules is performed. These sub-modules consist in three elementary robot tasks: following a wall, avoiding an obstacle and running towards the goal. Each module acts as a Sugeno–Takagi fuzzy controller where the inputs are the different sensor information and the output corresponds to the orientation of the robot. The rule-base is generated by the controller after some learning process based on a neural architecture close to that used by Wang and Menger. This leads to adaptive neuro-fuzzy inference systems (ANFIS) (one for each module). The adaptive navigation system (ANFIS), based on integrated reactive-cognitive parts, learns and generates the required knowledge for achieving the desired task. However, the generated rule-base suffers from redundancy and abundance of data, most of which are less useful. This makes the assignment of a linguistic label to the associated variable difficult and sometimes counter-intuitive. Consequently, a simplification phase allowing elimination of redundancy is required. For this purpose, an algorithm based on the class of fuzzy c-means algorithm introduced by Bezdek and we have developed an inclusion structure. Experimental results confirm the meaningfulness of the elaborated methodology when dealing with navigation of a mobile robot in unknown, or partially unknown environment.

Systems Control With Generalized Probabilistic Fuzzy-Reinforcement Learning

Abstract: Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be deterministic. In many problems, however, the consequence of an action may be uncertain or stochastic in nature. In this paper, we propose a novel RL approach to combine the universal-function-approximation capability of fuzzy systems with consideration of probability distributions over possible consequences of an action. The proposed generalized probabilistic fuzzy RL (GPFRL) method is a modified version of the actor-critic (AC) learning architecture. The learning is enhanced by the introduction of a probability measure into the learning structure, where an incremental gradient-descent weight-updating algorithm provides convergence. Our results show that the proposed approach is robust under probabilistic uncertainty while also having an enhanced learning speed and good overall performance.

A New Fuzzy Set Merging Technique Using Inclusion-Based Fuzzy Clustering

Abstract: This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets.

A neural network approach for railway safety prediction

Abstract: Artificial neural networks (ANNs) are becoming increasingly popular for solving complex problems, as they can behave quite well at solving problems that don't have an algorithmic solution or for which the algorithmic solution is too complex to be found. In railway systems, the problem of predicting the system malfunctions, or equivalently, railway safety is of paramount interest for most of railway companies. Traditional ways of predicting railway safety are very expensive in terms of time consuming, which make them inefficient under certain circumstances. This paper advocates the use of ANNs architecture to handle the safety problem. By taking irregularities in the positioning of the rails as input to the ANN, the ANN can predict the safety ratio of the rails. In order to reduce the dimensionality of inputs data a wavelet transformation technique has been employed. Different neural network structures are created and their performances both in terms of mean squared error and correlation coefficient have been evaluated to find out the best structure for predicting railway safety. The experiments showed that when the model is trained on a dataset subset and then tested on different subset, it performed satisfactorily and can predict the desired output with a very low error factor.

Fuzzy sets and systems

Abstract: The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Multiagent systems: a modern approach to distributed artificial intelligence

Abstract: Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures

Abstract: [1] Nondestructive imaging methods such as X-ray computed tomography (CT) yield high-resolution, three-dimensional representations of pore space and fluid distribution within porous materials. Steadily increasing computational capabilities and easier access to X-ray CT facilities have contributed to a recent surge in microporous media research with objectives ranging from theoretical aspects of fluid and interfacial dynamics at the pore scale to practical applications such as dense nonaqueous phase liquid transport and dissolution. In recent years, significant efforts and resources have been devoted to improve CT technology, microscale analysis, and fluid dynamics simulations. However, the development of adequate image segmentation methods for conversion of gray scale CT volumes into a discrete form that permits quantitative characterization of pore space features and subsequent modeling of liquid distribution and flow processes seems to lag. In this paper we investigated the applicability of various thresholding and locally adaptive segmentation techniques for industrial and synchrotron X-ray CT images of natural and artificial porous media. A comparison between directly measured and image-derived porosities clearly demonstrates that the application of different segmentation methods as well as associated operator biases yield vastly differing results. This illustrates the importance of the segmentation step for quantitative pore space analysis and fluid dynamics modeling. Only a few of the tested methods showed promise for both industrial and synchrotron tomography. Utilization of local image information such as spatial correlation as well as the application of locally adaptive techniques yielded significantly better results.

A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

Abstract: Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c-means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications.