Clustering of nonstationary data streams: a survey of fuzzy partitional methods

YesData streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift.Ministero dell‘Istruzione, dell‘Universitá e della Ricerca

Indoor/outdoor navigation system based on possibilistic traversable area segmentation for visually impaired people

Autonomous collision avoidance for visually impaired people requires a specific processing for an accurate definition of traversable area. Processing of a real time image sequence for traversable area segmentation is quite mandatory. Low cost systems suggest use of poor quality cameras. However, real time low cost camera suffers from great variability of traversable area appearance at indoor as well as outdoor environments. Taking into account ambiguity affecting object and traversable area appearance induced by reflections, illumination variations, occlusions (, etc...), an accurate segmentation of traversable area in such conditions remains a challenge. Moreover, indoor and outdoor environments add additional variability to traversable areas. In this paper, we present a real-time approach for fast traversable area segmentation from image sequence recorded by a low-cost monocular camera for navigation system. Taking into account all kinds of variability in the image, we apply possibility theory for modeling information ambiguity. An efficient way of updating the traversable area model in each environment condition is to consider traversable area samples from the same processed image for building its possibility maps. Then fusing these maps allows making a fair model definition of the traversable area. Performance of the proposed system was evaluated on public databases, with indoor and outdoor environments. Experimental results show that this method is challenging leading to higher segmentation rates

Low rank surrogates for polymorphic fields with application to fuzzy-stochastic partial differential equations

We consider a general form of fuzzy-stochastic PDEs depending on the interaction of probabilistic and non-probabilistic ("possibilistic") influences. Such a combined modelling of aleatoric and epistemic uncertainties for instance can be applied beneficially in an engineering context for real-world applications, where probabilistic modelling and expert knowledge has to be accounted for. We examine existence and well-definedness of polymorphic PDEs in appropriate function spaces. The fuzzy-stochastic dependence is described in a high-dimensional parameter space, thus easily leading to an exponential complexity in practical computations. To aleviate this severe obstacle in practise, a compressed low-rank approximation of the problem formulation and the solution is derived. This is based on the Hierarchical Tucker format which is constructed with solution samples by a non-intrusive tensor reconstruction algorithm. The performance of the proposed model order reduction approach is demonstrated with two examples. One of these is the ubiquitous groundwater flow model with Karhunen-Loeve coefficient field which is generalized by a fuzzy correlation length

Técnica local basada en conjuntos difusos de tipo 2 para mejorar la imagen de manchas

The proposed approach in the paper comes under “Advanced Soft Computing Based Medical Image Processing Research” and the work has been conducted by Dr. Dibya Jyoti Bora (Assistant Professor), School of Computing Sciences, The Assam Kaziranga University, Jorhat, Assam in the year 2018-2019. Introduction: HE stain images, although considered as the golden standard for medical image diagnosis, are still found to suffer from poor contrast and degradation in color quality. In this paper, a Type-2 fuzzy set-based enhancement technique is proposed for HE stain image enhancement with special care towards color-based computations and measurements. Methods: This paper introduces a new approach based on Type-2 fuzzy set for HE stain image enhancement where Bicubic Interpolation plays an important part. Unsharp Masking is also employed as a post enhancement factor. Results: From the results, it is clearly visible that cell nuclei and other cell bodies are easily distinguishable from each other in the enhanced result produced by our proposed approach. It implies that vagueness in the edges surrounding the objects in the original image is removed to an acceptable level. Conclusions: The proposed approach is found to be, through both subjective and objective evaluations, an efficient preprocessing technique for a better HE stain image analysis. Originality: The ideas involved in this paper are original. If work by other researchers are mentioned in any part of the paper, then they are cited properly. Limitation: The relatively high time complexity is the only limitation associated with the proposed approach.El enfoque propuesto en el artículo se encuentra en el proyecto “Investigación avanzada de procesamiento de imágenes médicas basadas en computación suave”, el trabajo ha sido realizado por el doctor Dibya Jyoti Bora (profesor asistente), de la Facultad de Ciencias de la Computación, Universidad de Assam Kaziranga, Jorhat, Assam en el año 2018-2019. Introducción: las imágenes de tinción HE, aunque consideradas como el estándar ideal para el diagnóstico de imágenes médicas, aún sufren de poco contraste y degradación en la calidad del color. En este documento se propone una técnica de mejora basada en conjuntos difusos tipo 2 para optimizar la imagen de tinción HE con especial cuidado hacia los cálculos y mediciones basados en el color. Métodos: este documento presenta un nuevo enfoque basado en el conjunto difuso tipo 2 para mejorar laimagen de tinción HE, donde la interpolación bicúbica juega un papel importante. La máscara de desenfoque también se emplea como factor de mejora posterior. Resultados: a partir de los resultados es claramente visible que los núcleos celulares y otros cuerpos celulares son fácilmente distinguibles entre sí en el resultado mejorado producido por el enfoque propuesto. Esto implica que la vaguedad en los bordes que rodean los objetos en la imagen original se elimina a un nivel aceptable. Conclusiones: se encuentra que el enfoque es, a través de evaluaciones tanto subjetivas como objetivas, una técnica de preprocesamiento eficiente para un mejor análisis de imagen de tinción HE. Originalidad: las ideas involucradas en este documento son originales. Si el trabajo de otros investigadores se menciona en alguna parte del artículo se citan correctamente. Limitación: la complejidad de tiempo relativamente alta es la única limitación asociada con el enfoque propuesto

Tracking time evolving data streams for short-term traffic forecasting

YesData streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems