How Validation Methodology Influences Human Activity Recognition Mobile Systems.

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results.

Single-Dose Anti-Thymocyte Globulin Compared With Divided-Dose for Induction Therapy in Kidney Transplantation in a Predominantly Black Population.

Background: The aim of this study was to compare single-dose rabbit anti-thymocyte globulin (rATG) with a divided dose in kidney transplant recipients within a majority Black patient population.

Methods: We analyzed the outcomes before and after a change in protocol from divided-dose (1.5 mg/kg/day over 4 days) to single-dose (6 mg/kg over 24 hours) rATG in a retrospective cohort study.

Using amino acids co-occurrence matrices and explainability model to investigate patterns in dengue virus proteins.

Background: Dengue is a common vector-borne disease in tropical countries caused by the Dengue virus. This virus may trigger a disease with several symptoms like fever, headache, nausea, vomiting, and muscle pain. Indeed, dengue illness may also present more severe and life-threatening conditions like hemorrhagic fever and dengue shock syndrome.

Estimating ecoacoustic activity in the Amazon rainforest through Information Theory quantifiers.

Automatic monitoring of biodiversity by acoustic sensors has become an indispensable tool to assess environmental stress at an early stage. Due to the difficulty in recognizing the Amazon's high acoustic diversity and the large amounts of raw audio data recorded by the sensors, the labeling and manual inspection of this data is not feasible. Therefore, we propose an ecoacoustic index that allows us to quantify the complexity of an audio segment and correlate this measure with the biodiversity of the soundscape.

A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory.

Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost.