Net melon cultivars productivity under different cultivation systems, during summer and winter

In order to access the effects of cultivation in soil, sand or a commercial substrate on the productivity of Bonus #2 cultivars, 'Don Carlos' and 'Hy Mark', two experiments were conducted in the greenhouse at FCAV-UNESP, Jaboticabal- SP, Brazil, 21° 15' 22 S, 48° 18'58 W, and altitude of 595 m, from November 1999 to April 2000 (summer), and from July to November 2000 (Winter). For cultivation in soil, chemical nutrients were added, and plants were irrigated with drip irrigation. Fertigation with recirculation of the nutrient solution was used on sand; slabs were used on commercial substrate with the fertigation with non circulating nutrient solution. Bonus #2 cultivar yielded the highest production of marketable fruit, but were later in production, while Hy Mark cultivar had early production but a lower number of fruit per plant. The winter planting yielded higher production of marketable fruit while summer plantings yielded lower number of fruit per plant, but with higher average weigh. The combination between cultivation systems and cultivars yielded higher fruit production in winter.

A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

Fault location in underground systems through optimum-path forest

In this paper we propose an accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the Time Domains Reflectometry method for signal acquisition, which was further analyzed by OPF and several other well known pattern recognition techniques. The results indicated that OPF and Support Vector Machines outperformed Artificial Neural Networks classifier. However, OPF has been much more efficient than all classifiers for training, and the second one faster for classification. © 2011 IEEE.

Effect of ceramic surface treatment on the shear bond strength of a resin cement to different ceramic systems

This study evaluated three surface treatments and their effects on the shear bond strength between a resin cement and one of three ceramics. The ceramic surfaces were evaluated with scanning electron microscopy (SEM ) as well. Specimens were treated with 50 μm aluminum oxide airborne particles, 10% hydrofluoric acid etching, or a combination of the two. Using a matrix with a center hole (5.0 mm × 3.0 mm), the ceramic bonding areas were filled with resin cement following treatment. The specimens were submitted to thermal cycling (1,000 cycles) and the shear bond strength was tested (0.5 mm/minute). The failure mode and the effect of surface treatment were analyzed under SEM . Data were submitted to ANOVA and a Tukey test (α = 0.05). Duceram Plus and IPS Empress 2 composite specimens produced similar shear bond strength results (p > 0.05), regardless of the treatment method used. Hydrofluoric acid decreased the shear bond strength of In-Ceram Alumina specimens. For all materials, surface treatments changed the morphological surface. All treatments influenced the shear bond strength and failure mode of the ceramic/resin cement composites.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Minimum Evaporation Model of Dermatological Delivery Systems. Lamellar Liquid Crystal Formulations Containing Brazilian Nut (Bertholletia excelsa HBK) Vegetable Oil and Guarana Glycolic Extract

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The effects of the C-terminal amidation of mastoparans on their biological actions and interactions with membrane-mimetic systems

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

Zirconium-methacrylate oxoclusters as new hybrid materials for the modification of epoxy systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Swarm-based descriptor combination and its application for image classification

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