ABSTRACT
SARS-CoV-2 has bought many challenges to the world, socially, economically, and healthy habits. Even to those that have not experienced the sickness itself, and even though it has changed the lifestyle of the people across the world nation wise the effects of COVID-19 need to be analyzed and understood, analyzing a large amount of data is a process by itself, in this document details the analysis of the data collected from México by the Secretary of Health, the data was analyzed by implementing statistics, and classification methods known as K-Means, C&R Tree and TwoStep Cluster, using processed and unprocessed data. With the main emphasis on K-means. The study has the purpose of detecting what makes the highest impact on a person, to get sick, and succumb to the effects of the disease. In the study, it was found that in México the age of risk is at its highest at the age of 57, and the ones at the highest risk of mortality are those with hypertension and obesity, with those that present both at the age of 57 having a 19.37% of death. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In current times where there are smart devices for households, and that apart from having different functions that are helpful in daily household chores, such as being able to maintain a full pantry, and to generate errand lists, in the market these devices have a high cost for this reason is that it is proposed to create a low cost smart device, in this document an analysis is made using data mining, with tensor flow, of the purchases generated by the users, derived from the current situation by the pandemic of the COVID-19, generated an increase in online shopping, this analysis is intended to be a support for online errand shopping to visualize classification and prediction in the comparisons of users. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic has led to the saturation of public health services worldwide. In this scenario, the early diagnosis of SARS-Cov-2 infections can help to stop or slow the spread of the virus and to manage the demand upon health services. This is especially important when resources are also being stretched by heightened demand linked to other seasonal diseases, such as the flu. In this context, the organisers of the DiCOVA 2021 challenge have collected a database with the aim of diagnosing COVID-19 through the use of coughing audio samples. This work presents the details of the automatic system for COVID-19 detection from cough recordings presented by team PANACEA. This team consists of researchers from two European academic institutions and one company: EURECOM (France), University of Granada (Spain), and Biometric Vox S.L. (Spain). We developed several systems based on established signal processing and machine learning methods. Our best system employs a Teager energy operator cepstral coefficients (TECCs) based frontend and Light gradient boosting machine (LightGBM) backend. The AUC obtained by this system on the test set is 76.31% which corresponds to a 10% improvement over the official baseline.
ABSTRACT
Every day there are more devices or objects that connect to the internet, these devices are found in different areas in the home, health, industry and others, this project is implemented in the internet of things for homes, it is a module called “nutrition”, the which allows you to have a record of diets provided by a nutritionist, this allows reducing costs and food waste because with the mobile application called: Mobile application for smart appliances (EInt), you can obtain the products directly from a preferred provider. Given the situation current due to the COVID-19 pandemic, exits to non-essential situations have been reduced, this prototype pretends to be a support for users when making errand purchases and avoid going out as little as possible. The analysis and design of the device contains several agents, this article describes the nutrition agent, the food ontology, its semantic network. © 2021, Springer Nature Switzerland AG.
ABSTRACT
This article presents the analysis and design of the intelligent agent model “IA-ACR”, which has the objective of monitoring movements, which are carried out in a coordinated and intelligent way in a robot, which will have the task of performing routines of physical exercises and dance, these routines will then be imitated by children with neurodevelopmental disorders (NDD), in order to capture their attention so that therapies are more effective, which will be evaluated by the specialist (psychologist). Due to the current situation of the pandemic that is being experienced due to COVID-19, health protocols were established, such as avoiding contact between people, given this restriction, a digital platform was developed that serves as support for children in order to receive their sessions, where the robot appears through videos, this being an advantage of telehealth. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Currently, there are various smart devices for homes, which have different functions that support domestic tasks such as keeping the pantry always full, accessing recipes, and creating shopping list. These devices have a high cost, for this reason, it arises in creating a low-cost smart device, which contains the options described above. In addition to having a module called “nutrition”, with which you can keep a record of meal plans and a diet provided by a nutritionist at a cost minor, for this, a mobile application was created called: Mobile application for smart appliances (MoASa) given the current situation due to the COVID-19 pandemic. It has resulted in many people taking shelter in their homes and taking care measures such as avoiding going out to non-essential situations, this pretends to be a support to carry out grocery shopping and avoid going out as little as possible. The analysis and design of the device contain several agents, this article described the nutrition agent, the food ontology, its semantic network, hardware, and software for communication with multiple applications that support the user. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
This paper presents the mathematical model Susceptible-Infected-Recovered SIR with parameters that describe the COVID-19 dynamics. This model is based on a system of ordinary differential equations in which appropriate conditions and starting parameter values such as transmission rates and recovery rates are considered known. These parameters are utilized to obtain a simulation of COVID-19 behavior, in order to establish a possible solution to avoid a greater chance of disease transmission. On the proposed scheme, we use a neural impulsive inverse optimal control for a complex network in which the dynamic of each node is a discrete version of SIR model that describe the dynamics of COVID-19. The neural network is trained with an extended Kalmans filter and is used as a neural identifier for the selected nodes of the system. The control law used represents a hypothetical treatment for COVID-19. This work aims to simulate the interaction of different populations during an epidemic outbreak in which populations are represented by the complex network nodes
ABSTRACT
This project's main objective is to discover which are those comorbidities that could lead to a fatal outcome in a patient diagnosed with COVID-19 in the state of Baja California through a classification algorithm using neural networks. For this, a database obtained on the federal government portal by the General Directorate of Epidemiology with a cutoff date of June 8, 2020 was used. Only the records of the residents of Baja California were kept and only the following data: Sex, Municipality, Date of death, Age, all those variables referring to morbidities, Result (Confirmed cases of COVID-19), ICU (If they needed to enter the intensive care unit);also, from the variable of the date also, from the date variable of death, another variable called "Deceased" was generated to categorize whether the patient died or not. The resulting database was imported into the software where the model of the neural network, data preparation was performed and built the neural network model (multilayer perceptron). The dependent variable "Deceased" was selected, as variables the variables referring to the patient's comorbidities and as a covariate the variable of the scalar type Age. For this model, a random partition of the data was carried out, where 70% of the data was assigned for training and the remaining 30% for tests, obtaining a success rate of 82% and an 18 % error.