scholarly journals Optimization of a Wireless Sensor Network-Based Smart Elderly Location Management System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Biying Wang ◽  
Dingzhuang Chen ◽  
Liangpeng Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Management System ◽  
Health Management ◽  
Location Management ◽  
The Elderly ◽  
Home Health ◽  
Prediction Algorithm ◽  
Unit Testing ◽  
Management Platform

In this paper, wireless sensors are used to design and build a location management system for smart aging, and the optimization of this system is analysed. The function and structure of the positioning system are designed and implemented. Next, the system commissioning process and field implementation results are summarized and analysed to guarantee the reliability and integrity of the system. Finally, the system requirements and implementation are integrated to realize the basic requirements of the IoT-based elderly positioning management system and reserve the interface for later expansion. Finally, the system excellence and functional usability tested through unit testing as well as comprehensive testing. In this paper, a theoretical analysis of the prediction algorithm is carried out for the disease prediction module in the health monitoring software of the smart senior care system. To improve the accuracy of the prediction, the traditional BP neural network algorithm is optimized using DS evidence theory, thus fusing multiple sets of prediction results obtained from the BP neural network into a more accurate set of data, and the performance of the algorithm before and after the improvement is compared. The IoT-based home health management for the elderly starts from the health service demand of the elderly, explains the basic concept of IoT technology and home health management for the elderly, and analyses the feasibility of home health management for the elderly and the advantages of IoT technology in-home health management for the elderly; through the field research, the IoT-based home health management platform for the elderly is carried out from three aspects of users, business, and technology. The design covers the platform architecture and functional modules of the IoT-based senior home health management platform, which can solve the problem that the elderly can spend a comfortable life in their old age without going home.

scholarly journals Unimed Recife's experience in treating 1,039 patients with Covid-19

2021 ◽  
Author(s):  
Marcelo Moraes Valença ◽  
Martha Maria Romeiro Figueiroa F. Fonseca ◽  
Cátia Arcuri Branco ◽  
Alex Maurício Garcia Santos ◽  
Antonio Oliveira ◽  
...  
Keyword(s):  
Health Management ◽  
Demographic Data ◽  
The Elderly ◽  
Obese Patients ◽  
Mechanical Ventilator ◽  
Average Hospital Stay ◽  
Average Hospital ◽  
Management Platform ◽  
Pathophysiological Process ◽  
Number Of Patients

ObjectiveTo describe the features related to patients with Covid-19 admitted to Unimed Recife hospitals, Recife, Brazil, evaluating demographic data, lethality, use of a mechanical ventilator, presence of associated diseases, the need to use the ICU, among other aspects related to the prognosis of these patients.MethodData were collected from the DRG Brazil health management platform, including the period from March 16, 2020, when the first patient with Covid-19 was admitted to the Hospital da Unimed III, until January 31, 2021. All patients admitted to one of the three hospitals of Unimed Recife - Hospital Unimed Recife I, Hospital Unimed Recife III, and Hospital Geral Unimed Recife – were included in the study. In the same period, we evaluated the number of patients with Covid-19 or suspected Covid-19 who were seen in the emergency room at Hospital Unimed Recife III.ResultsOne hundred twenty-six thousand five hundred fifty-three patients were seen in the Emergency Unit of Hospital Unimed Recife III in the period between March 26, 2020, and January 31, 2021; of those 126,553 patients seen in the emergency 39,340 (31.09%) patients were diagnosed with having Covid-19 or suspected of Covid-19. In the 10-month period, 1,039 patients with Covid-19 were hospitalized, 61% with hypertension, 31.1% with SARS, 30.0% with diabetes, and 9.9% were obese. The average hospital stay was 11.2 days. 342/1,039 (32.9%) patients were admitted to the ICU, and 57.9% of them had mechanical ventilation. The overall lethality was 13.76% (143 deaths/1,039 inpatients). An increase in lethality by Covid-19 was associated with increased age. Lethality in the first period of the Covid-19 pandemic was significantly higher when compared to the last 5 months of the pandemic(17.6% versus 9.7%). Obesity significantly increased lethality in patients with Covid-19 [120 deaths/1,016 non-obese patients (11.8%) versus 23 deaths/103 obese patients (22.3%), OR 2.15 (1.30 - 3.50), p = 0.005)].ConclusionWe conclude that Covid-19 is a disease with a poor prognosis, especially in the elderly and obese patients. In the second 5-month period of the Covid-19 pandemic, we noticed a significant reduction in lethality by Covid-19 in hospitalized patients. Covid-19 is a new disease and the mechanism by which the viruses multiply or how the pathophysiological process occurs in the infected organism are still barely understood.

scholarly journals Development of Customer Relationship Management System in E-Commerce Based on BP Neural Network

2012 ◽  
Vol 6-7 ◽  
pp. 995-999
Author(s):  
Mei Ling Zhou ◽  
Jing Jing Hao
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Bp Neural Network ◽  
Management System ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Feed Forward Network ◽  
Neural Network Algorithm ◽  
Hidden Layer

BP neural network can learn and store a lot of input - output mode mapping, without prior reveal the mathematical equations describe the mapping. The model based on BP neural network algorithm is constituted by an input layer, output layer and one hidden layer, three-layer feed forward network. CRM is to acquire, maintain and increase the methods and processes of profitable customers. The core of CRM is the customer value management, customer value; it is divided into the de facto value, potential value and model value. The paper presents development of customer relationship management system in e-commerce based on BP neural network. The experiment shows BP is superior to RFCA in CRM.

scholarly journals A health management system for large vertical mill

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091211
Author(s):  
Sugai Han ◽  
Ansheng Li ◽  
Hongchao Wang ◽  
Xiaoyun Gong ◽  
Liangwen Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Management System ◽  
Health Management ◽  
Back Propagation ◽  
Trend Prediction ◽  
Backtracking Search ◽  
Search Optimization ◽  
Backtracking Search Optimization Algorithm

The large vertical mill has complicated structure and tens of thousands of parts, which is a critical grinding equipment for slag and cinder. As large vertical mill always works in severe conditions, the on-line monitoring, timely fault diagnosis, and trend prediction are very important guarantees for the safe service and saving maintaining costs. To address this issue, the health management system for large vertical mill is developed. More specifically, in order to manage reservoirs of state-related running data, the intrinsic physic data, and diagnosis knowledge base, an entity-relationship-model-based database is first constructed. Based on the fault diagnosis reasoning of experts, the fault tree is developed and the fault diagnosis rules are derived. Especially, a hybrid condition prognosis method based on backtracking search optimization algorithm and neural network is developed, and in comparison with traditional back propagation neural network and ant colony neural network, the developed backtracking search optimization algorithm and neural network gets superior hybrid prediction performance in prediction accuracy and training efficiency. Finally, the health management system, including the functions of condition monitoring, fault diagnosis, and trend prediction for large vertical mill is implemented using Microsoft Visual Studio C # and Microsoft SQL Server.

Human resource management model based on three-layer BP neural network and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Guohua Wei ◽  
Yi Jin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Bp Neural Network ◽  
Management System ◽  
Learning Technology ◽  
Human Resource Management System ◽  
Resource Management System

At present, data is in a state of explosive growth. The rapid growth of data collected by enterprises has exceeded the processing capacity of traditional human resource management systems, resulting in their inability to perform data management and data analysis. In order to improve the practicality of the human resource management system, this paper applies machine learning technology to the human resource management system, selects dimensions according to the prediction method, and builds a combined model consisting of an optimized GM (1,1) model and a BP neural network model. The model is implemented by a three-layer BP neural network. In order to verify the performance of the research model, this article conducts research using an entity as an example. The research results show that the method proposed in this paper has certain practical effects and can improve the reference for subsequent related research.

Fall Detection Algorithm of the Elderly Based on BP Neural Network

2021 ◽  
Author(s):  
Qiushi Xiong ◽  
Danhong Chen ◽  
Ying Zhang ◽  
Zhen Gong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm

Application of BP Neural Network in Robot Soccer Competition System

2011 ◽  
Vol 338 ◽  
pp. 665-669
Author(s):  
Peng Li ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Linear Prediction ◽  
Prediction Algorithm ◽  
Competition System ◽  
Strategy System ◽  
System A ◽  
Multi Agent ◽  
And Control ◽  
Linear Prediction Model

Strategy system of small-size RoboCup robot is a Multi-Agent System of coordination and control . In order to solve the delay problem of small-size RoboCup competition system, this paper applies BP Neural Network to the situation prediction of strategy system. A linear prediction model based on BP Neural Network is established and Neural Network topology is ascertained. Then trained network is applied to the existing competition system and the efficiency in position, coordination and cooperation capability are greatly improved. The experiments show that the method can predict position and direction of robot correctly, thus proving the feasibility and superiority of prediction algorithm in the system.

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