Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation.

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

Cladistic analysis of the Neotropical genera Cerqueirellum Py-Daniel, 1983, Coscaroniellum Py-Daniel, 1983 and Shelleyellum Py-Daniel & Pessoa, 2005 (Diptera: Simuliidae)

Females of simuliid black flies are haematophagous insects and vectors of several pathogenic agents of human diseases such as the filarial worms Mansonella ozzardi and Onchocerca volvulus. The genus Cerqueirellum is one of the most important groups of vectors of mansonellosis and onchocerciasis diseases in South America, and the genera Coscaroniellum and Shelleyellum are phylogenetically close to Cerqueirellum. There is not yet an agreement among authors about the generic classification of the species which compose these three genera, being all lumped by some taxonomists within Psaroniocompsa. A cladistic analysis of all species of Coscaroniellum, Cerqueirellum, and Shelleyellum, based on 41 morphological characters were done. Species closely related to Cerqueirellum were included in the analysis. The genera Cerqueirellum, Coscaroniellum and Shelleyellum were demonstrated as consistent basal entities and well-defined monophyletic clades.

Ethnoveterinary knowledge of the inhabitants of Marajó Island, Eastern Amazonia, Brazil

There have been ethnoveterinary reports from around the world investigating plant usage in therapeutic protocols; however, there is no information regarding the ethnoveterinary practices in Brazilian Amazonia. The objective of this work was to register and document the ethnoveterinary knowledge of the inhabitants of the Island of Marajó, eastern Amazonia, Brazil. In the study, interviews were conducted with 50 individuals, with the application of semi-structured questionnaires that were quantitatively analyzed using descriptive statistic methods of frequency distribution. Use-value was calculated to determine the most important species. Samples of plants that were reported to have medicinal value were collected and identified by botanical classification. Fifty plants, distributed among 48 genera and 34 families, were indicated for 21 different medicinal uses. The family Asteraceae had the largest number of reported species; Carapa guianensis Aubl., Copaifera martii Hayne, Crescentia cujete L., Caesalpinia ferrea Mart., Chenopodium ambrosioides L., Jatropha curcas L. and Momordica charantia L. were species with highest use- value. The plant parts that were more commonly utilized for the preparation of ethnoveterinary medicines were the leaves (56%), bark (18%), roots (14%), seeds (14%) and fruit (8%). With regard to usage, tea was reported as a usage method by 56% of the informants; most preparations (90.9%) utilized only a single plant. In addition to medicinal plants, informants reported using products of animal and mineral origin. The present study contributed to the construction of an inventory of Marajó Island's ethnoveterinary plants, which might be the basis for future scientific validation studies.

Time-variations of equivalent water heights'from Grace Mission and in-situ river stages in the Amazon basin

Gravity Recovery and Climate Experiment (GRACE) mission is dedicated to measuring temporal variations of the Earth's gravity field. In this study, the Stokes coefficients made available by Groupe de Recherche en Géodésie Spatiale (GRGS) at a 10-day interval were converted into equivalent water height (EWH) for a ~4-year period in the Amazon basin (from July-2002 to May-2006). The seasonal amplitudes of EWH signal are the largest on the surface of Earth and reach ~ 1250mm at that basin's center. Error budget represents ~130 mm of EWH, including formal errors on Stokes coefficient, leakage errors (12 ~ 21 mm) and spectrum truncation (10 ~ 15 mm). Comparison between in situ river level time series measured at 233 ground-based hydrometric stations (HS) in the Amazon basin and vertically-integrated EWH derived from GRACE is carried out in this paper. Although EWH and HS measure different water bodies, in most of the cases a high correlation (up to ~80%) is detected between the HS series and EWH series at the same site. This correlation allows adjusting linear relationships between in situ and GRACE-based series for the major tributaries of the Amazon river. The regression coefficients decrease from up to down stream along the rivers reaching the theoretical value 1 at the Amazon's mouth in the Atlantic Ocean. The variation of the regression coefficients versus the distance from estuary is analysed for the largest rivers in the basin. In a second step, a classification of the proportionality between in situ and GRACE time-series is proposed.

Relation between oral health and socioeconomic variables among schoolchildren aged 12 in the City of Manaus - AM

Studies have shown that the age of 12 was determined as the age of global monitoring of caries for international comparisons and monitoring of disease trends. The aimed was to evaluate the prevalence of dental caries, fluorosis and periodontal condition and their relation with socioeconomic factors among schoolchildren aged twelve in the city of Manaus, AM. This study with a probabilistic sample of 661 children was conducted, 609 from public and 52 from private schools, in 2008. Dental caries, periodontal condition and dental fluorosis were evaluated. In order to obtain the socioeconomic classification of each child (high, upper middle, middle, lower middle, low and lower low socioeconomic classes), the guardians were given a questionnaire. The mean decayed teeth, missing teeth, and filled teeth (DMFT) found at age twelve was 1.89. It was observed that the presence of dental calculus was the most severe periodontal condition detected in 39.48%. In relation to dental fluorosis, there was a low prevalence in the children examined, i.e., the more pronounced lines of opacity only occasionally merge, forming small white areas. The study showed a significant association of 5% among social class with dental caries and periodontal condition. In schoolchildren of Manaus there are low mean of DMFT and fluorosis, but a high occurrence of gingival bleeding.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Spatial and temporal evolution of family-farming land use in the Tapajós region of the Brazilian Amazon

Pressures on the Brazilian Amazon forest have been accentuated by agricultural activities practiced by families encouraged to settle in this region in the 1970s by the colonization program of the government. The aims of this study were to analyze the temporal and spatial evolution of land cover and land use (LCLU) in the lower Tapajós region, in the state of Pará. We contrast 11 watersheds that are generally representative of the colonization dynamics in the region. For this purpose, Landsat satellite images from three different years, 1986, 2001, and 2009, were analyzed with Geographic Information Systems. Individual images were subject to an unsupervised classification using the Maximum Likelihood Classification algorithm available on GRASS. The classes retained for the representation of LCLU in this study were: (1) slightly altered old-growth forest, (2) succession forest, (3) crop land and pasture, and (4) bare soil. The analysis and observation of general trends in eleven watersheds shows that LCLU is changing very rapidly. The average deforestation of old-growth forest in all the watersheds was estimated at more than 30% for the period of 1986 to 2009. The local-scale analysis of watersheds reveals the complexity of LCLU, notably in relation to large changes in the temporal and spatial evolution of watersheds. Proximity to the sprawling city of Itaituba is related to the highest rate of deforestation in two watersheds. The opening of roads such as the Transamazonian highway is associated to the second highest rate of deforestation in three watersheds.

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

Some notes on a historical perspective of panic disorder

This article aims to describe important points in the history of panic disorder concept, as well as to highlight the importance of its diagnosis for clinical and research developments. Panic disorder has been described in several literary reports and folklore. One of the oldest examples lies in Greek mythology - the god Pan, responsible for the term panic. The first half of the 19th century witnessed the culmination of medical approach. During the second half of the 19th century came the psychological approach of anxiety. The 20th century associated panic disorder to hereditary, organic and psychological factors, dividing anxiety into simple and phobic anxious states. Therapeutic development was also observed in psychopharmacological and psychotherapeutic fields. Official classifications began to include panic disorder as a category since the third edition of the American Classification Manual (1980). Some biological theories dealing with etiology were widely discussed during the last decades of the 20th century. They were based on laboratory studies of physiological, cognitive and biochemical tests, as the false suffocation alarm theory and the fear network. Such theories were important in creating new diagnostic paradigms to modern psychiatry. That suggests the need to consider a wide range of historical variables to understand how particular features for panic disorder diagnosis have been developed and how treatment has emerged.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação