Attribute dependency data analysis for massive datasets by fuzzy transforms

We present a numerical attribute dependency method for massive datasets based on the concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts for numerical attribute dependency in data analysis: Therein, the multi-dimensional inverse fuzzy transform was useful for approximating a regression function. Here we give an extension of this method in massive datasets because the previous method could not be applied due to the high memory size. Our method is proved on a large dataset formed from 402,678 census sections of the Italian regions provided by the Italian National Statistical Institute (ISTAT) in 2011. The results of comparative tests with the well-known methods of regression, called support vector regression and multilayer perceptron, show that the proposed algorithm has comparable performance with those obtained using these two methods. Moreover, the number of parameters requested in our method is minor with respect to those of the cited in the above two algorithms

Fuzzy Entropy-Based Spatial Hotspot Reliability

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini’s Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots

Max-Min Fuzzy Relation Equations for a Problem of Spatial Analysis

We implement an algorithm that uses a system of max-min fuzzy  relation equations (SFRE) for solving a problem of spatial analysis. We integrate this algorithm in a Geographical information Systems (GIS) tool. We apply our  process to determine the symptoms after that an expert sets the SFRE with the values of the impact coefficients related to some parameters of a geographic zone under study. We also define an index of evaluation about the reliability of the results

A Multilevel Fuzzy Transform Method for High Resolution Image Compression

The Multilevel Fuzzy Transform technique (MF-tr) is a hierarchical image compression method based on Fuzzy Transform, which is successfully used to compress images and manage the information loss of the reconstructed image. Unlike other lossy image compression methods, it ensures that the quality of the reconstructed image is not lower than a prefixed threshold. However, this method is not suitable for compressing massive images due to the high processing times and memory usage. In this paper, we propose a variation of MF-tr for the compression of massive images. The image is divided into tiles, each of which is individually compressed using MF-tr; thereafter, the image is reconstructed by merging the decompressed tiles. Comparative tests performed on remote sensing images show that the proposed method provides better performance than MF-tr in terms of compression rate and CPU time. Moreover, comparison tests show that our method reconstructs the image with CPU times that are at least two times less than those obtained using the MF-tr algorithm

A Novel Image Similarity Measure Based on Greatest and Smallest Eigen Fuzzy Sets

A novel image similarity index based on the greatest and smallest fuzzy set solutions of the max–min and min–max compositions of fuzzy relations, respectively, is proposed. The greatest and smallest fuzzy sets are found symmetrically as the min–max and max–min solutions, respectively, to a fuzzy relation equation. The original image is partitioned into squared blocks and the pixels in each block are normalized to [0, 1] in order to have a fuzzy relation. The greatest and smallest fuzzy sets, found for each block, are used to measure the similarity between the original image and the image reconstructed by joining the squared blocks. Comparison tests with other well-known image metrics are then carried out where source images are noised by applying Gaussian filters. The results show that the proposed image similarity measure is more effective and robust to noise than the PSNR and SSIM-based measures

Happiness and Well-being Revisited: Drawing on the Contributions of Contemporary Feminism to Redefine our Understanding of the Good Life

Il lavoro esplora i contributi del femminismo contemporaneo al fine di ridefinire le attuali concezioni di benessere e felicità fornite dalla letteratura scientifica. Dopo aver introdotto l’argomento in una prospettiva interdisciplinare, saranno presentate due discipline, entrambe afferenti ai movimenti femministi contemporanei, ovvero l’economia femminista e l’etica della cura. L’analisi di tali contributi rileva che lo studio del benessere e della felicità necessita di una prospettiva che includa variabili contestuali, presenza/assenza di giustizia ed eguaglianza, e principi morali ed etici nel perseguimento della buona vita. A tal fine, nel successivo capitolo, saranno presentati il Capabilities approach e l’approccio della psicologia critica di comunità, al fine di operazionalizzare i concetti sviluppati nel capitolo precedente. La seconda parte del lavoro, esamina empiricamente i contributi della parte teorica attraverso tre studi. Il primo inserisce felicità e benessere in una prospettiva contestuale mediante l’utilizzo della HappyApp, uno strumento disegnato per rilevare la soddisfazione giornaliera degli individui rispetto ad aree importanti della propria vita. Il secondo studio esplora, tramite una serie di regressioni lineari multiple, il rapporto tra giustizia sociale e soddisfazione di vita al livello macro-sociale. Il terzo studio propone la validazione Italiana, attraverso l’ausilio del modello delle equazioni strutturali, di uno strumento per la rilevazione dell’orientamento morale alla giustizia e alla cura, ovvero il Measure of Moral Orientation second revision (MMO-2). Vantaggi, limitazioni, e prospettive future dei sopracitati studi, saranno discussi

Semantic Brokering of Multimedia Contents for Smart Delivery of Ubiquitous Services in Pervasive Environments

With the proliferation of modern mobile devices having the capability to interact each other and with the environment in a transparent manner, there is an increase in the development of those applications that are specifically designed for pervasive and ubiquitous environments. Those applications are able to provide a service of interest for the user that depends on context information, such as the user's position, his preferences, the capability of the device and its available resources. Services have to respond in a rational way in many different situations choosing the actions with the best expected result by the user, so making environment not only more connected and efficient, but smarter. Here we present a semantic framework that provides the technology for the development of intelligent, context aware services and their delivery in pervasive and ubiquitous environments

Fuzzy Functions, Relations, and Fuzzy Transforms (2012)

Fuzzy functions, fuzzy relations, and fuzzy transforms are applied to fuzzy systems and like in the first issue published in 2011, here these terms must be intended in general sense. Indeed, the topics concerning fuzzy optimizations with fuzzy functions and relations (including also clustering and PSO algorithms), applications of fuzzy relations/transforms to fusion images, and detection coarse grained fuzzy association rules in the datasets and fuzzy convex processes are dealt with widely in this second issue. The contents of any single paper are resumed in the following sequel