Paulo Fazendeiro

Observer-Biased Fuzzy Clustering

As generated by clustering algorithms, clusterings (or partitions) are hypotheses on data explanation which are better evaluated by experts from the application domain. In general, clustering algorithms allow a limited usage of domain knowledge about the cluster formation process. In this study, we propose both a design technique and a new partitioning-based clustering algorithm which can be used to assist the data analyst while looking for a set of meaningful clusters, i.e., clusters that actually correspond to the underlying data structure. Following an observer metaphor according to which the perception of a group of objects depends on the observer position-the closer an observer is from an image more details (s)he perceives-we resort to shrinkage to incorporate a regularization term, accounting for the observation point, within the objective function of an otherwise unbiased clustering algorithm. This technique allows our resulting biased algorithm to generate a set of reasonable partitions, i.e., partitions validated by a given cluster validity index, corresponding to views of data with different levels of granularity (levels of detail) in different regions of the data space. For the illustration of the design technique, we adopted the fuzzy c-means (FCM) algorithm as the unbiased clustering algorithm and include a convergence theorem assuring that changing the point of observation in the corresponding biased algorithm FCM with focal point (FCMFP) does not jeopardize its convergence. Experimental studies on both synthetic and real data are included to illustrate the usefulness of the approach. In addition, and as a convenient side effect of using shrinkage, the experimental results suggest that our biased algorithm (FCMFP) not only seems to scale better than the successive runs of the unbiased one (FCM) but on the average, seems to produce clusters exhibiting higher validity index values as well. In addition, less sensitivity to initialization was observed for the biased algorithm when compared with the unbiased one.

The role of radio frequency identification (RFID) technologies in improving process management and product tracking in the textiles and fashion supply chain

Abstract: This chapter aims to gain a better understanding of RFID deployment in the Fashion and Textile Supply Chain (FTSC), mainly concerning its application in supporting operations, its advantages and main business drivers. In an attempt to find an RFID deployment pattern within the FTSC, a cross-case analysis was performed. The RFID experience of five companies based in different countries was analysed. From these empirical data, three main conclusions are drawn: (1) RFID is deployed mainly to support the following operations: the handling process; tracking work-in-progress; receiving operations; shipping operations; tracking products; tracking inventories; monitoring and sorting of merchandise; counting stock and picking merchandise; tracking containers; shipping; locating products; and store management. (2) The main advantages highlighted are: better inventory management; improved read rates; and order accuracy. (3) The main driver that led the case study companies to deploy RFID technology was the identification of inefficiencies in several of their operations.

OpenCL implementations of a genetic algorithm for feature selection in periocular biometric recognition

This paper explores OpenCL implementations of a genetic algorithm used to optimize the features vector in periocular biometric recognition. Using a multi core platform the algorithm is tested for CPU and GPU, exploring different parallelization levels for each operator of the genetic algorithm. The results show that using the GPU platform it is possible to accelerate the algorithm by several orders of magnitude, with a recognition rate similar to the one obtained in the sequential version. The results also show that it is possible to use only a small portion of the features without any degradation of the classifiers recognition rate.

A fuzzy clustering algorithm with a variable focal point

In our everyday life the number of groups of similar objects that we visually perceive is deeply constrained by how far we are from the objects and also by the direction we are approaching them. Based on this metaphor, in this work we present a generalization of partitional clustering aiming at the inclusion into the clustering process of both distance and direction of the point of observation towards the dataset. This is done by incorporating a new term in the objective function, accounting for the distance between the clusterspsila prototypes and the point of observation. It is a well known fact that the chosen number of partitions has a major effect on the objective function based partitional clustering algorithms, conditioning both the level of granularity of the data grouping and the capability of the algorithm to accurately reflect the underlying structure of the data. Thus the correct choice of the number of clusters is essential for any successful application of such algorithms. The experimental part of this work shows how the proposed algorithm can be used to produce a set of valid alternatives for the appropriate number of partitions. The proposed method can be used in order to assist the data analyst when looking for a partition that correctly reflects a particular view of the data.

ARTE: An Embryo Quality Assessment Tool

This paper proposes an assessment model of putative embryos for in vitro fertilization (IVF) based on a triangular norm. One of the most common difficulties of IVF treatments is multiple pregnancy. Therefore the number of embryos for transfer is of paramount importance considering the need to reduce the incidence of multiple births without compromising overall pregnancy rates in fertility treatments. Consequently the selective embryo transfer is recommended to optimize efficacy and safety outcomes. The embryo evaluation is of enormous relevance, since it directly affects the success of different techniques used in assisted reproductive technologies (ART). The gathering of all the information needed to embryo evaluation, as well as software that can serve as aid in the decision is of great importance. The tool herein presented accomplishes these two objectives. The analysis of the requirements of the assessment process has resulted in a flexible data model, used in the presented prototype, supporting the selective embryo transfer decision-making process.

Towards cost-effective bio-inspired optimization: a prospective study on the GPU architecture

This paper studies the impact of varying the populations size and the problems dimensionality in a parallel implementation, for an NVIDIA GPU, of a canonical GA. The results show that there is an effective gain in the data parallel model provided by modern GPUs and enhanced by high level languages such as OpenCL. In the reported experiments it was possible to obtain a speedup higher than 140 thousand times for a populations size of 262 144 individuals.

On the Achievement of Both Accurate and Interpretable Fuzzy Systems Using Data-Driven Design Processes

In this chapter it is argued that, one of the most interesting features of fuzzy system is the insight provided on the linguistic relationship between their variables, or in other words, is the possibility of interpret their parameters as a set of linguistic rules. Both the notions of accuracy and interpretability are reviewed. A general design policy where both accuracy and interpretation can be taken into account is reviewed. It is argued that the lost of accuracy does not necessary occurs when this type of data-driven design policy is applied. For illustration purposes, simulation results are given from the realistic control problem of neuromuscular relaxation of patients under surgery using continuous infusion of atracurium.

Soft Methods for Automatic Drug Infusion in Medical Care Environment

The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.

A linguistic approach to supply chain performance assessment

This paper proposes a fuzzy LARG (Lean, Agile, Resilient, Green) index model for supply chain (SC) performance assessment. Through its performance evaluation, SCs are able to measure their level of efficiency and its ability to react, efficiently, to changes in a competitive environment. The case study presented shows that due to the uncertainties surrounding the SCs environment and to the qualitative description of the SCs practices, fuzzy logic can provide an effective assessment tool able to quickly incorporate changes in the SCs business policy.

Applications of artificial immune systems to computer security: A survey

For the last two decades, artificial immune systems have been studied in various fields of knowledge. They were shown to be particularly effective tools at detecting anomalous behavior in the security domain of computer systems. This article introduces the principles of artificial immune systems and surveys several works applying such systems to computer security problems. The works herein discussed are summarized and open issues are pointed out afterwards, elaborating on a novel applicability of these systems to cloud computing environments.