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Solving integer multi-objective optimization problems using TOPSIS, Differential Evolution and Tabu Search

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arXiv 2022年

作者： Schneider, Erick R.F.A. Krohling, Renato A. PPGI UFES- Federal University of Espírito Santo Av. Fernando Ferrari 514 ES Espírito Santo VitóriaCEP 29075-910 Brazil Department of Production Engineering Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo Av. Fernando Ferrari 514 ES Espírito Santo VitóriaCEP 29075-910 Brazil

This paper presents a method to solve non-linear integer multiobjective optimization problems. First the problem is formulated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Next, the Differential Evolution (DE) algorithm in its three versions (standard DE, DE best and DEGL) are used as optimizer. Since the solutions found by the DE algorithms are continuous, the Tabu Search (TS) algorithm is employed to find integer solutions during the optimization process. Experimental results show the effectiveness of the proposed method. © 2022, CC BY.

关键词： Multiobjective optimization

A multiobjective genetic approach with TOPSIS to learn fuzzy rule-based classification systems 8

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A multiobjective genetic approach with TOPSIS to learn fuzzy...

8th Brazilian Conference on Intelligent Systems, BRACIS 2019

作者： De Oliveira Araujo, Antonio Eloy Krohling, Renato Antonio Graduate Program in Computer Science PPGI Federal University of Espirito Santo - UFES Vitoria ES Brazil Production Engineering Department LABCin and Graduate Program in Computer Science PPGI Federal University of Espirito Santo - UFES Vitoria ES Brazil

ISBN: (纸本)9781728142531

The design of a Fuzzy Rule-Based Classification System (FRBCS) taking into account accuracy and interpretability (number of rules) of the FRBCS is an important issue. In this paper, we extend our previous work by considering the average number of fuzzy labels per input variable as a new objective to be treated with TOPSIS. In addition, we employ information entropy to determine the weights of the evaluation criteria. We compare our proposal with a method that deals with the accuracy-interpretability tradeoff by means of a multiobjective evolutionary process based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results show the effectiveness of the proposed approach. © 2019 IEEE.

关键词： Multiobjective optimization

Enrolling Actors in a Social Information System: The Incremental Development of Unique Registry in Brazilian "Bolsa Família" program 16th

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Enrolling Actors in a Social Information System: The Increme...

16th IFIP WG 9.4 International Conference on Social Implications of computers in Developing Countries, ICT4D 2020

作者： de Antonio, Nadja Piedade Fornazin, Marcelo de Araujo, Renata Mendes dos Santos, Rodrigo Pereira PPGI - Programa de Pós-Graduação em Informática UNIRIO - Universidade Federal do Estado do Rio de Janeiro Rio de Janeiro Brazil National School of Public Health of Oswaldo Cruz Foundation Rio de Janeiro Brazil Computer Science Department Federal Fluminense University Niterói Brazil Mackenzie Presbyterian University São Paulo Brazil Graduate Program in Information Systems University of São Paulo São Paulo Brazil

ISBN: (纸本)9783030658274

Although the Brazilian "Bolsa Família" program (BFP) was largely discussed due to its economic and social outcomes, little is known about the Information Systems (IS) that support BFP. In the context of BFP, CadÚnico system emerged as the registry for citizens who seek to qualify for BFP income transfers, as well as it is an important source of information for other public policies. This paper presents a case study of BFP’s evolution over decades through the CadÚnico *** analyze howthe CadÚnico system evolved in Brazil from a fragmented, offline databases in the early 2000 to a unified, online registry essential to BFP operation. Based on enrolment operations, the paper explains how CadÚnico first attracted other ministries from the Federal Government and later attracted municipalities to data registering. By analyzing BFP IS, we bring more evidences to the political and incremental nature of IS development in developing countries. © IFIP International Federation for Information Processing 2020.

关键词： Information systems

RECENT ADVANCES IN DEEP LEARNING APPLIED TO SKIN CANCER DETECTION

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arXiv 2019年

作者： Pacheco, Andre G.C. Krohling, Renato R. Graduate Program in Computer Science PPGI Federal University of Espírito Santo UFES Av. Fernando Ferrari 514 Vitória-ES Brazil Production Engineering Department Graduate Program in Computer Science PPGI Federal University of Espírito Santo UFES Av. Fernando Ferrari 514 Vitória-ES Brazil

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take. Copyright © 2019, The Authors. All rights reserved.

关键词： Dermatology

The impact of patient clinical information on automated skin cancer detection

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arXiv 2019年

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Dermatology

Deep learning for classification and severity estimation of coffee leaf biotic stress

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arXiv 2019年

作者： Esgario, José G. M. Krohling, Renato A. Ventura, José A. Federal University of Espìrito Santo Brazil PPGI - Graduate Program in Computer Science Production Engineering Department Incaper Rua Afonso Sarlo 160 Bento Ferreira Vitòria ES29052-010 Brazil

Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations. Copyright © 2019, The Authors. All rights reserved.

关键词： Agriculture

Turning social news curation into microtask crowdsourcing: a vision and research agenda

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Turning social news curation into microtask crowdsourcing: a...

IEEE International Conference on Systems, Man and Cybernetics

作者： Daniel Schneider António Correia Ramon Chaves Ana Paula Pimentel Marcio Antelio Edson Mello Lucas Marcos Antonio de Almeida Luiz Oliveira Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Graduate School of Engineering CEFET-RJ Rio de Janeiro Brazil Polytechnic Institute IPRJ/UERJ Rio de Janeiro Brazil Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Federal Institute of Rio de Janeiro (IFRJ) Rio de Janeiro Brazil

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

关键词： Crowdsourcing Industries Conferences Government Collaboration Turning Cybernetics

Improving Students Skills to Solve Elementary Equations in K-12 programs Using an Intelligent Tutoring System

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Improving Students Skills to Solve Elementary Equations in K...

Frontiers in Education (FIE) Conference

作者： Arcanjo Miguel Mota Lopes José Francisco de Magalhães Netto Ricardo A. L. de Souza Andreza Bastos Mourão Thais Oliveira Almeida Dhanielly P. R. de Lima Post-Graduate Program in Informatics (PPGI) Institute of Computing (IComp) Federal University of Amazonas (UFAM) Manaus AM Brazil Institute of Exact Sciences (ICE) Federal University of Amazonas (UFAM) Manaus AM Brazil University of State of Amazonas (UEA) Post-Graduate Program in Informatics (PPGI) Institute of Computing (IComp) Federal University of Amazonas (UFAM) Manaus AM Brazil Department of Science Computer Federal University of Roraima Boa Vista RR Brazil

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This full paper of research to practice category describes an experiment using an Intelligent Tutoring System to support teaching and learning that evolve solutions of first-degree equations. The difficulties encountered in solving algebra problems can make students consider math and related science a difficult task. We propose a Tutor to promote the learning of this class of problems. In this article, we describe the functioning and architecture of the tutor composed by the Interface, Student Model, Tutorial Strategies and Domain Model. Also, a Virtual Assistant was included to motivate students in the learning process. The tutoring strategies that comprise the tutor are projected from models found in the literature. To approximate the interactions of the tutor with the actions of the teachers, Lev Vygotsky's Concept of Zone Proximal Development was used. The methodology used in this project was the case study applied in a real-life context, obtaining a combination of quantitative and qualitative evidence about technical and pedagogical usability that show the degree of satisfaction of the students regarding the proposed approach.

关键词： Education Mathematical model Informatics Abstract algebra Cognition

Ranking of Classification Algorithms in Terms of Mean–Standard Deviation Using A-TOPSIS

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Annals of Data science 2018年第1期5卷 93-110页

作者： Pacheco, André G. C. Krohling, Renato A. Graduate Program in Computer Science PPGI UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 VitóriaESCEP 29060-270 Brazil Production Engineering Department UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 VitóriaESCEP 29060-270 Brazil

In classification problems when multiple algorithms are applied to different benchmarks a difficult issue arises, i.e., how can we rank the algorithms? In machine learning, it is common to run the algorithms several times and then a statistic is calculated in terms of means and standard deviations. In order to compare the performance of the algorithms, it is very common to employ statistical tests. However, these tests may also present limitations, since they consider only the means and not the standard deviations of the obtained results. In this paper, we present the so-called A-TOPSIS, based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to solve the problem of ranking and comparing classification algorithms in terms of means and standard deviations. We use two case studies to illustrate the A-TOPSIS for ranking classification algorithms and the results show the suitability of A-TOPSIS to rank the algorithms. The presented approach can be applied to compare the performance of stochastic algorithms in machine learning. Lastly, to encourage researchers to use the A-TOPSIS for ranking algorithms, we also presented in this work an easy-to-use A-TOPSIS web framework. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Statistical tests