Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.

Análise de Desempenho e Recomendação para Soluções IVR

Mestrado em Engenharia Informática, Área de Especialização em Tecnologias do Conhecimento e da Decisão

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Coalition of distributed generation units to Virtual Power Players - a game theory approach

Smart Grids (SGs) have emerged as the new paradigm for power system operation and management, being designed to include large amounts of distributed energy resources. This new paradigm requires new Energy Resource Management (ERM) methodologies considering different operation strategies and the existence of new management players such as several types of aggregators. This paper proposes a methodology to facilitate the coalition between distributed generation units originating Virtual Power Players (VPP) considering a game theory approach. The proposed approach consists in the analysis of the classifications that were attributed by each VPP to the distributed generation units, as well as in the analysis of the previous established contracts by each player. The proposed classification model is based in fourteen parameters including technical, economical and behavioural ones. Depending of the VPP strategies, size and goals, each parameter has different importance. VPP can also manage other type of energy resources, like storage units, electric vehicles, demand response programs or even parts of the MV and LV distribution network. A case study with twelve VPPs with different characteristics and one hundred and fifty real distributed generation units is included in the paper.

Detecting Faces in Impoverished Images

The ability to detect faces in images is of critical ecological significance. It is a pre-requisite for other important face perception tasks such as person identification, gender classification and affect analysis. Here we address the question of how the visual system classifies images into face and non-face patterns. We focus on face detection in impoverished images, which allow us to explore information thresholds required for different levels of performance. Our experimental results provide lower bounds on image resolution needed for reliable discrimination between face and non-face patterns and help characterize the nature of facial representations used by the visual system under degraded viewing conditions. Specifically, they enable an evaluation of the contribution of luminance contrast, image orientation and local context on face-detection performance.

Autonomia da Perícia Oficial Criminal no Brasil: identificação e classificação de stakeholders

Este estudo teve por objetivo identificar e classificar os stakeholders que influenciam e/ou são influenciados pela perícia oficial de natureza criminal e que, direta ou indiretamente, impactam a autonomia desse arranjo estatal, bem como, obter um conjunto de ações aos peritos oficiais como forma de exercer algum grau de influência no processo de autonomia. Foi abordada a Teoria dos Stakeholders e destacadas algumas propostas de classificação de partes interessadas. Foi eleita a proposta de classificação desenvolvida por Mainardes, Alves, et al. em trabalho apresentado no V Encontro de Estudos em Estratégia/3Es, realizado pela Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD), na cidade de Porto Alegre/RS, em maio de 2013, com o intuito de executar o teste empírico recomendado pelos autores da nova proposta de classificação. Os stakeholders foram identificados como resultado de entrevista coletiva a grupo focal composto por seis peritos oficiais criminais. Para fins de aplicação do teste empírico do modelo da proposta de classificação de stakeholders de Mainardes, Alves, et al. foi considerado um contexto empírico onde a organização, para fins da Teoria dos Stakeholders e deste trabalho, é a perícia oficial criminal e sua respectiva autonomia. Como resultado desse teste empírico, os stakeholders identificados e entrevistados foram classificados em dependente, passivo, parceiro, controlador ou regulador. Aqueles não abrangidos pela classificação são tidos como não stakeholders. Por fim, foi sugerido por esses stakeholders entrevistados um conjunto de ações aos peritos oficiais criminais como forma de exercerem algum grau de influência no processo de autonomia da perícia oficial de natureza criminal.

Modelo de classificação para pequenas empresas supermercadistas quanto ao uso de técnicas de prevenção de perdas: um estudo de caso

The objective of this work is to draw attention to the importance of use of techniques of loss prevention in small retail organization, analyzing and creating a classification model related to the use of these in companies. This work identifies the fragilities and virtues of companies and classifies them relating the use of techniques of loss prevention. The used methodology is based in a revision of the available literature on measurements and techniques of loss prevention, analyzing the processes that techniques needed to be adopted to reduce losses, approaching the "pillars" of loss prevention, the cycle life of products in retail and cycles of continues improvement in business. Based on the objectives of this work and on the light of researched techniques, was defined the case study, developed from a questionnaire application and the researcher's observation on a net of 16 small supermarkets. From those studies a model of classification of companies was created. The practical implications of this work are useful to point mistakes in retail administration that can become losses, reducing the profitability of companies or even making them impracticable. The academic contribution of this study is a proposal of an unpublished model of classification for small supermarkets based on the use of techniques of loss prevention. As a result of the research, 14 companies were classified as Companies with Minimum Use of Loss Prevention Techniques - CMULPT, and 02 companies were classified as Companies with Deficient Use of Loss Prevention Techniques - CDULPT. The result of the research concludes that on average the group was classified as being Companies with Minimum Use of Techniques of Prevention of Losses EUMTPP, and that the companies should adopt a program of loss prevention focusing in the identification and quantification of losses and in a implantation of a culture of loss prevention

Diagnosis of inflammatory lesions by high-wavenumber FT-Raman spectroscopy

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Informação polarimétrica PALSAR/ALOS aplicada à discriminação de espécies e estimação de parâmetros morfológicos de macrófitas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação de biomarcadores moleculares em mulheres com câncer de ovário

Pós-graduação em Ginecologia, Obstetrícia e Mastologia - FMB

Classificação de tábuas de madeira usando processamento de imagens digitais e aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Classificação multicritério de peças de reposição

Pós-graduação em Engenharia Mecânica - FEG

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.