Abstract The increasing popularity of various intelligent sensor and mobile communication technologies has enabled quick health physique sensing, monitoring, collection and analyses of students, which significantly promoted the development of sport education. Through collecting the students’ physiological signals and transmitted them to edge servers, we can precisely analyze and judge whether a student is in poor health (e.g., an outlier). However, with time elapsing, the accumulated physiological signals of students become massive, which places a heavy burden on the quick storage and in-time processing of physiological data of students. In this situation, it is becoming a necessity to develop a time-aware outlier detection technique for health physique evaluation of students in a time-efficient way. Considering this challenge, we propose a novel time-aware outlier detection method named TOD based on Locality-Sensitive Hashing. TOD condenses extensive physiological student data into a concise set of health indices. Leveraging these indices, we can efficiently identify potential student outliers from a large pool of candidates with precision and speed. Finally, we have designed a group of simulated experiments based on WS-DREAM dataset. Experiment results prove the feasibility and superiority of the TOD method compared with other existing methods. 

pdf/time-aware-outlier-detection-in-health-physique-monitoring-b2uf3xlx6s.pdf

Time-aware outlier detection in health physique monitoring in edge-aided sport education decision-makings

Upgrading and patching is a method to strengthen the host and virtualisation devices from malware. Inthe deployment to different entityâ€™s clients, different scenarios are faced to support their business process.A host and virtual machine (VM) are dependent on each other to provide high efficiency. A good design ofvirtual resources in a physical host can maintain the hostâ€™s efficiency. If the host is to be upgraded orpatched, then the relationship with VM needs to be explored to avoid missing or malfunctioning of certainapplication on the host or VM. A relationship how to check whether the application scenario is workingas expected is not being derived. This paper will discuss scenarios that we face and how it can beconverted into taxonomy to provide a strategic approach when upgrading a specific item. With that, ananalogy can be based on how the application scenario can be established as a model and converted intotaxonomy for troubleshooting when execution is facing an error.
Keywords: Design scenario, strategic way, taxonomy, keyword, application.

http://jurnalintelek.uitm.edu.my/index.php/main/article/download/224/222

A Deployment Scenario : A Taxonomy Mapping and Keyword Searching for the Application Relation

Heart is the human body's most vital organ. As a result, heart disease is considered as a serious condition, which may carry a significant long-term risk of death. However, the existing approaches cannot efficiently identify inherent correlations and patterns from e-health data. To save lives, a detailed data analysis process is required. Incorporating such analysis is difficult but it is highly essential to implement disease detection. This study suggested using machine learning to perform health analytics, which would allow for more accurate patient data analysis. Here, data mining techniques can be used to develop an intelligent model based on patient risk factor datasets. This study analyses how deep learning and machine learning approaches can be used to diagnose heart diseases. Recently, various data mining classifiers are developed to accurately and effectively identify heart diseases. MLP, a complex classification algorithm, employs artificial neural networks with deep learning capabilities. To generate accurate findings, the proposed method makes use of deep learning and data mining techniques.

Cardiovascular Disease Prediction Using Machine Learning and Deep Learning Algorithms

Health monitoring is one of the industries that is now on the rise and making quick strides in its development. According to the technological developments in healthcare systems, several analysts have suggested various arrangements for monitoring the health of paralyzed persons. The Internet is widely used nowadays, and one recent area that has seen growth is the Internet of Things (IoT), which many people have chosen to incorporate into a variety of applications. IoT is commonly utilized to link many easily accessible medical and related device resources. For individuals who are paralyzed, it offers amazing, efficient, and productive medical treatments. The secret to assist and dynamic living—which actively helps and modifies the paralyzed person by keeping an eye on his health—is to continuously improve the quality of life for those who are paralyzed. We provide a quick summary of the most current findings and advancements in the field of health monitoring systems for paralyzed people.

Survey on Health Care Monitoring of Paralyzed People

VR for Construction Engineering and Management: Review of User Experience Perspective

Patients now want a contemporary, advanced healthcare system that is faster and more individualized and that can keep up with their changing needs. An edge computing environment, in conjunction with 5G speeds and contemporary computing techniques, is the solution for the latency and energy efficiency criteria to be satisfied for a real-time collection and analysis of health data. The feature of optimum computing approaches, including encryption, authentication, and classification that are employed on the devices deployed in an edge-computing architecture, has been ignored by previous healthcare systems, which have concentrated on novel fog architecture and sensor kinds. To avoid this problem in this paper, an Optimized Deep Recurrent Neural Network (O-DRNN) model is used with a multitier secured architecture. Initially, the data obtained from the patient are sent to the healthcare server in edge computing and the processed data are stored in the cloud using the Elliptic Curve Key Agreement Scheme (ECKAS) security model. The data is pre-processed and optimal features are selected using the Particle Swarm Optimization (PSO) algorithm. O-DRNN algorithm hyper-parameters are optimized using Bayesian optimization for better diagnosis. The proposed work offers superior outcomes in terms of accuracy and encryption latency while using computational cloud services.

https://doi.org/10.1080/00051144.2023.2195218

A secured and optimized deep recurrent neural network (DRNN) scheme for remote health monitoring system with edge computing

Cloud computing is becoming very popular computing standard for network application. It is the result of existing technologies and paradigms. It is a set of resources and services offered to customers through network or internet. It provides resources that are provided as on request services to end user. It mainly aims at reduced cost, less complexity, flexibility, scalability and efficiency. Cloud computing extends various techniques such as grid computing, distributed computing and utility computing . Cloud computing is used in both industrial and academic field. The data storage as a service (DAAS) in cloud provides the user a facility to store their data in remote servers which may be accessible by the user anywhere he is through the internet facility . The data stored in cloud can also be accessed by other users. There is also a possibility where a hacker can pierce the cloud by stealing a genuine user’s data. He is also capable of infecting the cloud which in turn affects the entire user’s who are sharing the infected cloud. Here comes the problem of providing security to the cloud. Likewise both customer and service provider face few security threats . Data communication through any network is at risk now, there are many encryption techniques used to protect the data. This paper discusses about security issues faced by client and server and also have analyzed about threats involved in different service models such as infrastructure as a service (IAAS), software as a service (SAAS) and platform as a service (PAAS). We have also tried to provide few countermeasures for those security issues

/pdf/analysis-on-cloud-computing-security-issues-threats-and-rx4958ky4f.pdf

Analysis on Cloud Computing Security Issues, Threats and Solutions

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A secured and optimized deep recurrent neural network (DRNN) scheme for remote health monitoring system with edge computing

Analysis on Cloud Computing Security Issues, Threats and Solutions