IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

Hu, Fang; Liu, Jia; Li, Liuhuan; Huang, Mingfang; Yang, Changguo

doi:10.1109/jsen.2021.3064663

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Hu et al [24] focused on the significant issue of epidemic surveillance. They proposed an original technique that utilizes the Internet of Things (IoT) and an improved version of the gated recurrent unit (GRU) model.…”

Section: Related Workmentioning

confidence: 99%

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

Amoon,

Altameem,

Hashem

2023

Sensors

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The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.

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Section: Related Workmentioning

confidence: 99%

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

Amoon,

Altameem,

Hashem

2023

Sensors

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“…It was initially discovered in Wuhan, Hubei Province, China [1]. An unprecedented spread of this virus has created many challenges that make the roots of human civilization tremble [2]. Some of the most apparent challenges are described below.…”

Section: Introductionmentioning

confidence: 99%

Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

Abunadi

Kumar

2021

Security and Communication Networks

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From this global health disaster, the health profession is searching for a new technology to monitor and mitigate COVID-19 infections. Accurate and reliable data are needed to surveil and prevent the diffusion of the coronavirus. However, there is a lack of consistent data on existing technologies. Various entities—for example, medical labs and public hospitals—could provide data on patients with coronavirus infection. Yet, this information might not be precise since it is not supervised and recorded correctly. This paper proposes a Blockchain and Business Process Management (BBPM) system in healthcare to solve these problems. This system could be crucial in tracing coronavirus diffusion and discovering more dangerous patients and is extremely proficient at controlling the disease. BBPM can be utilized like a digital database that includes information concurrently utilized and distributed inside a wide, decentralized, and openly accessible network. Usage of blockchains in the medical sector is anticipated to revolutionize the industry in many regions, primarily because centralization in current health technologies inhibits information sharing and causes a lack of confidentiality. Furthermore, BBPM could help supervise the diffusion of coronavirus infection worldwide by utilizing blockchain networks on the mobile devices of individuals. Protecting patient information is one of the critical strengths of BBPM. Participating BBPM nodes can be governments, hospitals, testing labs, or patients. In addition, the digital ledger has a few documents, including patient reports, consequences, the condition of treatment, and a summary of discharge. The BBPM system is categorized into four processes. In the first process, the patient is analyzed and diagnosed by a testing lab to detect any early signs of COVID-19 infection. A sample of the patients was taken. If it is positive, the second process begins. In the second process, the patient is isolated and begins treatment for a minimum of 14 days. If the patient’s health condition is improving, the third process begins. In the third process, the patients were retested for COVID-19. If the patient sample is negative, the patient is discharged, and an outline is created. During discharge, the patient pays the hospital for treatment. However, if the patient sample is positive, the isolation period is continued for another 14 days. In the last process, the details of total COVID-19 are confirmed, recovered, and death cases are conveyed to the government by the testing labs and hospitals. Patient records are stored for upcoming usage; their confidentiality is maintained when distributed on a larger scale. BBPM can guarantee the security and accuracy of patients’ recorded data.

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Implementation of Optimal Leaf Feature Selection-Based Plant Leaf Disease Classification Framework with RNN+GRU Technique

Sahu

Minz

2023

Communications in Computer and Information Science

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IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

Cited by 5 publications

References 36 publications

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

Implementation of Optimal Leaf Feature Selection-Based Plant Leaf Disease Classification Framework with RNN+GRU Technique

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