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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Durand R, José Ribamar Junior, Geraldo Braz Da Silva, Italo Francyles Santos Da Costa Oliveira, Rui Miguel Gil Applied Computing Group Federal University of Maranhão Vila Bacanga São Luís65080-805 Brazil Graduate Program in Adult Health - PPGSAD Federal University of Maranhão Vila Bacanga São Luís65080-805 Brazil

Penile cancer, although rare, has an increasing mortality rate in Brazil, highlighting the need for effective diagnostic methods. Artificial Intelligence (AI) in histopathological analysis can speed up and objectify diagnosis, but designing an ideal architecture is challenging. In this study, we propose a neural architecture search (NAS) methodology for detecting penile cancer in digital histopathology images. We explored different configurations of stem blocks and the inclusion of attention mechanisms, highlighting specific preferences depending on the magnification of the images. The results showed that the NAS methodology enabled the discovery of more accurate and optimized architectures for this task, surpassing conventional models. The proposed models achieve 89.5% and 88.5% F1-Score for 40X and 100X magnification, respectively. © 2025 The Author(s).

关键词： Lung cancer

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Analytical propagation of errors and uncertainties in deep neural networks in the domain of physical sciences

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Engineering Applications of Artificial Intelligence 2025年 152卷

作者： de Mello, Gerson Eduardo Nardelli, Vitor Camargo Righi, Rodrigo da Rosa Schirmer, Luiz Ramos, Gabriel de Oliveira Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos São Leopoldo Brazil EMBRAPII Competence Center for Digital Agriculture RS São Leopoldo Brazil

In recent years, neural networks have gained popularity for making predictions in various applications. However, these predictions come with inherent uncertainties due to factors such as data acquisition, parameter choices, network architecture, and the training process. These uncertainties, both epistemic and aleatory, require careful quantification and propagation for a comprehensive understanding of the predicted results. To address these requirements, we propose an algorithm called Analytical Propagation of Uncertainties (APU), which is capable of quantifying and assessing the uncertainties associated with predictions made by Multilayer Perceptron neural networks in the domain of physical sciences. APU, with its modular architecture, can provide model, data, and prediction uncertainties for pre-trained Multilayer Perceptron networks. While generally offering lower computational cost compared to Bayesian methods, the efficiency and effectiveness of APU may vary depending on the specific application and model complexity. To the best of our knowledge, this is the first method able to propagate measurement uncertainty, originating from the calibration of measuring instruments, in the training process. To evaluate the effectiveness of data uncertainty estimation, we compared our approach against Monte Carlo Dropout in the Airfoil Self-Noise dataset. The analysis revealed that APU is more realistic than Monte Carlo Dropout and satisfactorily matched Monte Carlo simulations. Thus, the APU method becomes promising under real-world conditions, especially in the field of physical sciences, where low computational cost and immediate response are essential. This is significant in edge computing scenarios, real-time monitoring, addressing concerns such as latency, bandwidth, and data privacy. The study contributes to advancing the application of deep learning in physical sciences. The source code is available at https://***/Geduardomello/Dissertation. © 2025 Elsevier

关键词： Multilayer neural networks

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HistAttentionNAS: A CNN built via NAS for Penile Cancer Diagnosis using Histopathological Images

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Procedia Computer Science 2025年 256卷 764-771页

作者： José Ribamar Durand R. Geraldo Braz Junior Italo Francyles Santos da Silva Rui Miguel Gil da Costa Oliveira Applied Computing Group - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil Graduate Program in Adult Health - PPGSAD - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil

关键词： Penile Cancer Detection Histopathologic Classification Neural Architecture Search

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A linguistic approach to short sentences keywords identification for a question answering system

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International Journal of Web Engineering and Technology 2019年第4期14卷 367-382页

作者： Araujo, Denis Rigo, Sandro José Schmitt, Bruna Koch Hentges, Alencar Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - UNISINOS São Leopoldo Brazil

One of the aims of question answering systems is to identify which words are more relevant to understand the users' needs. Known approaches involve the identification of the users' intentions through a set of previously built related sentences. Some limitations of these approaches are the lack of flexibility and limited selection options. In this paper, we present an approach based on computational linguistics to identify the keywords in short sentences for question answering systems. The main contribution of our approach is related to the new way we use the information generated by the natural language processing tools to identify the keywords of the sentences, by profoundly exploring the linguistic information to select the keywords of the questions. Besides, we emphasise the generalisation and the simplicity of our algorithm. The efficiency of our method was proved by the performance of 0.9776 in precision, recall value of 0.9962, resulting in an F1 score of 0.9868 reached in the validation experiment using QALD-7 as a gold standard. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Natural language processing systems

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An IoT Transport Architecture for Passenger Counting: A Real Implementation 17

An IoT Transport Architecture for Passenger Counting: A Real...

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17th IFIP/IEEE International Symposium on Integrated Network Management, IM 2021

作者： Kniess, Janine Rutke, Julio Cezar Castaneda, William Alberto Cruz Santa Catarina State University Graduate Program in Applied Computing Brazil

ISBN: (纸本)9783903176324

A smart urban mobility considers the technological-based development in transport systems, services and their use to improve the movement of people and goods inter or intra-cities. Cities increasingly face problems caused by traffic and need to find solutions to improve mobility and to reduce congestion and pollution. In this paper, we present a Passenger Counting Scheme into buses. Embedded counting algorithms based on Computer Vision are proposed to count passenger arrivals and departures in real time. Experiments in a real scenario expose that the proposed scheme achieved a high percentage of success in passenger count. © 2021 IFIP.

关键词： Smart city

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Data Science Approach to Analysis of Lattes CV Data 4

Data Science Approach to Analysis of Lattes CV Data

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4th Annual International Symposium on Information Management and Big Data, SIMBig 2017

作者： De Santana, Thiago Luís Viana Santos, Rafael Applied Computing Graduate Program INPE – Nat. Inst. for Space Research Brazil Applied Computing Graduate Program INPE – Nat. Inst. for Space Research Brazil

The Lattes Platform is an online database of academical records. It is used by the research and educational community of Brazil (and some other countries), being of great value for identification of researchers and their relationships with other researchers and, for that, it can be considered a specialized kind of social network. In spite of its usefulness, the main interface of access to its data does not allow any type of analysis, just basic reports. In this paper, we present a new tool and approach to analysis of groups of records from the Lattes Platform which is simpler and more flexible than other similar tools proposed in the past.

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DCANon: Towards Distributed Certification Authority (CA) with Non-fungible Token (NFT) 37th

DCANon: Towards Distributed Certification Authority (CA) wit...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Descio-Trineto, Rafael Pillon, Maurício Koslovski, Guilherme Miers, Charles Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783031290558

Distributed applications rely on Certification Authorities (CAs) entities associated with a few institutions around the world. There are countless trusted institutions on the networks, e.g., universities, governments, banks, making effective use of CAs. Although largely used, the decentralized management of CAs is an administrative challenge. In this context, the joint emergence of digital ledgers and Non-Fungible Tokens (NFTs) compose a new opportunity to effectively decentralize single assets safely. Towards Distributed Certification Authority with Non-Fungible Token (DCANon) is a distributed Certification Authority (CA) built to trusted institutions and supported by Non-Fungible Token (NFT) concepts. DCANon changes the way revocation may be find, permitting that all consortium member crawler the blockchain for revoked certificates, independently from which CA this revocation occurs. A proof-of-concept prototype was implemented with Hyperledger Fabric demonstrating the benefits of DCANon. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distributed ledger

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DeepScheduling: Grid computing Job Scheduler Based on Deep Reinforcement Learning 34th

DeepScheduling: Grid Computing Job Scheduler Based on Deep R...

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34th International Conference on Advanced Information Networking and Applications, AINA 2020

作者： Casagrande, Lucas C. Koslovski, Guilherme P. Miers, Charles C. Pillon, Maurício A. Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783030440404

Grid systems are large-scale platforms which consume a considerable amount of energy. Several efficient resource/power management strategies were proposed by the specialized literature. However, most of the proposed strategies are rule-based policies which do not exploit workload patterns. Deploying the same set of rules on systems using different usage patterns, and platform settings, may lead to a sub-optimized setup. Due to the complex nature of grid systems, tailoring such a system-specific policy is not a straightforward task. In this paper, we explore a Deep Reinforcement Learning (DRL) method to build an adaptive energy-aware scheduling policy. We trained our algorithm using real workload traces from Grid’5000 platform. Our experiments pointed out an energy setup saving up to 7%, as well as average requests waiting time reduction of 27%. Finally, the resuslts clarify the importance of explore the workload to build system-specific policies. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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SSL-C4.5: Implementation of a Classification Algorithm for Semi-supervised Learning Based on C4.5 9th

SSL-C4.5: Implementation of a Classification Algorithm for S...

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9th Brazilian Conference on Intelligent Systems, BRACIS 2020

作者： Ortiz-Díaz, Agustín Alejandro Bayer, Flavio Roberto Baldo, Fabiano Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783030613792

Classification algorithms have been extensively studied in many of the major scientific investigations in recent decades. Many of these algorithms are designed for supervised learning, which requires labeled instances to achieve effective learning models. However, in many of the real human processes, data labeling is expensive and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without a significant loss of model performance. This paper presents the Semi-Supervised Learning C4.5 algorithm (SSL-C4.5) designed to work in scenarios where only a small part of the data is labeled. SSL-C4.5 was implemented over the J48 implementation of the C4.5 algorithm available at the WEKA platform. The J48 was modified incorporating a metric for semi-supervised learning. This metric aims at inducing decision tree models able to analyze and extract information from the entire training dataset, including instances of unlabeled data in scenarios where they are the majority. The assessment performed using eight different benchmark datasets showed that the new proposal has achieved promising results compared to the supervised version of C4.5. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

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An improved evolutionary hybrid method for cloud provider selection 8

An improved evolutionary hybrid method for cloud provider se...

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8th Brazilian Conference on Intelligent Systems, BRACIS 2019

作者： Borges De Moraes, Lucas Fiorese, Adriano Stubs Parpinelli, Rafael Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9781728142531

Cloud computing (CC) popularization unleashed the creation and competition of a large number of new companies providing CC services, the so called Cloud Providers (CPs). Thus, select the smallest CP set with the lowest price that are the most suitable to a given request (customer's needs, such as demanded resources and desired service quality) becomes a hard problem. Nonetheless, robust search methods like metaheuristics ones are used to solve it, for instance Evolutionary Algorithms (EAs). Performance Indicators (PIs) were used to quantify features of each CP used later to select them using a method. This paper proposes an improved hybrid method which uses in sequence: a deterministic, an EA and a local search method to find the most suitable possible CP set in the solution search space. This hybrid method was tested and has the highest assertiveness among all others methods developed to solve this problem, becoming a promising solution to a real application. © 2019 IEEE.

关键词： Benchmarking

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