Guilherme Palermo Coelho

State University of Campinas (UNICAMP) | UNICAMP · Faculty of Technology

Disturbances in space weather can negatively affect several fields, including aviation and aerospace, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are the most significant events that can affect the Earth's atmosphere, thus leading researchers to drive efforts on their forecasti...

Respirable solid particles and liquid droplets suspended in the air, known as particulate matter (PM), may have a significant impact on human health, urban infrastructure, and natural and agricultural systems. The adverse effects of PM have raised public concern, especially in heavily polluted areas in the world, making it imperative the developmen...

According to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extraction. The technique known as Sentiment Analysis (S...

This paper presents an extension of the RMFinder technique, previously proposed for scenario reduction within the decision-making process in oil fields. As there are several uncertainties associated with this process, a large number of scenarios should be analyzed so that high-quality production strategies can be defined. Such broad analysis is ver...

Clustering is an unsupervised task employed when there is no prior knowledge about the structure and information contained in the data. Nowadays the amount of information and the dimensionality of data increased. Due to this, several datasets contain samples that can be clustered in different ways, presenting different partitions. Classical algorit...

In the area of reservoir engineering, the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure. Traditionally, this optimization process was centered on a single objective, such as net present value, return on investment, cumulative oil produc...

Recent advances in Machine Learning and, especially, Deep Learning, have led to applications of these areas in different fields of knowledge, with great emphasis on stock market prediction. There are two main approaches in the literature to predict future prices in the stock market: (1) considering historical stock prices; and (2) considering news...

Multi-objective evolutionary algorithms (MOEAs) are a practical tool to solve non-linear problems with multiple objective functions. However, when applied to expensive black-box scenario-based optimization problems, MOEA’s performance becomes constrained due to computational or time limitations. Scenario-based optimization refers to problems that a...

The stock market is an important segment of the economy that circulates a large volume of assets. Several factors may affect the stock market transactions, leading to fluctuations in the stock values that may pose a problem for those who seek to forecast future stock values and maximize their profits. This issue is more serious when the stock value...

In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as the derivative-free evolutionary algorithms, these...

Surrogate models are techniques to approximate the objective functions of expensive optimization problems. Recently, Random Forests have been studied as a surrogate model technique for combinatorial optimization problems. Nonetheless, Random Forests contain several hyper-parameters that are used to control the prediction process. Despite their impo...

Stock market prediction considering financial news is still an open challenge in the literature. Generally, related works consider the sentiment present in news with the following approaches: (1) lexicons; or (2) machine learning algorithms. However, both strategies are subject to errors introduced by human factors. While the lexical approach needs...

Many real-world optimization problems involve time-consuming fitness evaluation. To reduce the computational cost of expensive evaluations, researchers have been developing surrogate models to approximate the objective function values of unevaluated candidate solutions. However, most of the research has been developed for continuous optimization pr...

The Pap test is a screening procedure used worldwide for the diagnosis of cervical cancer. The Malignancy AssociatedChanges (MAC) are slight morphological and textural changes that take place in the chromatin of the cell nucleus. Their study allows the early detection of several types of cancers. However, the number of data set for MAC studies is l...

Expensive multi-objective combinatorial optimization problems have constraints in the number of objective function evaluations due to time, financial, or resource restrictions. As most combinatorial problems, they are subject to a high number of duplicated solutions. Given the fact that expensive environments limit the number of objective function...

Investments in the stock market have grown in Brazil in recent years, especially considering the individual number of investors. According to data from April 2020, the Brazilian stock market reached the historic mark of 2.38 million active investors, and with this scenario, there is an increasing need to study the Brazilian financial market, seekin...

Cervical cancer is a global public health problem. Much progress has been made to improve its early detection. Despite Brazil’s efforts to reduce mortality indicators from this disease, they are still not sufficient compared to progress in other countries. The objective of this study is to present descriptors of the characteristics used in Malignit...

Ensembles of geological realizations (GR) are normally processed by numerical simulators to evaluate geological uncertainty during the decision-making process. Although different stochastic spatial algorithms can quickly generate hundreds to thousands of GR to capture the full uncertainty range, the simulation process applied to this number of real...

The Efficient Market Hypothesis states that stock market changes reflect the arrival of new information through external events and news. Thus, many recent studies in the literature evaluate the impact of Sentiment Analysis (SA) applied to social media and news in the stock market. However, these studies generally do not present investment strategi...

Air quality monitoring data are useful in different areas of research and have varied applications, especially with a focus on the relationship between air pollution, respiratory problems, and other health hazards. The main atmospheric pollutants are: ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and particulate ma...

Space weather events may cause damage to several types of technologies, including aviation, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares belong to the most significant events, and refer to sudden radiation releases that can affect the Earth’s atmosphere within a few hours or min...

Diversos trabalho na literatura indicam que poluentes atmosféricos são nocivos à saúde. Sendo assim, neste trabalho buscou-se verificar a possibilidade de prever, a partir de dados de concentração de poluentes atmosféricos e com 24 h de antecedência, o número de internações hospitalares associadas a doenças respiratórias. Para isso, foram utilizado...

Ao longo dos anos, diversos trabalho estudaram os riscos da poluição atmosférica à saúde humana, descobrindo que grande parte dos poluentes, tais como Material Particulado (tanto MP2,5 quanto MP10) e Ozônio (O3), são nocivos ao ser humano. Dentre os problemas causados, destacam-se as doenças respiratórias. Os dados de concentração de poluentes atmo...

The World Wide Web (WWW) is one of the main sources of opinions nowadays. Whether through news sites, blogs or social networks, a lot of information is available to explore. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extraction. The technique known as Sentiment Analysis aims to...

Due to the harmful effects that high intensity solar flares may cause, several research groups are dedicated to the task of predicting this phenomenon. Given this scenario, the present project applied and compared hierarchical clustering techniques as a preprocessing step to solar flare forecasting, in order to verify whether this approach leads to...

Among the phenomena that occur on the surface of the Sun, solar flares may cause several damages, from short circuits in power transmission lines to complete interruptions in telecommunications systems. In order to mitigate these effects, many works have been dedicated to the proposal of mechanisms capable of predicting the occurrence of solar flar...

OFDM modulation generates signals with high PAPR values, which affects the system performance and energy efficiency. From the vast spectrum of PAPR reduction techniques, PTS stands out for achieving significant reduction in the cost of high computational complexity. The technique divides the OFDM symbols into partial sequences, which are rotated an...

This paper presents an extension of the RMFinder technique, previously proposed to identify representative models (RMs) within the decision-making process in oil fields. As there are several uncertainties associated with this decision-making process, a large number of scenarios are supposed to be analyzed, so that high-quality production strategies...

The application of time series analysis and forecasting to stock markets is particularly relevant to Technical Analysis, which uses historical values to obtain indicators that highlight possible trends in stock prices. In practice, most of these indicators are evaluated graphically and their direct impact on the quality of stock price forecasting h...

Altas concentrações de material particulado (MP) na atmosfera, particularmente de MP2,5, podem trazer impactos negativos para a sociedade. Com isso, muitos trabalhos propõem mecanismos para prever a concentração de MP2,5 ([MP2,5]). Mesmo com evidências de que o uso de variáveis meteorológicas (VMs) pode melhorar a qualidade das predições, a maioria...

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a...

Most community detection algorithms from the literature work as optimization tools that minimize a given fitness function, while assuming that each node belongs to a single community. Since there is no hard concept of what a community is, most proposed fitness functions focus on a particular definition. As such, these functions do not always lead t...

High concentrations of atmospheric pollutants provoke negative effects that range from respiratory problems in humans to altered growth in crops due to the reduction of solar radiation. In this context, the study of suspended particulate matter (PM) in the atmosphere is especially relevant. Several works in the literature are dedicated to evaluate...

In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem — associated with a minimal entropy configuration — adopting a Michigan-like...

The analysis of complex networks is an important research topic that helps us understand the underlying behavior of complex systems and the interactions of their components. One particularly relevant analysis is the detection of communities formed by such interactions. Most community detection algorithms work as optimization tools that minimize a g...

In this work, a novel biclustering-based approach to data imputation is proposed. This approach is based on the Mean Squared Residue metric, used to evaluate the degree of coherence among objects of a dataset, and presents an algebraic development that allows the modeling of the predictor as a quadratic programming problem. The proposed methodology...

The development of brain-computer interfaces (BCIs) for disabled patients is currently a growing field of research. Most BCI systems are based on electroencephalography (EEG) signals, and within this group, systems using motor imagery (MI) are amongst the most flexible. However, for stroke patients, the motor areas of the brain are not always avail...

In this work, we present a novel framework for automatic feature selection in brain-computer interfaces (BCIs). The proposal, which manipulates features generated in the frequency domain by an estimate of the power spectral density of the EEG signals, is based on feature optimization (with both binary and real coding) using a state-of-the-art artif...

In this paper, a review of the conceptual and practical aspects of the aiNet (Artificial Immune Network) family of algorithms will be provided. This family of algorithms started with the aiNet algorithm, proposed in 2002 for data clustering and, since then, several variations have been developed for data clustering, biclustering and optimization in...

Diversity maintenance is an important aspect in population-based metaheuristics for optimization, as it tends to allow a better exploration of the search space, thus reducing the susceptibility to local optima in multimodal optimization problems. In this context, metaheuristics based on the Artificial Immune System (AIS) framework, especially those...

The class of inventory routing problems (IRPs) is present in several areas, including automotive industry and cash management for ATM networks. In the specific case of vendor-managed IRPs, in which the supplier is responsible for managing the product inventory in each client and for properly providing replenishments, the challenge is to determine w...

Until recently, the main focus of researchers that develop algorithms for evolutionary multi-objective optimization has been the creation of mechanisms capable of obtaining sets of solutions that are as close as possible to the true Pareto front of the problem and also as diverse as possible in the objective space, to properly cover such front. How...

This work presents the application of the omni-aiNet algorithm—an immune-inspired algorithm originally developed to solve single and multi-objective optimization problems—to the reconstruction of phylogenetic trees. The main goal here is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minim...

This work applies two immune-inspired algorithms, namely opt-aiNet and omni-aiNet, to train multi-layer perceptrons (MLPs) to be used in the construction of ensembles of classifiers. The main goal is to investigate the influence of the diversity of the set of solutions generated by each of these algorithms, and if these solutions lead to improvemen...

Query expansion is a technique utilized to improve the performance of information retrieval systems by automatically adding related terms to the initial query. These additional terms can be obtained from documents stored in a database. Usually, this task is performed by clustering the documents and then extracting representative terms from the clus...

Metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though they generally implement very simple mechanisms to control the dynamics of the network. In the AIS literatur...

Immune-inspired algorithms based on the Immune Network theory have been frequently claimed to be capable of maintaining diversity among the candidate solutions in their population. However, no specific study on this aspect to verify how the intrinsic diversity mechanisms of such immune algorithms behave, when compared to other approaches from the l...

In this paper, a review of the conceptual and practical aspects of the aiNet (Artificial Immune Network) family of algorithms will be provided. This family of algorithms started with the aiNet algorithm, proposed in 2002 for data clustering and, since then, several variations have been developed for data clustering, biclustering and optimization in...

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques. Given that biclustering requires the optimization of at least two conflicting objectives and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were propos...

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features, and, consequently, to allow the extraction of more accurate information from datasets. Given that biclustering requires the optimization of at least...

A recent proposal developed to avoid some of the drawbacks presented by standard clustering algorithms is the so-called biclustering technique [1], which performs clustering of rows and columns of the data matrix simultaneously, allowing the extraction of additional information from the dataset. Since the biclustering problem is combinatorial, and...

Biclustering is a technique developed to allow simultaneous clustering of rows and columns of a dataset. This might be useful to extract more accurate information from sparse datasets and to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features. Gi...

This work introduces an ant-inspired algorithm for optimization in continuous search spaces that is based on the generation of random vectors with multivariate Gaussian pdf. The proposed approach is called MACACO -- Multivariate Ant Colony Algorithm for Continuous Optimization -- and is able to simultaneously adapt all the dimensions of the random...

This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the reconstruction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologie...

This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the construction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies,...

This work presents omni-aiNet, an immune-inspired algorithm developed to solve single and multi-objective optimization problems, either with single and multi-global solutions. The search engine is capable of automatically adapting the exploration of the search space according to the intrinsic demand of the optimization problem. This proposal unites...

In this paper, we propose the use of an immune-inspired approach called opt-aiNet to generate a diverse set of high-performance candidates to compose an ensemble of neural network classifiers. Being a population-based search algorithm, the opt-aiNet is capable of maintaining diversity and finding many high-performance solutions simultaneously, whic...

In this work we propose an immune-based approach for designing of fuzzy systems. From numerical data and with membership function previously defined, the immune algorithm evolves a population of fuzzy classification rules based on the clonal selection, hypermutation and immune network principles. Once AIS are able to find multiple good solutions o...

Most recommender systems calculate the similarities between the profiles of two users considering the global set of attributes (which can be items or services) available in a given application, which frequently leads to distortions in the identification of groups of users with similar profiles. If only subsets of attributes are considered in this p...