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Training machine learning models for predicting stock market share prices is an active area of research since the automatization of trading such papers was available in real time. While most of the work in this field of research is done by training Neural networks based on past prices of stock shares, in this work, we use iFeel 2.0 platform to extract 19 sentiment features from posts obtained from microblog platform Twitter that mention the company Petrobras. Then, we used those features to train XBoot models to predict future stock prices for the referred company. Later, we simulated the trading of Petrobras' shares based on the model's outputs and determined the gain of R$88,82 (net) in a 250-day period when compared to a 100 random models' average performance., Comment: in Portuguese language, Presented at: 1o Semin\'ario de Ci\^encia de Dados do IFSP. Campinas: 2023

This systematic review focuses on analyzing the use of machine learning techniques for identifying and quantifying analytes in various electrochemical applications, presenting the available applications in the literature. Machine learning is a tool that can facilitate the analysis and enhance the understanding of processes involving various analytes. In electrochemical biosensors, it increases the precision of medical diagnostics, improving the identification of biomarkers and pathogens with high reliability. It can be effectively used for the classification of complex chemical products; in environmental monitoring, using low-cost sensors; in portable devices and wearable systems; among others. Currently, the analysis of some analytes is still performed manually, requiring the expertise of a specialist in the field and thus hindering the generalization of results. In light of the advancements in artificial intelligence today, this work proposes to carry out a systematic review of the literature on the applications of artificial intelligence techniques. A set of articles has been identified that address electrochemical problems using machine learning techniques, more specifically, supervised learning., Comment: in Portuguese language

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity. We analyze the risks that may come along with rapid technological progress and future trends in AI, an area that is on the path to becoming a general-purpose technology, just like electricity, which revolutionized society in the 19th and 20th centuries. A provocativa compara\c{c}\~ao entre IA e eletricidade, feita pelo cientista da computa\c{c}\~ao e empreendedor Andrew Ng, resume a profunda transforma\c{c}\~ao que os recentes avan\c{c}os em Intelig\^encia Artificial (IA) t\^em desencadeado no mundo. Este cap\'itulo apresenta uma vis\~ao geral pela paisagem em constante evolu\c{c}\~ao da IA. Sem pretens\~oes de exaurir o assunto, exploramos as aplica\c{c}\~oes que est\~ao redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos que acompanham o r\'apido progresso tecnol\'ogico e as tend\^encias futuras da IA, \'area que trilha o caminho para se tornar uma tecnologia de prop\'osito geral, assim como a eletricidade, que revolucionou a sociedade dos s\'eculos XIX e XX., Comment: In Portuguese

Pressure ulcers have high prevalence in ICU patients but are preventable if identified in initial stages. In practice, the Braden scale is used to classify high-risk patients. This paper investigates the use of machine learning in electronic health records data for this task, by using data available in MIMIC-III v1.4. Two main contributions are made: a new approach for evaluating models that considers all predictions made during a stay, and a new training method for the machine learning models. The results show a superior performance in comparison to the state of the art; moreover, all models surpass the Braden scale in every operating point in the precision-recall curve. -- -- Les\~oes por press\~ao possuem alta preval\^encia em pacientes de UTI e s\~ao preven\'iveis ao serem identificadas em est\'agios iniciais. Na pr\'atica utiliza-se a escala de Braden para classifica\c{c}\~ao de pacientes em risco. Este artigo investiga o uso de aprendizado de m\'aquina em dados de registros eletr\^onicos para este fim, a partir da base de dados MIMIC-III v1.4. S\~ao feitas duas contribui\c{c}\~oes principais: uma nova abordagem para a avalia\c{c}\~ao dos modelos e da escala de Braden levando em conta todas as predi\c{c}\~oes feitas ao longo das interna\c{c}\~oes, e um novo m\'etodo de treinamento para os modelos de aprendizado de m\'aquina. Os resultados obtidos superam o estado da arte e verifica-se que os modelos superam significativamente a escala de Braden em todos os pontos de opera\c{c}\~ao da curva de precis\~ao por sensibilidade., Comment: 3 pages, 1 figure, in Portuguese, accepted at XVIII Congresso Brasileiro de Inform\'atica em Sa\'ude (CBIS 2021)

A basic task for most Legal Artificial Intelligence (Legal AI) applications is Named Entity Recognition (NER). However, texts produced in the context of legal practice make references to entities that are not trivially recognized by the currently available NERs. There is a lack of categorization of legislation, jurisprudence, evidence, penalties, the roles of people in a legal process (judge, lawyer, victim, defendant, witness), types of locations (crime location, defendant's address), etc. In this sense, there is still a need for a robust golden collection, annotated with fine-grained entities of the legal domain, and which covers various documents of a legal process, such as petitions, inquiries, complaints, decisions and sentences. In this article, we describe the development of the Golden Collection of the Brazilian Judiciary (CDJUR-BR) contemplating a set of fine-grained named entities that have been annotated by experts in legal documents. The creation of CDJUR-BR followed its own methodology that aimed to attribute a character of comprehensiveness and robustness. Together with the CDJUR-BR repository we provided a NER based on the BERT model and trained with the CDJUR-BR, whose results indicated the prevalence of the CDJUR-BR., Comment: 15 pages, in Portuguese language, 3 figures, 5 tables

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results, Comment: in Portuguese language. CIACA - Conferencia Ibero-Americana Computa\c{c}\~ao Aplicada 2022 Proceedings

Mapping structures such as settlements, roads, individual houses and any other types of artificial structures is of great importance for the analysis of urban growth, masking, image alignment and, especially in the studied use case, the definition of Fuel Management Networks (FGC), which protect buildings from forest fires. Current cartography has a low generation frequency and their resolution may not be suitable for extracting small structures such as small settlements or roads, which may lack forest fire protection. In this paper, we use time series data, extracted from Sentinel-1 and 2 constellations, over Santar\'em, Ma\c{c}\~ao, to explore the detection of permanent structures at a resolution of 10 by 10 meters. For this purpose, a XGBoost classification model is trained with 133 attributes extracted from the time series from all the bands, including normalized radiometric indices. The results show that the use of time series data increases the accuracy of the extraction of permanent structures when compared using only static data, using multitemporal data also increases the number of detected roads. In general, the final result has a permanent structure mapping with a higher resolution than state of the art settlement maps, small structures and roads are also more accurately represented. Regarding the use case, by using our final map for the creation of FGC it is possible to simplify and accelerate the process of delimitation of the official FGC., Comment: 12 pages, in Portuguese, 7 figures, conference: INForum 2019

This work investigates the use of two-stage clustering methods. Four techniques were proposed: SOMK, SOMAK, ASCAK and SOINAK. SOMK is composed of a SOM (Self-Organizing Maps) followed by the K-means algorithm, SOMAK is a combination of SOM followed by the Ant K-means (AK) algorithm, ASCAK is composed by the ASCA (Ant System-based Clustering Algorithm) and AK algorithms, SOINAK is composed by the Self-Organizing Incremental Neural Network (SOINN) and AK. SOINAK presented a better performance among the four proposed techniques when applied to pattern recognition problems., Comment: 20 pages, in Portuguese, 10 figures, 6 tables

Neural Networks have been applied for time series prediction with good experimental results that indicate the high capacity to approximate functions with good precision. Most neural models used in these applications use activation functions with fixed parameters. However, it is known that the choice of activation function strongly influences the complexity and performance of the neural network and that a limited number of activation functions have been used. In this work, we propose the use of a family of free parameter asymmetric activation functions for neural networks and show that this family of defined activation functions satisfies the requirements of the universal approximation theorem. A methodology for the global optimization of this family of activation functions with free parameter and the weights of the connections between the processing units of the neural network is used. The central idea of the proposed methodology is to simultaneously optimize the weights and the activation function used in a multilayer perceptron network (MLP), through an approach that combines the advantages of simulated annealing, tabu search and a local learning algorithm, with the purpose of improving performance in the adjustment and forecasting of time series. We chose two learning algorithms: backpropagation with the term momentum (BPM) and LevenbergMarquardt (LM)., Comment: 19 pages, in Portuguese, 2 figures, 12 tables

This works proposes a methodology to searching for automatically Artificial Neural Networks (ANN) by using Cellular Genetic Algorithm (CGA). The goal of this methodology is to find compact networks whit good performance for classification problems. The main reason for developing this work is centered at the difficulties of configuring compact ANNs with good performance rating. The use of CGAs aims at seeking the components of the RNA in the same way that a common Genetic Algorithm (GA), but it has the differential of incorporating a Cellular Automaton (CA) to give location for the GA individuals. The location imposed by the CA aims to control the spread of solutions in the populations to maintain the genetic diversity for longer time. This genetic diversity is important for obtain good results with the GAs., Comment: 35 pages, in Portuguese, 4 figures, 7 algorithms

The optimization of Artificial Neural Networks (ANNs) is an important task to the success of using these models in real-world applications. The solutions adopted to this task are expensive in general, involving trial-and-error procedures or expert knowledge which are not always available. In this work, we investigated the use of meta-learning to the optimization of ANNs. Meta-learning is a research field aiming to automatically acquiring knowledge which relates features of the learning problems to the performance of the learning algorithms. The meta-learning techniques were originally proposed and evaluated to the algorithm selection problem and after to the optimization of parameters for Support Vector Machines. However, meta-learning can be adopted as a more general strategy to optimize ANN parameters, which motivates new efforts in this research direction. In the current work, we performed a case study using meta-learning to choose the number of hidden nodes for MLP networks, which is an important parameter to be defined aiming a good networks performance. In our work, we generated a base of meta-examples associated to 93 regression problems. Each meta-example was generated from a regression problem and stored: 16 features describing the problem (e.g., number of attributes and correlation among the problem attributes) and the best number of nodes for this problem, empirically chosen from a range of possible values. This set of meta-examples was given as input to a meta-learner which was able to predict the best number of nodes for new problems based on their features. The experiments performed in this case study revealed satisfactory results., Comment: 12 pages, in Portuguese, 2 figures, 2 tables

The universe is composed of galaxies that have diverse shapes. Once the structure of a galaxy is determined, it is possible to obtain important information about its formation and evolution. Morphologically classifying galaxies means cataloging them according to their visual appearance and the classification is linked to the physical properties of the galaxy. A morphological classification made through visual inspection is subject to biases introduced by subjective observations made by human volunteers. For this reason, systematic, objective and easily reproducible classification of galaxies has been gaining importance since the astronomer Edwin Hubble created his famous classification method. In this work, we combine accurate visual classifications of the Galaxy Zoo project with \emph {Deep Learning} methods. The goal is to find an efficient technique at human performance level classification, but in a systematic and automatic way, for classification of elliptical and spiral galaxies. For this, a neural network model was created through an Ensemble of four other convolutional models, allowing a greater accuracy in the classification than what would be obtained with any one individual. Details of the individual models and improvements made are also described. The present work is entirely based on the analysis of images (not parameter tables) from DR1 (www.datalab.noao.edu) of the Southern Photometric Local Universe Survey (S-PLUS). In terms of classification, we achieved, with the Ensemble, an accuracy of $\approx 99 \%$ in the test sample (using pre-trained networks)., Comment: 18 pages, 13 figures, codes and data available at https://natanael.net, text in portuguese

Graph-based Neural Networks (GNNs) are recent models created for learning representations of nodes (and graphs), which have achieved promising results when detecting patterns that occur in large-scale data relating different entities. Among these patterns, financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes, and the concept drift (i.e., statistical properties of the data change over time). Since GNNs are based on message propagation, the representation of a node is strongly impacted by its neighbors and by the network's hubs, amplifying the imbalance effects. Recent works attempt to adapt undersampling and oversampling strategies for GNNs in order to mitigate this effect without, however, accounting for concept drift. In this work, we conduct experiments to evaluate existing techniques for detecting network fraud, considering the two previous challenges. For this, we use real data sets, complemented by synthetic data created from a new methodology introduced here. Based on this analysis, we propose a series of improvement points that should be investigated in future research., Comment: 12 pages, in Portuguese

In this work we deal with the problem of grouping in headlines of the newspaper ABC (Australian Bro-adcasting Corporation) using unsupervised machine learning techniques. We present and discuss the results on the clusters found, Comment: in Portuguese

We present a study of possible predictors based on four supervised machine learning models for the prediction of four mechanical properties of the main industrially used steels. The results were obtained from an experimental database available in the literature which were used as input to train and evaluate the models., Comment: in Portuguese

This is a set of lecture notes for an introductory course (advanced undergaduates or the 1st graduate course) on foundations of supervised machine learning (in Portuguese). The topics include: the geometry of the Hamming cube, concentration of measure, shattering and VC dimension, Glivenko-Cantelli classes, PAC learnability, universal consistency and the k-NN classifier in metric spaces, dimensionality reduction, universal approximation, sample compression. There are appendices on metric and normed spaces, measure theory, etc., making the notes self-contained. Este \'e um conjunto de notas de aula para um curso introdut\'orio (curso de gradua\c{c}\~ao avan\c{c}ado ou o 1o curso de p\'os) sobre fundamentos da aprendizagem de m\'aquina supervisionada (em Portugu\^es). Os t\'opicos incluem: a geometria do cubo de Hamming, concentra\c{c}\~ao de medida, fragmenta\c{c}\~ao e dimens\~ao de Vapnik-Chervonenkis, classes de Glivenko-Cantelli, aprendizabilidade PAC, consist\^encia universal e o classificador k-NN em espa\c{c}os m\'etricos, redu\c{c}\~ao de dimensionalidade, aproxima\c{c}\~ao universal, compress\~ao amostral. H\'a ap\^endices sobre espa\c{c}os m\'etricos e normados, teoria de medida, etc., tornando as notas autosuficientes., Comment: 390 pp. + vii, in Portuguese, a preliminary version, to be published by IMPA as a book of lectures of the 23nd Brazilian Math Colloquium (July 28 - Aug 2, 2019), submitted to arXiv upon IMPA permission