Management for stroke intelligent early warning empowered by big data.

Global aging population, especially with the global pandemic outbreak of the Corona Virus Disease 2019 (COVID-19), has endangered human health security. Digital information technology through big data empowerment and intelligent application is widely considered a key element to solve the problems. Stroke is a life-threaten disorder. We studied individual health management and disease risk perception using human health assessment model and make full use of wearable wireless sensor, Internet of Things, big data, and Artificial Intelligence for potential risk monitoring and real-time stroke warning. We proposed an effective method of monitoring, early warning and rescue to improve the stroke treatment. The result shows that the health management empowered by big data can generate new opportunities and ideas to solve early detection and warning of stroke.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation.

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

Intelligent Monitoring Model for Aggregated Infection Risk 计Against the Background of COVID-19 Epidemic

The Corona Virus Disease 2019（COVID-19）epidemic is a serious threat to people’s lives.Supervision of the density of clustered people and wearing of masks is key to controlling the virus.Public places are characterized by a dense flow of people and high mobility.Manual monitoring can easily increase the risk of infection，and existing mask detection algorithms based on deep learning suffer from the limitation of having a single function and can be applied to only a single type of scenes；as such，they cannot achieve multi-category detection across multiple scenes. Furthermore，their accuracy needs to be improved. The Cascade-Attention R-CNN target detection algorithm is proposed for realizing the automatic detection of aggregations in areas，pedestrians，and face masks. Aiming to solve the problem that the target scale changes too significantly during the task，a high-precision two-stage Cascade R-CNN target detection algorithm is selected as the basic detection framework. By designing multiple cascaded candidate classification regression networks and adding a spatial attention mechanism，we highlight the important features of the candidate region features and suppress noise features to improve the detection accuracy. Based on this，an intelligent monitoring model for aggregated infection risk is constructed，and the infection risk level is determined by combining the outputs of the proposed algorithm. The experimental results show that the model has high accuracy and robustness for multi-category target images with different scenes and perspectives. The average accuracy of the Cascade Attention R-CNN algorithm reaches 89.4%， which is 2.6 percentage points higher than that of the original Cascade R-CNN algorithm，and 10.1 and 8.4 percentage points higher than those of the classic two-stage target detection algorithm，Faster R-CNN and the single-stage target detection framework，RetinaNet，respectively. © 2022, Editorial Office of Computer Engineering. All rights reserved.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment (PPE) donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19. However, the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers??? PPE use in the field of healthcare. In this paper, we construct a dress codes dataset for medical staff under the epidemic. And based on this, we propose a PPE donning automatic detection approach using deep learning. With the participation of health care personnel, we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment, and an effective and robust dataset is constructed with a total of 5233 preprocessed images. Starting from the task???s dual requirements for speed and accuracy, we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety. Experimental results show that compared with three typical deep -learning-based detection models, our method achieves a relatively optimal balance while ensuring high detection accuracy (84.14%), with faster processing time (42.02 ms) after the average analysis of 17 classes of PPE donning situation. Overall, this research focuses on the automatic detection of worker safety protection for the first time in healthcare, which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.

Formation of the Structure of Multilayer Polyagent Functionals

The processes of intellectual monitoring in emergencies are studied. The intelligent monitoring system is an environment for creating and using intelligent agents to provide knowledge of decision-making processes. In emergencies, objects acquire new properties quickly, and the informativeness of the results of previous observations decreases. To increase the power of data mining tools, monitoring agents are combined into agent functionalities with a multi-tier structure. The paper presents the results of research on the processes of formation of multi-echelon polyagent functionals. The efficiency of construction of a multi-echelon polyagent functional in solving the problem of predicting the incidence of the population of Ukraine on Covid-19 in conditions of low informativeness of the results of observations has been experimentally confirmed. © 2021 Copyright for this paper by its authors

Formation of feedback in the structures of poly-agent functionals

The research aim is to improve the efficiency of poly-agent functional monitoring information system by feedback creation. The signs list at the entrance functionality varies according to the characteristics of the signal at the output. The signal characteristics at the functional output are improved by changing the features list of the results of observations at its input. Building poly-agent functionalities by the monitoring information system (MIS) is improved. The MIS is a software implementation of the information technology of intelligent monitoring (ITLM). This paper describes the use of ITLM for forecasting the incidence of COVID-19 disease in the Ukrainian population. The information technology is designed to work under conditions of crisis monitoring. During the pandemic, the properties of the monitoring objects change, and the informativeness of the accumulated results of monitoring decreases. It is proposed to adapt the list of features of the array of input data (AID) to change the informativeness of the observation results. A method for informativeness identifying the AID features of a poly-agent functional based on the results of constructing agents with structural tasks is proposed. AID increasing informativeness by signs list optimizing according to signals' characteristics at the agents' output with the structural tasks of MIS is experimentally confirmed. © 2021 Elsevier B.V.. All rights reserved.