Intrusion detection system using optimum-path forest

Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. Neural networks and Support Vector Machines have been also extensively applied to this task. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In this research, we introduce a new pattern classifier named Optimum-Path Forest (OPF) to this task, which has demonstrated to be similar to the state-of-the-art pattern recognition techniques, but extremely more efficient for training patterns. Experiments on public datasets showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, as well as allow the algorithm to learn new attacks faster than the other techniques. © 2011 IEEE.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.

Influência de protocolos de jateamento na rugosidade da superfície de uma cerâmica de zircônia tetragonal parcialmente estabilizada por ítria

Objective: To evaluate the influence of different air abrasion protocols on the surface roughness of an yttria-stabilized polycrystalline tetragonal zirconia) (Y-TZP) ceramic, as well as the surface topography of the ceramic after the treatment. Method: Fifty-four specimens (7.5×4×7.5mm) obtained from two ceramic blocks (LAVA, 3M ESPE) were flattened with fine-grit sandpaper and subjected to sintering in the ceramic system's specific firing oven. Next, the specimens were embedded in acrylic resin and the surfaces to be treated were polished in a polishing machine using sandpapers of decreasing abrasion (600- to 1,200-grit) followed by felt discs with 10μm and 3μm polishing pastes and colloidal silica. The specimens were then randomly assigned to 9 groups, according to factors particle and pressure(n=6): Gr1- control; Gr2- Al 2O 3(50μm)/2.5 bar; Gr3- Al 2O 3(110μm)/2.5 bar; Gr4- SiO 2(30μm)/2.5 bar; Gr5- SiO 2(30μm)/2.5 bar; Gr6- Al 2O 3(50μm)/3.5 bar; Gr7- Al2O3(110μm)/3.5 bar; Gr8- SiO 2(30μm)/3.5 bar; Gr9- SiO 2(30μm)/3.5 bar. After treatments, surface roughness was analyzed by a digital optical profilometer and the morphology was examined by scanning electron microscopy (SEM). Data (μm) were subjected to statistical analysis by Dunnett's test (5%), two-way ANOVA and Tukey's test (5%). Results: The type of particle (p=0.0001) and the pressure (p=0.0001) used in the air abrasion protocols influenced the surface roughness values among the experimental groups (ANOVA). The mean surface roughness values (μm) obtained for the experimental groups (Gr2 to Gr9) were, respectively: 0.37 D; 0.56 BC; 0.46 BC; 0.48 CD; 0.59 BC; 0.82 A; 0.53B CD; 0.67 AB. The SEM analysis revealed that Al 2O 3, regardless of the particle size and pressure used, caused damage to the surface of the specimens, as it produced superficial damages on the ceramic, in the form of grooves and cracks. Conclusion: Al2O3 (110 μm/3.5 bar) air abrasion promoted the highest surface roughness on the ceramics, but it does not mean that this protocol promotes better ceramic-cement union compared to the other air abrasion protocols.

New trends in musical genre classification using optimum-path forest

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.

Inter-relação entre o balanço cátion-aniônico do alimento e o ph urinário de gatos

Studies for the food development of formulations for pets, look for key components to maintaining healthy way of life and safety of products, including these, elements capable of preventing the risk of certain metabolic disorders associated with diet. Feline urinarytract disorders, highlights the urolithiasis, have high incidence in clinical series. Studies linking dietary factors such as ingredients, digestibility and chemical composition, changing the volume, density and pH of urine and consequent induction training for urolithiasis. A highly significant correlation between the mineral composition of the diet and urine pH of cats began to be studied, using the association between the cation-anion balance of the diet (DCAB) and regulation of acid-base balance of the body. The DCAB can be defined as the difference between the total fixed anions and cations present in the diet, important tool for estimating the urinary pH and to determine the range of pH that favors the food used, thereby linking the trigger and the prevention of struvite and calcium oxalate urolithiasis in the urinary tract of cats. Thus, this review aims to clarify the effects of the nutritional composition of diet on urine pH in cats.

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.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

Automatic classification of fish germ cells through optimum-path forest

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques. © 2011 IEEE.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Dissimilaridade genética e análise de trilha de cultivares de soja avaliada por meio de descritores quantitativos

In addition to the evaluations among genotypes, the use of multivariate techniques enables to restrict errors, mainly concerning genetic diversity, and therefore to predict combinations with greater heterotic effect, and the greater possibility of recovery of superior genotypes. The objective of this study was to evaluate the genetic divergence between 18 soybean cultivars based on six morphological characteristics. Path analysis was performed to verify the contribution of direct and indirect characters on grain yield. The Mahalanobis distance has founded techniques of both Tocher Method and dendrogram by Single Linkage. Five different groups were formed: with nine genotypes considered similar among them; while the cultivars CEP 59, Netuno and Urano formed groups isolated by the two grouping methods. The path analysis showed that the indirect characters had little influence on grain yield, with significant direct relationship with mass of 100 grain, and cultivars Tertulha and CEP 53 standing out with grain yields above 3.7 t.ha-1.

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

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.

Efeitos do extrato de parede de levedura em dieta seca sobre a Microbiologia, ácidos graxos de cadeia curta e redução do odor das fezes de gatos adultos

The aim of this study was to evaluate the effects of yeast cell wall extract (YCW) in dry diet on the fecal microbiota, concentration of short-chain fatty acids (SCFA) and on the odor reduction of cats feces. We used 20 animals of both sexes, randomly assigned to four treatments and five repetitions totaling 20 experimental units: 1) dry commercial diet (control); 2) control + 0.2%, 3) control + 0.4%, and 4) control + 0.6% of YCW in dry matter. Enterobacteriaceae and lactic acid bacteria, fecal concentration of acetic, propionic and butyric acids, ammonia nitrogen and sensory panel were performed. There were no significant differences (p> 0.05) for bacterial counts and the concentration of SCFA and ammonia, but in sensory panel a reduction in the odor of feces could be noted with the use of 0.2% of YCW. We concluded that the addition of up to 0.6% YCW had no effect on the microbiology and the concentration of fatty acids, but there is potential for its use as an additive because of the improvement in the odor of feces. However, further studies are needed to understand the mechanisms of action and the effects of prebiotics for domestic cats.