Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks

Due to wearables’ popularity, human activity recognition (HAR) plays a significant role in people’s routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models’ decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR’s high performance with SD comes not only from physical activity learning but also from learning an individual’s signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability

Supporting a Knowledge Base With Evidence Retrieved From Randomized Controlled Clinical Trials: A Case Study

This work describes a methodology based on Evidence-Based Medicine for finding literature-based evidence for a clinical decision support system. As an illustration, we applied this method to parenteral nutrition therapy (PNT). PNT requires expertise and experience and is prone to errors. The Pico's strategy was used to built structured clinical questions, which considered 11 PN clinical indications plus a PN nutrient (amino acid, glucose, lipid, electrolyte, trace elements and vitamins), and an outcome. 211 PICO strategies were structured, and 447 searches at PubMed were performed. The results were classified in levels of evidence and recommendation grades according to criteria of the Oxford Centre for Evidence Based Medicine

Evaluation of Predisposing Factors of Necrotic Enteritis in Experimentally Challenged Broiler Chickens

Clostridium perfringens is the etiological agent of NE, a disease that greatly affects the poultry industry. Experiments on the induction of NE are difficult to carry out, as it is a multifactorial disease, and thus different predisposing factors have been used. This study evaluated the effect of the Gumboro disease vaccine virus vaccine (IBDV-vac) associated or not with infection by Eimeria spp. in broilers, as a predisposing factor for NE. Broilers (n = 99) were divided into groups (11) challenged with IBDV-vac, Eimeria spp. CP type G (CP13, CP14 and CP03) or both. The macroscopic evaluation revealed that the highest average (3.45) of injury occurred for the CP13 + IBDV-vac group. The microscopic analysis showed that Eimeria spp. increased the population of intraepithelial lymphocytes and reduced the villus/crypt ratio in duodenum and jejunum when associated with CP13 or CP14. There was a synergistic effect between the CP strain used and the predisposing factors; nevertheless, it was not clear which was the most effective predisposing factor to potentiate the lesions, suggesting that the association of the strain with the factors should first be evaluated for each experimental protocol