VALORIZA - Centro de Investigação para a Valorização de Recursos Endógenos
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- Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic reviewPublication . Pinto, Rui João; Silva, Pedro Miguel; Duarte, Rui P.; Marinho, Francisco Alexandre; Pimenta, Luís; Gouveia, António Jorge; Gonçalves, N.J.A.P.; Coelho, Paulo; Zdravevski, Eftim; Lameski, Petre; LEITHARDT, VALDERI; Garcia, Nuno M.; Pires, Ivan MiguelThe prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.
- An architectural proposal to protect the privacy of health data stored in the BlockchainPublication . Sega, Christofer L.; De Moraes Rossetto, Anubis Graciela; Correia, Sérgio; LEITHARDT, VALDERIA Blockchain é um livro razão público, descentralizado e distribuído na rede peer-to-peer que utiliza uma estrutura de blocos para verificar e armazenar dados, empregando um mecanismo de consenso confiável. Com o rápido desenvolvimento dessa tecnologia nos últimos anos, diversas preocupações e empecilhos para a sua aplicação em alguns cenários começaram a surgir, dentre eles a privacidade sendo um dos tópicos apontados em vários trabalhos. Este trabalho propõe uma arquitetura para garantir a privacidade dos dados relacionados à área da saúde, que são armazenados dentro de uma rede Blockchain de maneira descentralizada, através do uso de técnicas de criptografia que serão comparadas como o RSA (Rivest-Shamir-Adleman) e o ECC (Elliptic Curve Cryptography).
- An Architecture for Managing Data Privacy in Healthcare with BlockchainPublication . De Moraes Rossetto, Anubis Graciela; Sega, Christofer Luiz; LEITHARDT, VALDERIWith the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC, and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage, and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, which is justifiable considering the privacy and security provided with the architecture and encryption.
- An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International TradePublication . Lima, Ligia; Fernandes, Anita; James Roberto Bombasar; Da Silva, Bruno Alves; Crocker, Paul; LEITHARDT, VALDERI
- Análisis psicométrico de versiones cortas del Big Five Inventory en universitarios mexicanosPublication . Dominguez Lara, Sergio Alexis; Campos-Uscanga, Yolanda; Valente, Sabina
- Análisis psicométrico y datos normativos de la UWES en adolescentes peruanosPublication . Dominguez-Lara, Sergio; Peceros Pinto, Benigno; Centeno-Leyva, Sharon; Valente, Sabina; Lourenço, Abílio Afonso; Quistgaard-Alvarez, Alberto; Morales-Velasquez, Mercedes Patricia
- Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression ControlPublication . Alves Araujo, Rafael Silfarney; Tironi, Jéssica Cristina; D. Parreira, Wemerson; Coelho Borges, Renata; Ruiz Juan, Francisco; LEITHARDT, VALDERIThe increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.
- Analysis of MQTT-SN and LWM2M communication protocols for precision agriculture IoT devicesPublication . PEREIRA DOS SANTOS, ROGERIO; LEITHARDT, VALDERI; Beko, MarkoA Internet das Coisas (IoT) tem se tornado parte integrante do estilo de vida da sociedade moderna e uma ferramenta importante em muitas áreas de negócios. Nos últimos anos, verificou-se uma grande necessidade de conectar novos dispositivos IoT à agricultura de precisão. Conhecidos como objetos conectados, esses vêm ganhando cada vez mais força. Assim como a adoção da IoT para agricultura, residências, cidades inteligentes, logística, saúde, manufatura e outros. Existe também inúmeras preocupações em relação à comunicação destes dispositivos. Com a capacidade de coletar dados, a tecnologia IoT se torna um recurso valioso e cuidados devem ser tomados na busca por mecanismos eficazes de comunicação. Nesse sentido, este trabalho tem como objetivo apresentar uma análise dos protocolos de comunicação MQTT-SN e LWM2M, comparando seus desempenhos na transmissão de mensagens. O modelo foi desenvolvido com auxílio da ferramenta Node-RED, que consiste em programação baseada em fluxo na avaliação e desempenho implementado em tempo de execução. Ao final das simulações, foi possível avaliar que o protocolo MQTT-SN apresentou melhores resultados nos testes realizados.
- Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures MarketsPublication . Aslam, Faheem; Ferreira, Paulo; Ali, HaiderThe investigation of the fractal nature of financial data has been growing in the literature. The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the efficiency of agricultural futures markets by using multifractal detrended fluctuation analysis (MF-DFA). To better understand the relative changes in the efficiency of agriculture commodities due to the pandemic, we split the dataset into two equal periods of seven months, i.e., 1 August 2019 to 10 March 2020 and 11 March 2020 to 25 September 2020. We used the high-frequency data at 15 min intervals of cocoa, cotton, coffee, orange juice, soybean, and sugar. The findings reveal that the COVID-19 pandemic has great but varying impacts on the intraday multifractal properties of the selected agricultural future markets. In particular, the London sugar witnessed the lowest multifractality while orange juice exhibited the highest multifractality before the pandemic declaration. Cocoa became the most efficient while the cotton exhibited the minimum efficient pattern after the pandemic. Our findings show that the highest improvement is found in the market efficiency of orange juice. Furthermore, the behavior of these agriculture commodities shifted from a persistent to an antipersistent behavior after the pandemic. The information given by the detection of multifractality can be used to support investment and policy-making decisions.
- Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning MethodsPublication . Stefenon, Stefano Frizzo; Bruns, Rafael; Sartori, Andreza; Meyer, Luiz Henrique; Garcia, Raul; LEITHARDT, VALDERIOutdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).