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- 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.
- 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.
- 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.