Unsupervised Ensembles Techniques for Visualization

In this paper we introduce two unsupervised techniques for visualization purposes based on the use of ensemble methods. The unsupervised techniques which are often quite sensitive to the presence of outliers are combined with the ensemble approaches in order to overcome the influence of outliers. The first technique is based on the use of Principal Component Analysis and the second one is known for its topology preserving characteristics and is based on the combination of the Scale Invariant Map and Maximum Likelihood Hebbian learning. In order to show the advantage of these novel ensemble-based techniques the results of some experiments carried out on artificial and real data sets are included

Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way

Study of the potential employment of Malvaceae Species in composites materials

The employ of vegetal fibers for textiles and composites represents a great potential in economic and social sustainable development. Some Malvaceae species are considered tropical cosmopolitans, such as from Sida genus. Several species of this genus provide excellent textile bast fibers, which are very similar in qualities to the jute textile fiber. The objective of the present study is present the physicochemical characterization of six Brazilian vegetal fibers: Sida rhombifolia L.; Sida carpinifolia L. f.; Sidastrum paniculatum (L.) Fryxell; Sida cordifolia L.; Malvastrum coromandelianum (L.) Gurck; Wissadula subpeltata (Kuntze) R.E.Fries. Respectively the two first species are from Brazilian Atlantic Forest biome and the four remaining from Brazilian Cerrado biome, despite of present in other regions of the planet. The stems of these species were retted in water at 37oC for 20 days. The fibers were tested in order to determine tensile rupture strength, tenacity, elongation, Young’s modulus, cross microscopic structure, Scanning Electronic Microscopy (SEM), regain, combustion, acid, alkali, organic solvent and cellulase effects, pH of the aqueous extract, Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA). The obtained values were compared with those from fibers of recognized applicability in the textile industry including hemp. The results are promising in terms of their employment in thermoset and thermoplastic medium resistance composites.FAPESP (“Fundação de Amparo à Pesquisa do Estado de São Paulo”), CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and CNPq (“Conselho Nacional de Desenvolvimento Científico e Tecnológico”) are gratefully acknowledged. The authors would also like to thank Mr. Ervin Sriubas Jr. and Kellinton José Mendonça Francisco for their technical support

Genetic Algorithms to Simplify Prognosis of Endocarditis

This ongoing interdisciplinary research is based on the application of genetic algorithms to simplify the process of predicting the mortality of a critical illness called endocarditis. The goal is to determine the most relevant features (symptoms) of patients (samples) observed by doctors to predict the possible mortality once the patient is in treatment of bacterial endocarditis. This can help doctors to prognose the illness in early stages; by helping them to identify in advance possible solutions in order to aid the patient recover faster. The results obtained using a real data set, show that using only the features selected by employing a genetic algorithm from each patient’s case can predict with a quite high accuracy the most probable evolution of the patient

Proposing to use artificial neural Networks for NoSQL attack detection

[EN] Relationships databases have enjoyed a certain boom in software worlds until now. These days, with the rise of modern applications, unstructured data production, traditional databases do not completely meet the needs of all systems. Regarding these issues, NOSQL databases have been developed and are a good alternative. But security aspects stay behind. Injection attacks are the most serious class of web attacks that are not taken seriously in NoSQL. This paper presents a Neural Network model approach for NoSQL injection. This method attempts to use the best and most effective features to identify an injection. The features used are divided into two categories, the first one based on the content of the request, and the second one independent of the request meta parameters. In order to detect attack payloads features, we work on character level analysis to obtain malicious rate of user inputs. The results demonstrate that our model has detected more attack payloads compare with models that work black list approach in keyword level

Features and models for human activity recognition

Human Activity Recognition (HAR) is aimed at identifying current subject task performed by a person as a result of analyzing data from wearable sensors. HAR is a very challenging task that has been applied in different areas such as rehabilitation and localization. During the past ten years, plenty of models, number of sensors and sensor placements, and feature transformations have been reported for this task. From this bunch of previous ideas, what seems to be clear is that the very specific applications drive to the selection of the best choices for each case. Present research is focused on early diagnosis of stroke, what involves reducing the feature space of gathered data and subsequent MAR, among other tasks. In this study, an Information Correlation Coefficient (ICC) analysis was carried out followed by a wrapper Feature Selection (FS) method on the reduced input space. Additionally, a novel MAR method is proposed for this specific problem of stroke early diagnosing, comprising an adaptation of the well-known Genetic Fuzzy Finite State Machine (GFFSM) method. To the best of the author's knowledge, this is the very first analysis of the feature space concerning all the previously published feature transformations on raw acceleration data. The main contributions of this study are the optimization of the sample rate, selection of the best feature subset, and learning of a suitable HAR method based on GFFSM to be applied to the HAR problem