Gas Sensing System using An Unmanned Aerial Vehicle

Piñeres-Espitia, Gabriel; Butt, Shariq Aziz; Canate-Masson, M.; Alvarez-Navarro, A.; Hassan, Syed Areeb; Gochhait, Saikat

doi:10.1109/i2ct51068.2021.9418000

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…The concentration of CO 2 gas in the environment was monitored by UAV. The commercially available CO 2 sensor MQ arduino UNO platform [128]. The Concentrations of various hazardous gases were measured and analysed using commercially available sensors [129].…”

Section: Recent Trends On Mems Based Gas Sensing Technologymentioning

confidence: 99%

Strategic report on MEMS based carbon monoxide sensor technology for environmental air quality monitoring using unmanned aerial vehicle

2022

IJATEE

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Section: Recent Trends On Mems Based Gas Sensing Technologymentioning

confidence: 99%

Strategic report on MEMS based carbon monoxide sensor technology for environmental air quality monitoring using unmanned aerial vehicle

2022

IJATEE

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“…By applying stochastic simulation techniques to analyze the variable bed occupancy carried out daily, they came to know that flexible bed occupancy results in a lesser overflow of patients in the hospital than the ones with settled occupancies. Varying discharge rates came out to be more suggestive than the changing admit rate [26,34]. Varying discharge rate plays a major role in reducing overflow in a hospital.…”

Section: Bed Occupancymentioning

confidence: 99%

A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector

Gochhait

Butt

De-la-Hoz-Franco

et al. 2021

Aut. Control Comp. Sci.

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The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and Tele-Medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services system; (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a Machine Learning solution for patient admission scheduling autonomously. The propose framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.

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