Automatic classification of enzyme family in protein annotation

Most of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process - thus freeing the specialist to carry out more valuable tasks - has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function. © 2009 Springer Berlin Heidelberg.

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.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Portable Hardness tester for timber classification

Hardness is a property largely used in material specifications, mechanical and metallurgical research and quality control of several materials. Specifically for timber, Janka hardness is a simple, quick and easy test, with good correlations with the compression parallel to grain strength, a strong reference in structural classification for this material. More recently, international studies have reported the use of Brinell hardness for timber assessment which resumes the advantages previously mentioned for Janka hardness and make it easier to be performed in the field, especially because of the lower magnitude of the involved loads. A first generation of an equipment for field evaluation of hardness in wood - Portable Hardness tester for wood - based on Brinell hardness has already been developed by the Research Group on Forest Products from FCA/UNESP, Brazil, with very good correlations between the evaluated hardness and several other mechanical properties of the material when performing tests with different species of native and reforested wood (traditionally used as ties - sleepers - in railways). This paper presents results obtained in the experimental program with the first generation of this equipment and preliminary tests with its second generation, which uses accelerometers to substitute the indentation measurements in wood. For the first generation of the equipment functional and calibration tests were carried out using 16 native and reforestation timber lots, among there E. citriodora, E. tereticornis, E. saligna, E. urophylla, E. grandis, Goupia glabra and Bagassa guianenses, with different origins and ages. The results obtained confirm its potential in the classification of specimens, with inclusion errors varying from 4.5% to 16.6%.

Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.

Is it possible to make pixel-based radar image classification user-friendly?

In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.

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.

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.

The evaluation of an instrument for pediatric oncology patient classification

The objectives of this study were to assess the interrater reproducibility of the instrument to classify pediatric patients with cancer; verify the adequacy of the patient classification instrument for pediatric patients with cancer; and make a proposal for changing the instrument, thus allowing for the necessary adjustments for pediatric oncology patients. A total of 34 pediatric inpatients of a Cancer Hospital were evaluated by the teams of physicians, nurses and nursing technicians. The Kappa coefficient was used to rate the agreement between the scores, which revealed a moderate to high value in the objective classifications, and a low value in the subjective. In conclusion, the instrument is reliable and reproducible, however, it is suggested that to classify pediatric oncology patients, some items should be complemented in order to reach an outcome that is more compatible with the reality of this specific population.

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.

Delineation of specific management areas for coffee cultivation based on the soil-relief relationship and numerical classification

Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

Estudio sobre la clasificación económica y social de los países de América Latina = Study of the economic and social classification of the Latin American countries

Incluye Bibliografía

Classification of crucifer cultivars based on the life-history of diamondback moth (Plutella xylostella)

We compared different cultivars and hybrids of crucifers in relation to development and life-history of diamondback moth (Plutella xylostella) to classify the plants according to their resistance to the pest. The plants used were Manteiga da Geórgia kale, Bola de Neve cauliflower, Ramoso Piracicaba Precoce broccoli, Chato-de-quintal cabbage, and the hybrid cabbages Midori, TPC668, TPC308, and TPC681. We evaluated performance daily until the pupal stage. Pupae were assessed individually to determine the pupal weight, performance, and pupal period. We determined the sex ratio, fecundity, fertility, and longevity of the emerged adults and calculated their reproductive potential. Cabbage hybrids TPC668, TPC308, and TPC681 do not support the development and reproduction of the diamondback moth. These hybrids show a level of resistance that is similar to that found the commercially available hybrid Midori and cultivar Chato de Quintal, which are known to be resistant to the diamondback moth. This finding implies that the capitata (cabbage) cultivars are the most suitable for planting because they are more resistant to pest than the cultivar's moth, acephala (kale). © 2013 Copyright Taylor and Francis Group, LLC.

Unfolding physics from the algebraic classification of spinor fields

After reviewing the Lounesto spinor field classification, according to the bilinear covariants associated to a spinor field, we call attention and unravel some prominent features involving unexpected properties about spinor fields under such classification. In particular, we pithily focus on the new aspects - as well as current concrete possibilities. They mainly arise when we deal with some non-standard spinor fields concerning, in particular, their applications in physics. © 2012 Elsevier B.V.