Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

The properties of robustness and finite‐time convergence provided by sliding mode (SM) theory have motivated several researches to deal with the problems of control and state estimation. In the SM theory, the super‐twisting algorithm (STA), a second‐order SM scheme, has demonstrated remarkable characteristics when it is implemented as a controller, observer or robust signal differentiator although the presence of noise and parametric uncertainties. However, the design of this algorithm was originally developed for continuous‐time systems. The growth of microcomputers technology has attracted the attention of researchers inside the SM discrete‐time domain. Recently, discretisations schemes for the STA were studied using majorant curves. In this study, the stability analysis in terms of Lyapunov theory is proposed to study a discrete‐time super twisting‐like algorithm (DSTA) for non‐linear discrete‐time systems. The objective is to preserve the STA characteristics of robustness in a quasi‐sliding mode regime that was proved in terms of practical Lyapunov stability. An adequate combination of gains obtained by the same Lyapunov analysis forces the convergence for the DSTA. The problem of state estimation is also analysed for second‐order mechanical systems of n degrees of freedom. Simulation results regarding the design of a second‐order observer using the DSTA for a simple pendulum and a biped model of seven degrees of freedom are presented.

Worldwide, fuzzy systems are becoming a very useful mathematical tool to deal with nonlinear problems by inferring from a rule base that contains the necessary knowledge extracted from an expert. There are diverse forms to implement a fuzzy system depending on the desired response time, for which hardware implementations are best suited for high-speed demands. Fuzzy hardware has been exploited since the mid-1980s, and there have been many interesting architecture cases with different functional and performance characteristics that are reviewed in this paper from a categorical and historical point of view.

The path planning process for an unmanned aerial vehicle is a complex process due to different operating environment conditions and aircraft constraints. The particle swarm optimization algorithm is one of several heuristic techniques used to solve the path planning problem. However, it has been observed that this algorithm tends to converge prematurely to a local optimum, resulting in poor quality paths. This paper proposes an adaptive strategy for calculating the inertia weight (KPSO) of the particles in the PSO algorithm for UAV path planning in a real-world environment. The strategy was applied to a reconnaissance mission during an eruptive event based on the Popocatepetl volcano area in Mexico and was found to outperform other adaptive strategies from the literature in a specific scenario.