Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Evaluation of motivation methods used to obtain appropriate oral hygiene levels in schoolchildren.

The aim of the present study is to assess the behaviour of different motivation methods on levels of oral hygiene among schoolchildren aged from 7 to 9 years in Araraquara, SP, Brazil. The methods tested were: indirect instruction using 'The Smiling Robot' (group I), indirect instruction through class presentation (group II) and direct instruction with macromodels (group III). A control group was also constituted, which received no kind of motivation (group IV). The O'Leary Plaque Index was used as the evaluation method, applied before the instruction and 30 days after application of the different methods. It was noted that the plaque index had not decreased in group IV only. In conclusion, all the motivation methods promoted significant decrease of plaque index and among these methods, the 'The Smiling Robot' was the one that provided the best results.

Fault identification in distribution lines using intelligent systems and statistical methods

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Comparison of Two Staining Methods for Pollen Viability Studies in Sugarcane

The present work aimed to compare two staining methods for pollen viability evaluation in sugarcane. Pollen from four sugarcane genotypes were collected at three different times (6.00, 8.00 and 9.00 a.m.) and tested for viability using two staining methods (iodine and lactophenol blue). Three anthers, of each genotype were crushed in a glass slide with a drop of the respective stain (iodine 0.1 N and lactophenol blue). The percentage of pollen viability was obtained with an optic microscope (250×) and compared with the pollen germination at culture media where one raquis of each genotype was gentle shaken in a petridish. Three replicates (petri dishes) was performed for each genotype which were maintained at the temperature of 25 °C and air humidity around 95 % for 30 min. The factors (staining methods, genotypes and times) and their interactions were evaluated by the analysis of variance, F test (P < 0.01) and the means compared by the t test (P < 0.05). The lactophenol blue staining was more sensible than the iodine staining method to detect the decrease of pollen viability which occurs naturally in sugarcane. The iodine staining method was more stable and easier than lactophenol to perform the inflorescence classification at any evaluated time (6.00, 8.00 and 9.00 a. m.). Both staining methods overestimated the viability obtained by the germination at culture media when performed at 6.00 a.m. © 2012 Society for Sugar Research & Promotion.

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Midpalatal suture maturation: Classification method for individual assessment before rapid maxillary expansion

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Use of classification in organizing and searching the web

The traditional characteristics and challenges for organizing and searching information on the World Wide Web are outlined and reviewed. The classification features of two of these methods, such as Google, in the case of automated search engines, and Yahoo! Directory, in the case of subject directories are analyzed. Recent advances in the Semantic Web, particularly the growing application of ontologies and Linked Data are also reviewed. Finally, some problems and prospects related to the use of classification and indexing on the World Wide Web are discussed, emphasizing the need of rethinking the role of classification in the organization of these resources and outlining the possibilities of applying Ranganathan's facet theories of classification.

Classification of Eucalyptus urograndis hybrids under different water availability based on biometric traits

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)