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The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm. We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategies.

This work presents N-BEATS-RNN, an extended version of an existing ensemble of deep learning networks for time series forecasting, N-BEATS. We apply a state-of-the-art Neural Architecture Search, based on a fast and efficient weight-sharing search, to solve for an ideal Recurrent Neural Network architecture to be added to N-BEATS. We evaluated the proposed N-BEATS-RNN architecture in the widely-known M4 competition dataset, which contains 100,000 time series from a variety of sources. N-BEATS-RNN achieves comparable results to N-BEATS and the M4 competition winner while employing solely 108 models, as compared to the original 2,160 models employed by N-BEATS, when composing its final ensemble of forecasts. Thus, N-BEATS-RNN’s biggest contribution is in its training time reduction, which is in the order of 9x compared with the original ensembles in N-BEATS.

The recent growing size of datasets requires scalability of data mining algorithms, such as clustering algorithms. The MapReduce programing model provides the scalability needed, alongside with portability as well as automatic data safety and management. k-means is one of the most popular algorithms in data mining and can be easily adapted to the MapReduce model. Nevertheless, k-means has drawbacks, such as the need to provide the number of clusters (k) in advance and the sensitivity of the algorithm to the initial cluster prototypes. This paper presents two evolutionary scalable metaheuristics in MapReduce that automatically seek the solution with the optimal number of clusters and best clustering structure for scalable datasets. The first consists in an algorithm able to iteratively enhance k-means clusterings through evolutionary operators designed to handle distributed data. The second consists in applying evolutionary k-means to cluster each distributed portion of a dataset in an independent way, combining the obtained results into an ensemble afterwards. The proposed techniques are compared asymptotically and experimentally with other state-of-the-art clustering algorithms also developed in MapReduce. The results are analyzed by statistical tests and show that the first proposed metaheuristic yielded results with the best quality, while the second achieved the best computing times.

Autoimmune diseases are the third highest cause of mortality in the world, and the identification of an anti-nuclear antibody via an immunofluorescence test for HEp-2 cells is a standard procedure to support diagnosis. In this work, we assess the performance of six preprocessing strategies and five state-of-the-art convolutional neural network architectures for the classification of HEp-2 cells. We also evaluate enhancement methods such as hyperparameter optimization, data augmentation, and fine-tuning training strategies. All experiments were validated using a five-fold cross-validation procedure over the training and test sets. In terms of accuracy, the best result was achieved by training the Inception-V3 model from scratch, without preprocessing and using data augmentation (98.28%). The results suggest the conclusions that most CNNs perform better on non-preprocessed images when trained from scratch on the analyzed dataset, and that data augmentation can improve the results from all models. Although fine-tuning training did not improve the accuracy compared to training the CNNs from scratch, it successfully reduced the training time.

A key challenge for document clustering consists in finding a proper similarity measure for text documents that enables the generation of cohesive groups. Measures based on the classic bag-of-words model take into account solely the presence (and frequency) of words in documents. In doing so, semantically similar documents which use different vocabularies may end up in different clusters. For this reason, semantic similarity measures that use external knowledge, such as word n-gram corpora or thesauri, have been proposed in the literature. In this paper, the Frequency Google Tri-gram Measure is proposed to assess similarity between documents based on the frequencies of terms in the compared documents as well as the Google n-gram corpus as an additional semantic similarity source. Clustering algorithms are applied to several real datasets in order to experimentally evaluate the quality of the clusters obtained with the proposed measure and compare it with a number of state-of-the-art measures from the literature. The experimental results demonstrate that the proposed measure improves significantly the quality of document clustering, based on statistical tests. We further demonstrate that clustering results combining bag-of-words and semantic similarity are superior to those obtained with either approach independently.

Dealing with distributed data is one of the challenges for clustering, as most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential data mining algorithms for being simple, scalable, and easily modifiable to a variety of contexts and application domains. However, exact distributed versions of k-means are still sensitive to the selection of the initial cluster prototypes and require the number of clusters to be specified in advance. Additionally, preserving data privacy among repositories may be a complicating factor. In order to overcome k-means limitations, two different approaches were adopted in this paper: the first obtains a final model identical to the centralized version of the clustering algorithm and the second generates and selects clusters for each distributed data subset and combines them afterwards. It is also described how to apply the algorithms compared while preserving data privacy. The algorithms are compared experimentally from two perspectives: the theoretical one, through asymptotic complexity analyses, and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests. The results obtained indicate which algorithm is more suitable for each application scenario.

Autoimmune diseases are the third cause of mortality in the world. A conventional method to support the diagnosis of Autoimmune diseases is the identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2). In the present work, a new evaluation of the Convolutional Neural Networks (CNNs) LeNet-5, AlexNet, and GoogLeNet is made for such task. Here, new validation techniques and a variety of CNNs' hyper-parameters values are considered. We also assess several pre-processing strategies in order to evaluate these CNNs. Moreover, our work presents an analysis of optimization of training hyper-parameters, which can affect the convergence of cost function, the learning speed and the classification performance. Our best results were achieved by GoogLeNet architecture trained with images with contrast stretching and average subtraction resulting in 95.53% of accuracy, with initial learning rate in 0.001 and gamma factor in 0.5.

Autoimmune diseases are the third cause of mortality in the world. The identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2) is a conventional method to support the diagnosis of such diseases. In the present work, three popular Convolutional Neural Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and GoogLeNet. We also assess the impact of six different pre-processing strategies on the performance of these CNNs. Additionally, data augmentation based on the rotation of the training set images after the pre-processing strategies was evaluated. Our work is the first to consider AlexNet and GoogLeNet models for the proposed analysis and classification of HEp-2 cells images, besides the LeNet-5. Experimental results allow to conclude that neither pre-processing strategies were essential to improve accuracy values of the CNNs. However, when data augmentation is considered, contrast enhancement followed by data centralization is significant in order to achieve good results. Additionally, our results were compared with results from other state-of-art papers. Our best results were achieved by GoogLeNet architecture trained with images with no pre-processing and no data augmentation, resulting in 98.17% of accuracy, which outperforms the results presented in other works in literature.

One of the challenges for clustering resides in dealing with data distributed in separated repositories, because most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential data mining algorithms for being simple, scalable and easily modifiable to a variety of contexts and application domains. Although distributed versions of k-means have been proposed, the algorithm is still sensitive to the selection of the initial cluster prototypes and requires the number of clusters to be specified in advance. In this paper, we propose the use of evolutionary algorithms to overcome the k-means limitations and, at the same time, to deal with distributed data. Two different distribution approaches are adopted: the first obtains a final model identical to the centralized version of the clustering algorithm; the second generates and selects clusters for each distributed data subset and combines them afterwards. The algorithms are compared experimentally from two perspectives: the theoretical one, through asymptotic complexity analyses; and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests. The obtained results indicate which variant is more adequate for each application scenario.

Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, market segmentation, document categorization, and web mining. The need for organizing data into categories of similar objects has made the task of clustering very important to these domains. In this context, there has been an increasingly interest in the study of evolutionary algorithms for clustering, especially those algorithms capable of finding blurred clusters that are not clearly separated from each other. In particular, a number of evolutionary algorithms for fuzzy clustering have been addressed in the literature. This chapter has two main contributions. First, it presents an overview of evolutionary algorithms designed for fuzzy clustering. Second, it describes a fuzzy version of an evolutionary algorithm for clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presented. These experiments reveal interesting aspects of the effect of an important parameter found in many evolutionary algorithms for clustering, namely, the number of iterations of a given local search procedure to be performed at each generation.

Genetic Algorithms (GAs) have been successfully applied to several complex data analysis problems in a wide range of domains, such as image processing, bioinformatics, and crude oil analysis. The need for organizing data into categories of similar objects has made the task of clustering increasingly important to those domains. In this chapter, the authors present a survey of the use of GAs for clustering applications. A variety of encoding (chromosome representation) approaches, fitness functions, and genetic operators are described, all of them customized to solve problems in such an application context.

Neural networks are a tool to solve prediction problems that have gained much prominence recently. In general, neural networks are used as a predictive method, that is, their are used to estimate a regression function. Instead, this work presents the use of neural networks as an optimization tool to combine existing regression estimators in order to obtain more accurate predictions and to fit local linear models more efficiently. Several tests were conducted to show the greater efficiency of these methods when compared to the usual ones. Redes neurais são uma ferramenta para resolver problemas de predição que ganharam muito destaque recentemente. Em geral, redes neurais são utilizados como um método preditivo, ou seja, estimando uma função de regressão. Este trabalho, no entanto, apresenta o uso de redes neurais como uma ferramenta de otimização para combinar estimadores de regressão já existentes de modo a obter predições mais precisas e ajustar modelos lineares locais de forma mais eficiente. Vários testes foram conduzidos para mostrar a maior eficiência desses métodos quando comparados aos usuais.