This paper proposes automatically estimating postural balance test scores and their subsystems using stabilometry results. These results are obtained using a force platform that captures the variation in the person's center of pressure position over time. On the other hand, postural balance test scores are conducted by qualified health professionals and comprise the assessment of various subsystems that can provide specific diagnoses separately and, when combined, can form a comprehensive assessment method called the MINI-BESTest. Early diagnosis makes it possible to identify the risk of falls due to advancing age or related limiting illnesses. Using a hierarchical approach, which makes it possible to automate the process and reduce the subjectivity of the assessment, it is possible to estimate the MINI-BESTest score and its Reactive Postural Control subsystem with an accuracy of, respectively, 17% and 20% higher than the state-of-the-art.

Telecommunications improvements in the last decades have led to an increase in the available data rate and reliability, and to reduce latency. Massive MIMO (mMIMO) has emerged as a promising solution to replace simple antenna setups. This arrangement involves a large number of transmitter antennas compared to the served terminals in the covered area, enabling higher data rates through the key characteristics of mMIMO. Despite its advantages, the utilization of such large arrays is energy-intensive. With fewer antennas, it is possible to achieve high data rates with reduced power consumption. Antenna selection is crucial for energy efficiency while maintaining computational efficiency. Additionally, power allocation in the downlink for each served terminal is an important step to consider. This work aims to propose a joint algorithm for selecting antennas and distributing energy among terminals, simplifying the process through low-complexity algorithms. The results show the penalty in sum-rate capacity for each proposed method. The two proposed algorithms demonstrate an adequate approach to the target value asymptotically as the number of transmitter antennas increases. This implies the possibility of achieving the joint process with significantly lower computational costs compared to perform antenna selection and power allocation separately.

The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree – DT). The DT is trained with an artificial dataset generated by Genetic Algorithms (GAs). The primary objective of this approach is to replicate the decision boundaries of the black-box model in proximity to the instance under explanation. We show that the artificial examples generated by the GAs in LORE are not necessarily diverse. Consequently, we propose the integration of GAs with fitness sharing in LORE to generate a more diversified subset of artificial examples. The underlying motivation is to ensure that the local decision boundaries of the DT more closely resemble those of the black-box classifier. Experimental results with two classifiers (Multilayer Perceptron and Random Forests), and four classification problems, indicate that LORE with fitness sharing yields more diverse GA populations, consequently leading to improved local explanations. These findings underscore the effectiveness of incorporating fitness sharing into the LORE methodology for enhancing the explainability of black-box classifiers.

Breast cancer is a neoplastic disease that can be diagnosed either as benign or malign according to the growth-rate of the neoplastic lesion. Owing to the relevance of obtaining better detection tools, this work describes the development and optimization of support vector machines for the classification of the types of such cancer. Tests were performed using the breast cancer dataset of the University of Wisconsin Hospitals, USA, available at the Machine Learning Repository of the University of California Irvine. The radial basis function kernel was selected for the classifier and its hyperparameters were refined using two methods: particle swarm optimization and genetic algorithms. The results for the first method exhibited 97.71% accuracy, 96.30% sensitivity, and 98.65% of selectivity. On the other hand, using the second method, the accuracy was 95.78%, with sensitivity and selectivity of 96.73% and 95.25%, respectively. Therefore, there is an indication that these search algorithms are viable tools to optimize machine learning models for the purpose of breast cancer classification.

The growing number of vehicles in cities has a great impact on our quality of life, such as air and noise pollution, traffic jams and traffic accidents. Cooperative Intelligent Transportation System (C-ITS) relies on communication technologies to provide innovative services and applications for transportation and traffic management. In the C-ITS context, users, roadside infrastructure and vehicles need to be connected and, for this purpose, a wide variety of wireless technologies can be used (e.g, vehicular WiFi, cellular and visible light communication). In this work we consider a VANET (Vehicular Ad-hoc NETwork) using vehicular WiFi (based on 802.11p). The communications in VANET networks have been studied for years and several routing algorithms have been developed for such a kind of network. However, a benchmark to compare the performance of such algorithms is still lacking. To fill this gap, the present work proposes a benchmark composed by instances of data routing for different scenarios in the VANET. Moreover, we propose a multi-objective algorithm based on ACO (Ant Colony Optimization) to compare with such benchmark. The results of simulations show the impact of several factors in the VANET connectivity, such as vehicle density, geographical location, propagation and fading models. The results are promising and indicate the importance of choosing appropriated simulation models.

The coronavirus disease 2019 (COVID-19) has been declared by the World Health Organization (WHO) as an unprecedented pandemic in the present days, straining healthcare systems due to the high demand for admissions to intensive care units. In this context, estimating the dynamics of the COVID19 pandemic is essential to deal with health system drawbacks. Therefore, in this work we developed an empirical study on time series related to the COVID-19 pandemic and, based on this study, we present a deep morphological-linear model, trained by a gradient-based learning process, able to predict this particular kind of time series. Trying to assess the predictive performance of the proposed model, we use daily COVID-19 time series in Brazil and United States of America. The achieved results show that the proposed model outperforms classical and recent machine learning models to estimate the dynamics of the COVID-19 pandemic.

This work presents a method to choose the integration step-size h for discretization of nonlinear and chaotic dynamic systems, in order to obtain a simulation with numerical reliability.In this context, the Lower Bound Error is used as an auxiliary technique in the search for the optimal value of h, considering the Fourth Order Runge Kutta as the discretization method.The Lorenz equations, Rössler equations and Duffing-Ueda oscillator were used as case studies.This work, besides investigating the most adequate step-size h for each case, shows that the choice of very small values of h results in significantly inferior solutions, despite the consensus that the smaller the step-size, the higher the accuracy.