A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05 degrees from satellite observations to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05-0.1 degrees, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~0.04 K in Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods.

A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allows turbulent properties to be obtained from studying the variation in radial velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Any bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. The reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.

PCA Tomography: how to extract information from data cubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector`s orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.

ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

Fraction of work-related accidents attributable to occupational noise in the city of Botucatu, São Paulo, Brazil

Background: Noise is the most common agent of occupational exposure. It may induce both auditory and extraauditory dysfunction and increase the risk of work accidents. The purpose of this study was to estimate the fraction of accidents attributable to noise occupational exposure in a mid-size city located in southeastern Brazil. Materials and Methods: In this population case-control study, which included 108 cases and 324 controls, the incidence rate ratio of work accidents controlled for several covariables was obtained by classifying occupational noise exposure into three levels, as well as determining the prevalence in each level. Results: Based on these data, the attributable fraction was estimated as 0.6391 (95 CI = 0.2341-0.3676), i.e., 63 of the work accidents that took place in the study site were statistically associated with occupational noise exposure. Discussion: The causes of this association as well as its implications in the prevention of work accidents are discussed.

Electrochemical noise analysis of bioleaching of bornite (Cu5FeS4) by Acidithiobacillus ferrooxidans

Electrochemical noise (EN) is a generic term describing the phenomenon of spontaneous fluctuations of potential or current noise of electrochemical systems. Since this technique provides a non-destructive condition for investigating corrosion processes, it can be useful to study the electrochemical oxidation of mineral sulfides by microorganisms, a process known as bacterial leaching of metals. This technique was utilized to investigate the dissolution of a bornite electrode in the absence (first 79 h) and after the addition of Acidithiobacillus ferrooxidans (next 113 h) in salts mineral medium at pH 1.8, without addition of the energy source (Fe2+ ions) for this chemolithotrophic bacterium. Potential and current noise data have been determined simultaneously with two identical working bornite electrodes which were linked by a zero resistance ammeter (ZRA). The mean potential, E-coup, coupling current, I-coup, standard deviations of potential and current noise fluctuations and noise resistance, R-n, have been obtained for coupled bornite electrodes. Noise measurements were recorded twice a day in an unstirred solution at 30 degrees C. Significant changes in these parameters were observed when the A. ferrooxidans suspension was added, related with bacterial activity on reduced species present in the sulfide moisture (Fe2+, S2-). ENA was a suitable tool for monitoring the changes of the corrosion behavior of bornite due to the presence of bacterium. (C) 2006 Elsevier B.V. All rights reserved.

Attributable fraction of work accidents related to occupational noise exposure in a Southeastern city of Brazil

Noise is the most frequent type of occupational exposure and can lead to both auditory and extra-auditory dysfunction as well as increasing the risk of work accidents. The purpose of this study was to estimate the attributable fraction of work accidents related to occupational noise exposure in a medium-sized city in Southeast Brazil. In this hospital-based case-control study, including 600 cases and 822 controls, the odds ratio of work accidents (controlled for several covariables) was obtained classifying occupational noise exposure into four levels and determining the prevalence at each level. Based on these data, the calculated attributable fraction was 0.3041 (95%CI: 0.2341-0.3676), i.e., 30% of work accidents in the study area were statistically associated with occupational noise exposure. The authors discuss the causes of this association and the implications for the prevention of work accidents.

Identification and filtering of uncharacteristic noise in the CMS hadron calorimeter

Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic ray muon data, calorimeter noise data, and single beam data collected with CMS in 2008. The noise rejection algorithms can be applied to LHC collision data at the trigger level or in the offline analysis. The application of the algorithms at the trigger level is shown to remove 90% of noise events with fake missing transverse energy above 100 GeV, which is sufficient for the CMS physics trigger operation. © 2010 IOP Publishing Ltd and SISSA.

The use of current transformers for noise detection due to sparking in insulators strings in high voltage transmission lines

In a general way, in an electric power utility the current transformers (CT) are used to measurement and protection of transmission lines (TL) 1 The Power Line Carriers systems (PLC) are used for communication between electrical substations and transmission line protection. However, with the increasing use of optical fiber to communication (due mainly to its high data transmission rate and low signal-noise relation) this application loses potentiality. Therefore, other functions must be defined to equipments that are still in using, one of them is detecting faults (short-circuits) and transmission lines insulator strings damages 2. The purpose of this paper is to verify the possibility of using the path to the ground offered by the CTs instead of capacitive couplings / capacitive potential transformers to detect damaged insulators, since the current transformers are always present in all transmission lines (TL's) bays. To this a comparison between this new proposal and the PLC previous proposed system 2 is shown, evaluating the economical and technical points of view. ©2010 IEEE.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Ecosystem services and urban vegetation coverage in Belém: the influence on noise pollution, air pollution and climate regulation

A ligação entre as zonas urbanas e as questões ambientais ficam mais próximas na medida em que cresce a conscientização global de conservar, melhorar e valorizar os serviços ambientais prestados pela natureza para a sustentabilidade da vida, dentro e fora da cidade. Cobertura vegetal (ou cobertura verde) está dentre as principais fontes de tais serviços. Uma vez que o processo de urbanização se mostra irreversível e os problemas ambientais urbanos se alastram em tamanho e extensão, a presença do verde está diretamente relacionada aos indicadores de qualidade de vida urbana. Como reflexo do processo de urbanização, a cidade de Belém perdeu uma grande porcentagem de seus ecossistemas naturais, de modo que este trabalho se concentrou em analisar alguns serviços ecossistêmicos—qualidade do ar, poluição do ar e regulação do clima - fornecidos pela qualidade e pela quantidade de cobertura vegetal local, considerando as alterações na distribuição espaço-temporal, em três distritos administrativos. Um marco teórico foi construído e analisado; a cobertura vegetal foi calculada, utilizando-se NDVI e Cobertura Vegetal Fracional em imagens do LANDSAT 5, ao longo de um período de 23 anos. A partir de uma proposta de escala mais detalhada de NDVI, análises quantitativas e qualitativas da cobertura verde evidenciaram perda significativa de cobertura muito densa, densa, moderada e aumento de áreas de pouca ou nenhuma vegetação. Ademais, lesão das áreas verdes sinalizou tendências de aumento da poluição do ar, da poluição sonora e da temperatura. A carência de dados relacionados ao meio ambiente não deixa dúvida sobre a urgência de investimento nos serviços ambientais provenientes da cobertura vegetal, para a sustentabilidade urbana em Belém, cujos cenários previstos são de drásticas perdas de área verde. Mais pesquisas e iniciativas de instituições públicas e privadas são necessárias para a contribuição aos serviços ambientais em Belém e, consequentemente, ao bem-estar público.

Treatment of geophysical data as a non-stationary process

ABSTRACT: The Kalman-Bucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic non-stationary. The derivation of the estimator is based on the relationship between the Kalman-Bucy and Wiener approaches for linear systems. In the present work we emphasize the criterion used, the model with apriori information, the algorithm, and the quality as related to the results. The examples are for the ideal well-log response, and the results indicate that this method can be used on a variety of geophysical data treatments, and its study clearly offers a proper insight into modeling and processing of geophysical problems.

Desenvolvimento de um aplicativo Android e de uma interface bluetooth para um dinamômetro biomédico

Pós-graduação em Engenharia Elétrica - FEIS

Association between interleukin-6 polymorphism in the-174 G/C region and hearing loss in the elderly with a history of occupational noise exposure

Introduction: The biological processes involved in noise-induced hearing loss (NIHL) are still unclear. The involvement of inflammation in this condition has been suggested.Objective: To investigate the association between interleukin - 6 (IL-6) polymorphism and susceptibility to NIHL.Methods: This was a cross-sectional study with a sample of 191 independent elderly individuals aged >60 years of age. Information on exposure to occupational noise was obtained by interviews. Audiological evaluation was performed using pure tone audiometry and genotyped through PCR by restriction fragment length polymorphism - PCR-RFLP. Data were analyzed using the chi-square test and the odds ratio (OR), with the significance level set at 5%.Results: Among elderly with hearing loss (78.0%), 18.8% had a history of exposure to occupational noise. There was a statistically significant association between the genotype frequencies of the IL-6 - 174 and NIHL. The elderly with the CC genotype were less likely to have hearing loss due to occupational noise exposure when compared to those carrying the GG genotype (OR = 0.0124; 95% CI 0.0023-0.0671; p<0.001).Conclusion: This study suggests there is an association of polymorphisms in the IL-6 gene at position - G174C with susceptibility to noise-induced hearing loss. (C) 2014 Associacao Brasileira de Otorrinolaringologia e Cirurgia Cervico-Facial. Published by Elsevier Editora Ltda. All rights reserved.