A Deep Learning Model Based on Capsule Networks for COVID Diagnostics through X-ray Images.

X-ray diagnostics are widely used to detect various diseases, such as bone fracture, pneumonia, or intracranial hemorrhage. This method is simple and accessible in most hospitals, but requires an expert who is sometimes unavailable. Today, some diagnoses are made with the help of deep learning algorithms based on Convolutional Neural Networks (CNN), but these algorithms show limitations.

Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling.

Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients.

Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking.

Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands.

Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics.

The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine.

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms.

Background: Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications.

Methods: We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search.

Results: A set of candidates trajectories were found for two surgical procedures.

KIR Genes and Patterns Given by the A Priori Algorithm: Immunity for Haematological Malignancies.

Killer-cell immunoglobulin-like receptors (KIRs) are membrane proteins expressed by cells of innate and adaptive immunity. The KIR system consists of 17 genes and 614 alleles arranged into different haplotypes. KIR genes modulate susceptibility to haematological malignancies, viral infections, and autoimmune diseases.

Support vector machine algorithms in the search of KIR gene associations with disease.

Killer-cell immunoglobulin-like receptors (KIR) are membrane proteins expressed by natural killer cells and CD8 lymphocytes. The KIR system consists of 17 genes and 614 alleles, some of which bind human leukocyte antigens (HLA). Both KIR and HLA modulate susceptibility to haematological malignancies, viral infections and autoimmune diseases.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction).