Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimativa de ocorrência de precipitação em áreas agrícolas utilizando floresta de caminhos ótimos

As condições meteorológicas são determinantes para a produção agrícola; a precipitação, em particular, pode ser citada como a mais influente por sua relação direta com o balanço hídrico. Neste sentido, modelos agrometeorológicos, os quais se baseiam nas respostas das culturas às condições meteorológicas, vêm sendo cada vez mais utilizados para a estimativa de rendimentos agrícolas. Devido às dificuldades de obtenção de dados para abastecer tais modelos, métodos de estimativa de precipitação utilizando imagens dos canais espectrais dos satélites meteorológicos têm sido empregados para esta finalidade. O presente trabalho tem por objetivo utilizar o classificador de padrões floresta de caminhos ótimos para correlacionar informações disponíveis no canal espectral infravermelho do satélite meteorológico GOES-12 com a refletividade obtida pelo radar do IPMET/UNESP localizado no município de Bauru, visando o desenvolvimento de um modelo para a detecção de ocorrência de precipitação. Nos experimentos foram comparados quatro algoritmos de classificação: redes neurais artificiais (ANN), k-vizinhos mais próximos (k-NN), máquinas de vetores de suporte (SVM) e floresta de caminhos ótimos (OPF). Este último obteve melhor resultado, tanto em eficiência quanto em precisão.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

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.

Performance evaluation of the fuzzy ARTMAP for network intrusion detection

Recently, considerable research work have been conducted towards finding fast and accurate pattern classifiers for training Intrusion Detection Systems (IDSs). This paper proposes using the so called Fuzzy ARTMAT classifier to detect intrusions in computer network. Our investigation shows, through simulations, how efficient such a classifier can be when used as the learning mechanism of a typical IDS. The promising evaluation results in terms of both detection accuracy and training duration indicate that the Fuzzy ARTMAP is indeed viable for this sort of application.

Optimum-Path Forest Classifier for Large Scale Biometric Applications

This paper addresses biometric identification using large databases, in particular, iris databases. In such applications, it is critical to have low response time, while maintaining an acceptable recognition rate. Thus, the trade-off between speed and accuracy must be evaluated for processing and recognition parts of an identification system. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. The existing Gauss-Laguerre Wavelet based coding scheme is used for iris encoding. The performance of the OPF and two other - Hamming and Bayesian - classifiers, is compared using small, medium, and large-scale databases. Such a comparison shows that the OPF has faster response for large-scale databases, thus performing better than the more accurate, but slower, classifiers.

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Longitudinal distribution and lateral pattern of megalopal settlement and juvenile recruitment of Carcinus maenas (L.) (Brachyura, Portunidae) in the Mira River Estuary, Portugal

Settlement is a critical process in the life history of crabs, and thus affecting the abundance, distribution and structure of estuarine communities. The spatial pattern of settlement of megalopae of the shore crab Carcinus maenas along a longitudinal estuarine gradient (Mira River Estuary, Portugal) was examined, as well as its effects on the juvenile population. To measure megalopal settlement, four replicate collectors were deployed in six equally spaced stations along the estuarine axis. Juveniles were collected on the same locations with a quadrat randomly deployed on the substrate. To assess fine-scale megalopal settlement within a curved region of the estuary, replicate collectors were deployed on both margins along Moinho da Asneira curve. Megalopae settled differently along the six longitudinal points, with a tendency to attenuate their settlement upstream. Within the curved region, megalopae preferentially settled on the left margin collectors, probably due to the weaker velocity speeds felt on this margin. Concerning the overall juvenile density, there were significant differences among the stations distributed along the estuary, but they did no reflect a longitudinal dispersion attenuation pattern. Size-frequency distribution of the juvenile population showed that the average size is higher on the left margin. Recruits (carapace length between 1.0 mm and 3.4 mm) were more abundant on the upstream stations. Density of early juveniles (3.4 mm-6.5 mm) and juveniles (6.5 mm-10 mm) was more stable throughout the estuary axis than that of recruits. This distribution pattern may result from tidal excursion processes or mechanisms to avoid biotic interactions, such as predation and competition. (c) 2006 Elsevier Ltd. All rights reserved.

Determination of the highest no-effect dose (HNED) and of the elimination pattern for cocaine in horses

Cocaine is one of the most widespread illegal stimulants utilized by the human population throughout the world. The aim of this study was to establish the highest no-effect dose (HNED) of cocaine on the spontaneous locomotor activity (SLA) of horses in a behavior chamber, and thereby to determine the maximal acceptable threshold of the urinary drug concentration in horses. Twelve English thoroughbred mares received 0.02, 0.03, 0.04, 0.08 or 0.12 mg kg(-1) cocaine i.v. or saline solution (control). It was noted that doses above 0.04 mg kg(-1) induced a significant increase in SLA (P < 0.05, Tukey's test). No significant increase in SLA was seen in the mares that received 0.03 mg kg(-1), but the animals showed important behavioral changes that did not occur after the 0.02 mg kg(-1) dose. It was concluded that the HNED of cocaine for horses in a behavior chamber is 0.02 mg kg(-1). After injection of this dose in five horses, urine samples were collected at predetermined intervals through vesical catheterization. The concentrations of cocaine, norcocaine, benzoylecgonine and ecgonine methyl ester were quantified by liquid chromatography/electrospray ionization tandem mass spectrometry. Cocaine and norcocaine concentrations remained consistently below the level of detection. Benzoylecgonine reached a mean (+/- SEM) maximum concentration of 531.9 +/- 168.7 ng ml(-1) after 4 h, whereas ecgonine methyl ester peaked 2 h after injection at a concentration of 97.2 +/- 26.5 ng ml(-1). The maximum admissible concentration for cocaine and/or metabolites in the urine of horses is difficult to establish unequivocally because of the substantial individual variation in the drug elimination pattern observed in horses, which can be inferred by the large standard error of the means obtained. Copyright (C) 2002 John Wiley Sons, Ltd.

Annual pattern of plasma melatonin and progesterone concentrations in hair and wool ewe lambs kept under natural photoperiod at lower latitudes in the southern hemisphere

To study the annual pattern of plasma melatonin and progesterone concentrations in hair [Santa Ines (SI)] and wool [Romney Marsh (RM) and Suffolk (SU)] ewe lambs kept under natural photoperiods at 21 degrees 59'S, 12 ewe lambs (four/breed) were used. For melatonin, blood samples were collected monthly throughout the year at the onset (17:00, 19:00 and 21:00 hr) and end (04:00, 06:00 and 08:00 hr) of the night, and for progesterone the samples were collected in the morning, two to three times a week throughout the year. Plasma melatonin concentrations at different times of the day changed according to the season. In diurnal periods (17:00 and 8:00 hr) no seasonal differences were observed but they became evident in the nocturnal intervals (21:00 and 4:00 hr) and transitional night-day (6:00 hr) times. The patterns of melatonin secretion were higher in winter and autumn than in spring and summer. The patterns of plasma progesterone secretion were affected by interaction between breed and season. There was no seasonal variation in plasma progesterone concentrations for SI females. The progesterone pattern for RM and SU females varied with season. The plasma levels were higher in autumn and winter than in spring and summer. At 21 degrees 59'S hair and wool ewe lambs showed the same annual pattern of plasma melatonin concentration while the annual progesterone profiles were quite different. For SI females this pattern was constant along all seasons and for RM and SU females this pattern was higher during autumn and winter than spring and summer.

The breathing pattern and the ventilatory response to aquatic and aerial hypoxia and hypercarbia in the frog Pipa carvalhoi

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

Immune response pattern of the popliteal lymph nodes of dogs with visceral leishmaniasis

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

Soil sampling intensity and spatial distribution pattern of soils attributes and corn yield in no-tillage system

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