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- 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
- Application of Machine Learning Techniques to Predict a Patient s No-Show in the Healthcare SectorPublication . Salazar, Luiz Henrique; LEITHARDT, VALDERI; D. Parreira, Wemerson; Fernandes, Anita; Barbosa, Jorge; Correia, S. D.
- Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature ReviewPublication . Freitas, Mauricio; Vinícius Aquino Piai; Ricardo Heffel Farias; Fernandes, Anita; De Moraes Rossetto, Anubis Graciela; LEITHARDT, VALDERIAccording to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.
- Image Segmentation for Human Skin DetectionPublication . Leite, Marcelo; D. Parreira, Wemerson; Fernandes, Anita; LEITHARDT, VALDERIHuman skin detection is the main task for various human–computer interaction applications. For this, several computer vision-based approaches have been developed in recent years. However, different events and features can interfere in the segmentation process, such as luminosity conditions, skin tones, complex backgrounds, and image capture equipment. In digital imaging,skin segmentation methods can overcome these challenges or at least part of them. However, the images analyzed follow an application-specific pattern. In this paper, we present an approach that uses a set of methods to segment skin and non-skin pixels in images from uncontrolled or unknown environments. Our main result is the ability to segment skin and non-skin pixels in digital images from a non-restrained capture environment. Thus, it overcomes several challenges, such as lighting conditions, compression, and scene complexity. By applying a segmented image examination approach, we determine the proportion of skin pixels present in the image by considering only the objects of interest (i.e., the people). In addition, this segmented analysis can generate independent information regarding each part of the human body. The proposed solution produces a dataset composed of a combination of other datasets present in the literature, which enables the construction of a heterogeneous set of images.
- Decision Support Using Machine Learning Indication for Financial InvestmentPublication . Oliveira, Ariel Vieira de; Dazzi, Márcia Cristina Schiavi; Fernandes, Anita; Dazzi, Rudimar Luis Scaranto; Ferreira, Paulo; LEITHARDT, VALDERITo support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniqueswere implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.
- Improving Speaker Recognition in Environmental Noise with Adaptive FilterPublication . Almeida Dos Santos, Vinícius; D. Parreira, Wemerson; Fernandes, Anita; RAUL, GARCIA; LEITHARDT, VALDERISpeaker recognition is challenging in real-world environments. Typically, studies approach noises only in an additive manner. However, real environments commonly present everberating conditions that worsen speech processing. When not considering reverberation in the system modeling, the system may not be robust when applied to real-world conditions. In this work, we use a slight different approach to simulate reverberation, considering randomized conditions of the environment. With this approach, each VoxCeleb1 test sample is corrupted by randomly generated conditions, with diversified amplitudes of noise and speech. We generate a corrupted dataset, in which the best model EER degraded from 0.93% to 30.13%. To improve this degradation, we propose using Normalized Kernel Least-Mean-Square (NKLMS) adaptive filter. Through the use of NKLMS, we were able to improve the EER from 30.13% to 1.11%. The results indicate that NKLMS has a great potential for speech enhancement to improve speaker recognition.
- No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature ReviewPublication . Salazar, Luiz Henrique; D. Parreira, Wemerson; Fernandes, Anita; LEITHARDT, VALDERINo-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling.