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Prof. Marley Maria Bernardes Rebuzzi Vellasco
Pontifical Catholic University of Rio de Janeiro - PUC-Rio

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0 Evolutionary Computation
0 Fuzzy Logic
0 Robotics
0 Machine Learning and Applications
0 neural networks & deep learning

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neural networks & deep learning
Fuzzy Logic
Evolutionary Computation
Machine Learning and Applications
Robotics

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Journal article
Published: 23 August 2021 in Sensors
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Problem: An application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented. Motivation: It is possible that classifiers are using spurious artifacts in dataset images to achieve high performances, and such explainable techniques can help identify this issue. Aim: For this purpose, several approaches were used in tandem, in order to create a complete overview of the classificatios. Methodology: The techniques used included GradCAM, LIME, RISE, Squaregrid, and direct Gradient approaches (Vanilla, Smooth, Integrated). Main results: Among the deep neural networks architectures evaluated for this image classification task, VGG16 was shown to be most affected by biases towards spurious artifacts, while DenseNet was notably more robust against them. Further impacts: Results further show that small differences in validation accuracies can cause drastic changes in explanation heatmaps for DenseNet architectures, indicating that small changes in validation accuracy may have large impacts on the biases learned by the networks. Notably, it is important to notice that the strong performance metrics achieved by all these networks (Accuracy, F1 score, AUC all in the 80 to 90% range) could give users the erroneous impression that there is no bias. However, the analysis of the explanation heatmaps highlights the bias.

ACS Style

Iam Palatnik de Sousa; Marley M. B. R. Vellasco; Eduardo Costa da Silva. Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers. Sensors 2021, 21, 5657 .

AMA Style

Iam Palatnik de Sousa, Marley M. B. R. Vellasco, Eduardo Costa da Silva. Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers. Sensors. 2021; 21 (16):5657.

Chicago/Turabian Style

Iam Palatnik de Sousa; Marley M. B. R. Vellasco; Eduardo Costa da Silva. 2021. "Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers." Sensors 21, no. 16: 5657.

Journal article
Published: 21 October 2020 in Expert Systems with Applications
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In many real-world problems, some coordination between agents is necessary to enable the task to be optimally performed. However, obtaining this coordination can be challenging due to the quantity and characteristics of the agents, the dynamics of the environment and/or the complexity of the task, requiring much computation time. Furthermore, some problems require different types of agent specialization. In this case, it is very difficult for the programmers to define learning strategies and their parameters. Optimization of these parameters using standard evolutionary algorithms is also inadequate due to the high computational cost in these real multi-agent situations. The main objective of this study is therefore to propose a new neuroevolution model to be applied to agent coordination problems, termed the Quantum-Inspired Neuro Coevolution (QNCo) Model. QNCo makes use of paradigms from quantum physics and biological coevolution to evolve sub-populations of quantum individuals aiming convergence gains. The model has the capacity to autonomously obtain the best neural network topology of each agent, eliminating the need for the programmer to set this configuration. New quantum crossover and mutation operators were proposed and compared during function optimization of different dimensions. The proposed model was tested in two simulation problems, prey-predator and multi-rover tasks, and one real problem of mobile telephony coverage. The QNCo model yielded promising results compared to similar algorithms, with good solutions in terms of learning strategies and a great reduction in convergence time.

ACS Style

Eduardo Dessupoio Moreira Dias; Marley Maria Bernardes Rebuzzi Vellasco; André Vargas Abs da Cruz. Quantum-inspired neuro coevolution model applied to coordination problems. Expert Systems with Applications 2020, 167, 114133 .

AMA Style

Eduardo Dessupoio Moreira Dias, Marley Maria Bernardes Rebuzzi Vellasco, André Vargas Abs da Cruz. Quantum-inspired neuro coevolution model applied to coordination problems. Expert Systems with Applications. 2020; 167 ():114133.

Chicago/Turabian Style

Eduardo Dessupoio Moreira Dias; Marley Maria Bernardes Rebuzzi Vellasco; André Vargas Abs da Cruz. 2020. "Quantum-inspired neuro coevolution model applied to coordination problems." Expert Systems with Applications 167, no. : 114133.

Article
Published: 06 October 2020 in International Journal of Fuzzy Systems
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This paper focuses on the new model for the classification of railhead defects, through images acquired by a rail inspection vehicle. In this regard, we discuss the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed. The performance is based on the data set composed of images provided by a Brazilian railway company, which covers the four possible railhead defects (cracking, flaking, head-check and spalling) and the normal condition of the railhead. Additionally, we apply different levels of additive white Gaussian noise in the images in order to challenge the proposed model. Finally, we discuss performance analysis in terms of convergence speed, computational complexity reduction, and classification ratio. The reported results show that the proposal achieved improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required under real-time constraint or low computational resource availability.

ACS Style

Eduardo P. de Aguiar; Thiago E. Fernandes; Fernando M. De A. Nogueira; Daniel D. Silveira; Marley M. B. R. Vellasco; Moisés V. Ribeiro. A New Model to Distinguish Railhead Defects Based on Set-Membership Type-2 Fuzzy Logic System. International Journal of Fuzzy Systems 2020, 23, 1057 -1069.

AMA Style

Eduardo P. de Aguiar, Thiago E. Fernandes, Fernando M. De A. Nogueira, Daniel D. Silveira, Marley M. B. R. Vellasco, Moisés V. Ribeiro. A New Model to Distinguish Railhead Defects Based on Set-Membership Type-2 Fuzzy Logic System. International Journal of Fuzzy Systems. 2020; 23 (4):1057-1069.

Chicago/Turabian Style

Eduardo P. de Aguiar; Thiago E. Fernandes; Fernando M. De A. Nogueira; Daniel D. Silveira; Marley M. B. R. Vellasco; Moisés V. Ribeiro. 2020. "A New Model to Distinguish Railhead Defects Based on Set-Membership Type-2 Fuzzy Logic System." International Journal of Fuzzy Systems 23, no. 4: 1057-1069.

Journal article
Published: 23 July 2020 in Sensors
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The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

ACS Style

Alimed Celecia; Karla Figueiredo; Marley Vellasco; René González. A Portable Fuzzy Driver Drowsiness Estimation System. Sensors 2020, 20, 4093 .

AMA Style

Alimed Celecia, Karla Figueiredo, Marley Vellasco, René González. A Portable Fuzzy Driver Drowsiness Estimation System. Sensors. 2020; 20 (15):4093.

Chicago/Turabian Style

Alimed Celecia; Karla Figueiredo; Marley Vellasco; René González. 2020. "A Portable Fuzzy Driver Drowsiness Estimation System." Sensors 20, no. 15: 4093.

Journal article
Published: 28 May 2020 in Sensors
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This study aims to develop a prototype of an autonomous robotic device to assist the locomotion of the elderly in urban environments. Among the achievements presented are the control techniques used for autonomous navigation and the software tools and hardware applied in the prototype. This is an extension of a previous work, in which part of the navigation algorithm was developed and validated in a simulated environment. In this extension, the real prototype is controlled by an algorithm based on fuzzy logic to obtain standalone and more-natural navigation for the user of the device. The robotic device is intended to guide an elderly person in an urban environment autonomously, although it also has a manual navigation mode. Therefore, the device should be able to navigate smoothly without sudden manoeuvres and should respect the locomotion time of the user. Furthermore, because of the proposed environment, the device should be able to navigate in an unknown and unstructured environment. The results reveal that this prototype achieves the proposed objective, demonstrating adequate behaviour for navigation in an unknown environment and fundamental safety characteristics to assist the elderly.

ACS Style

Daniel Leite; Karla Figueiredo; Marley Vellasco. Prototype of Robotic Device for Mobility Assistance for the Elderly in Urban Environments. Sensors 2020, 20, 3056 .

AMA Style

Daniel Leite, Karla Figueiredo, Marley Vellasco. Prototype of Robotic Device for Mobility Assistance for the Elderly in Urban Environments. Sensors. 2020; 20 (11):3056.

Chicago/Turabian Style

Daniel Leite; Karla Figueiredo; Marley Vellasco. 2020. "Prototype of Robotic Device for Mobility Assistance for the Elderly in Urban Environments." Sensors 20, no. 11: 3056.

Journal article
Published: 12 March 2020 in IEEE Systems Journal
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Refinery scheduling comprises a group of decisions that aims to optimize asset allocation, activity sequencing, and the time-related realization of those activities. This scheduling must achieve multiple objectives while considering different types of constraints. Uninterrupted processing unit operation, on-time crude oil batch receipts, and tank switchover minimization coexist in the everyday reasoning of a scheduler. However, it is not usual that works encompassing many operational aspects, such as multiple operational objectives, settling time, and an unlimited number of crudes, to blend in any tank. This article proposes a new algorithm that integrates linear and grammar-guided genetic programming concepts with a quantum-inspired approach to create programs that represent a crude oil refinery scheduling solution. The fitness function comprises four objectives that guide the evolution based on importance predefined by the decision maker. We propose a success ratio to evaluate the algorithm performance considering 50 runs for each case. A final solution is considered a success if two more important objectives are optimized. We assessed our approach with five different scenarios of a real refinery and three of them achieved a 100% success ratio.

ACS Style

Cristiane Salgado Pereira; Douglas Mota Dias; Marco Aurelio Cavalcanti Pacheco; Marley M. B. Rebuzzi Vellasco; Andre Vargas Abs da Cruz; Estefane Horn Hollmann. Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery. IEEE Systems Journal 2020, 14, 3926 -3937.

AMA Style

Cristiane Salgado Pereira, Douglas Mota Dias, Marco Aurelio Cavalcanti Pacheco, Marley M. B. Rebuzzi Vellasco, Andre Vargas Abs da Cruz, Estefane Horn Hollmann. Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery. IEEE Systems Journal. 2020; 14 (3):3926-3937.

Chicago/Turabian Style

Cristiane Salgado Pereira; Douglas Mota Dias; Marco Aurelio Cavalcanti Pacheco; Marley M. B. Rebuzzi Vellasco; Andre Vargas Abs da Cruz; Estefane Horn Hollmann. 2020. "Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery." IEEE Systems Journal 14, no. 3: 3926-3937.

Journal article
Published: 10 February 2020 in IEEE Systems Journal
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The development of an oil reservoir consists in drilling wells that maximize revenue. The quest for this configuration is often based on optimization processes that use the net present value as the evaluation function. Determining quantity, location, type, and trajectory of wells in a reservoir is a complex optimization problem, which has a high computational cost due to the continuous use of simulators. Many researchers have proposed the replacement of the reservoir simulator by a proxy, to reduce the computational cost, with promising results. However, in order to analyze all relevant variables and obtain a better solution, a comprehensive proxy model must also consider the conditions of the surface behavior, incorporating the complete integrated system. None of the previous works has developed a model based on the integrated simulation behavior. This article presents the proposal of a new proxy model based on neural networks, called SimProxy, which integrates reservoir and surface behavior, to reduce the computational cost of a decision support system. The proposed model was evaluated in a real oil reservoir. The results indicate that SimProxy can efficiently replace the use of commercial simulators in an optimization process, providing good accuracy with a substantial decrease in computational cost.

ACS Style

Manoela Kohler; Marley Vellasco; Eugenio Silva; Karla Figueiredo. SimProxy Decision Support System: A Neural Network Proxy Applied to Reservoir and Surface Integrated Optimization. IEEE Systems Journal 2020, 14, 5111 -5120.

AMA Style

Manoela Kohler, Marley Vellasco, Eugenio Silva, Karla Figueiredo. SimProxy Decision Support System: A Neural Network Proxy Applied to Reservoir and Surface Integrated Optimization. IEEE Systems Journal. 2020; 14 (4):5111-5120.

Chicago/Turabian Style

Manoela Kohler; Marley Vellasco; Eugenio Silva; Karla Figueiredo. 2020. "SimProxy Decision Support System: A Neural Network Proxy Applied to Reservoir and Surface Integrated Optimization." IEEE Systems Journal 14, no. 4: 5111-5120.

Article
Published: 30 January 2020 in Applied Intelligence
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This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.

ACS Style

Tatiana Escovedo; Adriano Koshiyama; Andre Abs da Cruz; Marley Vellasco. Neuroevolutionary learning in nonstationary environments. Applied Intelligence 2020, 50, 1590 -1608.

AMA Style

Tatiana Escovedo, Adriano Koshiyama, Andre Abs da Cruz, Marley Vellasco. Neuroevolutionary learning in nonstationary environments. Applied Intelligence. 2020; 50 (5):1590-1608.

Chicago/Turabian Style

Tatiana Escovedo; Adriano Koshiyama; Andre Abs da Cruz; Marley Vellasco. 2020. "Neuroevolutionary learning in nonstationary environments." Applied Intelligence 50, no. 5: 1590-1608.

Conference paper
Published: 01 January 2020 in Communications in Computer and Information Science
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An approximation method for faster generation of explanations in medical imaging classifications is presented. Previous results in literature show that generating detailed explanations with LIME, especially when fine tuning parameters, is very computationally and time demanding. This is true both for manual and automatic parameter tuning. The alternative here presented can decrease computation times by several orders of magnitude, while still identifying the most relevant regions in images. The approximated explanations are compared to previous results in literature and medical expert segmentations for a dataset of histopathology images used in a binary classification task. The classifications of a convolutional neural network trained on this dataset are explained by means of heatmap visualizations. The results show that it seems to be possible to achieve much faster computation times by trading off finer detail in the explanations. This could give more options for users of artificial intelligence black box systems in the context of medical imaging tasks, in regards to generating insight or auditing decision systems.

ACS Style

Iam Palatnik De Sousa; Marley M. B. R. Vellasco; Eduardo Costa Da Silva. Approximate Explanations for Classification of Histopathology Patches. Communications in Computer and Information Science 2020, 517 -526.

AMA Style

Iam Palatnik De Sousa, Marley M. B. R. Vellasco, Eduardo Costa Da Silva. Approximate Explanations for Classification of Histopathology Patches. Communications in Computer and Information Science. 2020; ():517-526.

Chicago/Turabian Style

Iam Palatnik De Sousa; Marley M. B. R. Vellasco; Eduardo Costa Da Silva. 2020. "Approximate Explanations for Classification of Histopathology Patches." Communications in Computer and Information Science , no. : 517-526.

Original papers
Published: 01 January 2020 in Brazilian Journal of Development
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Os métodos de agrupamento, apesar de amplamente usados, ainda não possuem uma teoria generalizada e amplamente aceita, dado que diferentes métodos podem fornecer resultados distintos, já que mesmo a técnica mais adequada pode variar de acordo com características intrínsecas aos dados, que nem sempre se tem conhecimento prévio. De maneira geral, não há consenso sobre quais métodos melhor se aplicam a cada caso. Mesmo considerando as diversas métricas já criadas para avaliar a qualidade dos agrupamentos, não há convergência entre os resultados dessas medidas, pois na elaboração de análises de agrupamentos há uma série de aspectos subjetivos - como a escolha do método mais adequado e do número de grupos – que podem impactar nos agrupamentos finais e comprometer os resultados obtidos. Nesse sentido, este trabalho tem como principal objetivo apresentar um novo índice de avaliação de agrupamentos, denominado Índice de Qualidade Final (IQF). Trata-se de um índice que conjuga resultados de diversas medidas de avaliação da qualidade dos grupos e, a partir disso, permite escolher o agrupamento mais adequado aos dados, dentre as diversas metodologias conhecidas. Em paralelo, e para aplicação do IQF, foi realizada uma análise da infraestrutura do Município do Rio de Janeiro, com foco na comparação entre áreas de favela e não favela. Foram utilizados diversos métodos de agrupamento com posterior aplicação do IQF para avaliar o índice proposto e identificar se há, de fato, questões de infraestrutura que caracterizam as favelas e as diferenciam dos demais bairros da cidade. Os resultados obtidos permitem elencar e ordenar os agrupamentos formados orientando a escolha do agrupamento mais satisfatório, indicando que o novo índice proposto poderá ajudar na avaliação de forma eficiente.

ACS Style

Joana A. Siqueira; Karla Figueiredo; Marley M.B.R. Vellasco. Índice de Qualidade Final – Um Novo Índice para Avaliação de Agrupamento Aplicado à Análise da Infraestrutura do Rio de Janeiro – um estudo comparativo entre bairros e favelas. Brazilian Journal of Development 2020, 6, 28958 -28984.

AMA Style

Joana A. Siqueira, Karla Figueiredo, Marley M.B.R. Vellasco. Índice de Qualidade Final – Um Novo Índice para Avaliação de Agrupamento Aplicado à Análise da Infraestrutura do Rio de Janeiro – um estudo comparativo entre bairros e favelas. Brazilian Journal of Development. 2020; 6 (5):28958-28984.

Chicago/Turabian Style

Joana A. Siqueira; Karla Figueiredo; Marley M.B.R. Vellasco. 2020. "Índice de Qualidade Final – Um Novo Índice para Avaliação de Agrupamento Aplicado à Análise da Infraestrutura do Rio de Janeiro – um estudo comparativo entre bairros e favelas." Brazilian Journal of Development 6, no. 5: 28958-28984.

Journal article
Published: 21 October 2019 in Applied Soft Computing
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The Particle Swarm Optimization algorithm is a metaheuristic based on populations of individuals in which solution candidates evolve through simulation of a simplified model of social adaptation. By aggregating robustness, efficiency and simplicity, PSO has gained great popularity. Modified PSO algorithms have been proposed to solve optimization problems with domain, linear and nonlinear constraints. Other algorithms that use multi-objective optimization to deal with constrained problems face the problem of not being able to guarantee finding feasible solutions. Current PSO algorithms that deal with constraints only treat domain constraints by controlling the velocity of particle displacement in the swarm, or do so inefficiently by randomly resetting each infeasible particle. This approach may make it infeasible to optimize certain problems, especially real ones. This work presents a new particle swarm optimization algorithm, called PSO+, capable of solving problems with linear and nonlinear constraints in order to solve these deficiencies. The proposed algorithm uses a feasibility repair operator and two swarms to ensure there will always be a swarm whose particles fully respect every constraint. A new particle update method is also proposed to insert diversity into the swarm and improve search-space coverage, allowing the search-space border to be exploited as well, which is particularly convenient when the optimization involves active constraints in global optimum. Two heuristics are proposed to initialize a feasible swarm with the purpose of speeding up the initialization mechanism and ensuring diversity at the starting point of the optimization process. Furthermore, a neighborhood topology is proposed to minimize premature convergence. The proposed algorithm was tested for twenty-four benchmark functions, as well as in a real reservoir drainage plan optimization problem. Results attest that the new algorithm is competitive, since it increases the efficiency of the PSO and the speed of convergence.

ACS Style

Manoela Kohler; Marley M.B.R. Vellasco; Ricardo Tanscheit. PSO+: A new particle swarm optimization algorithm for constrained problems. Applied Soft Computing 2019, 85, 105865 .

AMA Style

Manoela Kohler, Marley M.B.R. Vellasco, Ricardo Tanscheit. PSO+: A new particle swarm optimization algorithm for constrained problems. Applied Soft Computing. 2019; 85 ():105865.

Chicago/Turabian Style

Manoela Kohler; Marley M.B.R. Vellasco; Ricardo Tanscheit. 2019. "PSO+: A new particle swarm optimization algorithm for constrained problems." Applied Soft Computing 85, no. : 105865.

Journal article
Published: 05 July 2019 in Sensors
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An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the “locally-interpretable model-agnostic explanations” methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge.

ACS Style

Iam Palatnik De Sousa; Marley Maria Bernardes Rebuzzi Vellasco; Eduardo Costa Da Silva. Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases. Sensors 2019, 19, 2969 .

AMA Style

Iam Palatnik De Sousa, Marley Maria Bernardes Rebuzzi Vellasco, Eduardo Costa Da Silva. Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases. Sensors. 2019; 19 (13):2969.

Chicago/Turabian Style

Iam Palatnik De Sousa; Marley Maria Bernardes Rebuzzi Vellasco; Eduardo Costa Da Silva. 2019. "Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases." Sensors 19, no. 13: 2969.

Journal article
Published: 11 June 2019 in Journal of Intelligent & Fuzzy Systems
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ACS Style

Waldir Nunes; Marley Vellasco; Ricardo Tanscheit. Quantum-inspired evolutionary multi-objective fuzzy classifier with real and categorical representation. Journal of Intelligent & Fuzzy Systems 2019, 36, 5875 -5887.

AMA Style

Waldir Nunes, Marley Vellasco, Ricardo Tanscheit. Quantum-inspired evolutionary multi-objective fuzzy classifier with real and categorical representation. Journal of Intelligent & Fuzzy Systems. 2019; 36 (6):5875-5887.

Chicago/Turabian Style

Waldir Nunes; Marley Vellasco; Ricardo Tanscheit. 2019. "Quantum-inspired evolutionary multi-objective fuzzy classifier with real and categorical representation." Journal of Intelligent & Fuzzy Systems 36, no. 6: 5875-5887.

Journal article
Published: 17 April 2019 in Expert Systems with Applications
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Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method. The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods. We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct.

ACS Style

Paul Ross McWhirter; Abir Hussain; Dhiya Al-Jumeily; Iain A. Steele; Marley M.B.R. Vellasco. Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data. Expert Systems with Applications 2019, 131, 94 -115.

AMA Style

Paul Ross McWhirter, Abir Hussain, Dhiya Al-Jumeily, Iain A. Steele, Marley M.B.R. Vellasco. Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data. Expert Systems with Applications. 2019; 131 ():94-115.

Chicago/Turabian Style

Paul Ross McWhirter; Abir Hussain; Dhiya Al-Jumeily; Iain A. Steele; Marley M.B.R. Vellasco. 2019. "Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data." Expert Systems with Applications 131, no. : 94-115.

Proceedings article
Published: 22 October 2018 in Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC)
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Este trabalho descreve um Sistema Fuzzy do tipo 2 desenvolvido automaticamente com o auxílio da Programação Genética para aplicação em previsão de séries temporais. O modelo resultante, denominado GPFIS-Forecast+, é baseado no GPFIS-Forecast desenvolvido anteriormente, que fez uso da Programação Genética Multigênica com bons resultados. Os resultados demonstram que, conforme o esperado, o sistema com conjuntos fuzzy do tipo 2 melhora o desempenho, principalmente na presença de dados ruidosos.

ACS Style

Marco Antônio Da Cunha Ferreira; Ricardo Tanscheit; Marley Vellasco. Automatic Generation of a Type-2 Fuzzy System for Time Series Forecast based on Genetic Programming. Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC) 2018, 1 .

AMA Style

Marco Antônio Da Cunha Ferreira, Ricardo Tanscheit, Marley Vellasco. Automatic Generation of a Type-2 Fuzzy System for Time Series Forecast based on Genetic Programming. Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC). 2018; ():1.

Chicago/Turabian Style

Marco Antônio Da Cunha Ferreira; Ricardo Tanscheit; Marley Vellasco. 2018. "Automatic Generation of a Type-2 Fuzzy System for Time Series Forecast based on Genetic Programming." Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC) , no. : 1.

Journal article
Published: 12 September 2018 in TEMA - Tendências em Matemática Aplicada e Computacional
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Apresenta-se, neste artigo, o desenvolvimento completo de um sistema de navegação auxiliar baseado na Lógica Fuzzy para um robô móvel denominado Robô Ambiental Híbrido Médio (RAHM). Apresenta-se também o projeto eletrônico e a programação do sistema de navegação. O desempenho deste sistema é avaliado frente ao do Sistema Principal de Navegação do RAHM.

ACS Style

Cristhian J Gómez; Marley Vellasco; Ricardo Tanscheit. Controle de um Sistema de Navegação de um Robô Ambiental Híbrido por meio de um Sistema de Inferência Fuzzy Hierárquico. TEMA - Tendências em Matemática Aplicada e Computacional 2018, 19, 235 .

AMA Style

Cristhian J Gómez, Marley Vellasco, Ricardo Tanscheit. Controle de um Sistema de Navegação de um Robô Ambiental Híbrido por meio de um Sistema de Inferência Fuzzy Hierárquico. TEMA - Tendências em Matemática Aplicada e Computacional. 2018; 19 (2):235.

Chicago/Turabian Style

Cristhian J Gómez; Marley Vellasco; Ricardo Tanscheit. 2018. "Controle de um Sistema de Navegação de um Robô Ambiental Híbrido por meio de um Sistema de Inferência Fuzzy Hierárquico." TEMA - Tendências em Matemática Aplicada e Computacional 19, no. 2: 235.

Conference paper
Published: 01 July 2018 in 2018 IEEE Congress on Evolutionary Computation (CEC)
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The occurrence of a disaster brings about damages, destruction, ecological disruption, loss of human life, human suffering, deterioration of health and health service of sufficient magnitude to require external assistance, demanding the mobilization and deployment of emergency rescue units within the affected area, in order to reduce casualties and economic losses. The scheduling of those units is one of the key issues in the emergency response phase and can be seen as a generalization of the unrelated parallel machine scheduling problem with sequence and machine dependent setup. The objective is to minimize the total weighted completion time of the incidents to be attended, where the weight correspond to its severity level. We propose a biased random-key genetic algorithm to tackle this problem, considering fuzzy required processing times for the incidents, and compare the solutions with those generated by a constructive heuristic, from the literature, developed to deal with this problem. Our results show that the genetic algorithm's solutions are 2.17% better than those obtained with the constructive heuristic when applied to instances with up to 40 incidents and 40 rescue units.

ACS Style

Victor Cunha; Luciana Pessoa; Marley Vellasco; Ricardo Tanscheit; Marco Aurelio Pacheco. A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -6.

AMA Style

Victor Cunha, Luciana Pessoa, Marley Vellasco, Ricardo Tanscheit, Marco Aurelio Pacheco. A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-6.

Chicago/Turabian Style

Victor Cunha; Luciana Pessoa; Marley Vellasco; Ricardo Tanscheit; Marco Aurelio Pacheco. 2018. "A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-6.

Journal article
Published: 01 May 2018 in Expert Systems with Applications
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Antonio Carlos De S. Sampaio Filho; Marley M.B.R. Vellasco; Ricardo Tanscheit. A unified solution in fuzzy capital budgeting. Expert Systems with Applications 2018, 98, 27 -42.

AMA Style

Antonio Carlos De S. Sampaio Filho, Marley M.B.R. Vellasco, Ricardo Tanscheit. A unified solution in fuzzy capital budgeting. Expert Systems with Applications. 2018; 98 ():27-42.

Chicago/Turabian Style

Antonio Carlos De S. Sampaio Filho; Marley M.B.R. Vellasco; Ricardo Tanscheit. 2018. "A unified solution in fuzzy capital budgeting." Expert Systems with Applications 98, no. : 27-42.

Article
Published: 05 March 2018 in WIREs Data Mining and Knowledge Discovery
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Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve-fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine-tuning of FIS’s parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine-tuning, fuzzy rule-based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming-based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods. This article is categorized under:

ACS Style

Adriano S. Koshiyama; Ricardo Tanscheit; Marley M. B. R. Vellasco. Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective. WIREs Data Mining and Knowledge Discovery 2018, 9, 1 .

AMA Style

Adriano S. Koshiyama, Ricardo Tanscheit, Marley M. B. R. Vellasco. Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective. WIREs Data Mining and Knowledge Discovery. 2018; 9 (2):1.

Chicago/Turabian Style

Adriano S. Koshiyama; Ricardo Tanscheit; Marley M. B. R. Vellasco. 2018. "Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective." WIREs Data Mining and Knowledge Discovery 9, no. 2: 1.

Journal article
Published: 01 January 2018 in Applied Soft Computing
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Tatiana Escovedo; Adriano Koshiyama; Andre Abs da Cruz; Marley Vellasco. DetectA: abrupt concept drift detection in non-stationary environments. Applied Soft Computing 2018, 62, 119 -133.

AMA Style

Tatiana Escovedo, Adriano Koshiyama, Andre Abs da Cruz, Marley Vellasco. DetectA: abrupt concept drift detection in non-stationary environments. Applied Soft Computing. 2018; 62 ():119-133.

Chicago/Turabian Style

Tatiana Escovedo; Adriano Koshiyama; Andre Abs da Cruz; Marley Vellasco. 2018. "DetectA: abrupt concept drift detection in non-stationary environments." Applied Soft Computing 62, no. : 119-133.