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Related Concept Videos

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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An intelligent spatial stream processing framework for digital forensics amid the COVID-19 outbreak.

Sujit Bebortta1, Aditya Ranjan Dalabehera1, Bibudhendu Pati2

  • 1Department of Computer Science, Ravenshaw University, Cuttack, 753003, Odisha, India.

Smart Health (Amsterdam, Netherlands)
|August 17, 2022
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Summary
This summary is machine-generated.

This study introduces a smart, real-time image processing framework using thermal screening and AI to track COVID-19 spread and enforce social distancing. The event-triggered video framing model efficiently processes data, outperforming real-time methods.

Keywords:
AutocorrelationCOVID-19Event triggered video framing (ETVF)Poisson processQueuing theoryReal-time video framing (RTVF)

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Area of Science:

  • Computer Vision and Artificial Intelligence
  • Epidemiology and Public Health Technology

Background:

  • The COVID-19 pandemic necessitated innovative strategies for public health monitoring and control.
  • Technological advancements, including drone-based systems, have been explored for mobility management and early detection of symptomatic individuals.

Purpose of the Study:

  • To implement a smart, real-time image processing framework integrated with non-contact thermal screening for COVID-19 mitigation.
  • To develop modules for smart temperature screening, tracking infection footprints, and enforcing social distancing policies.
  • To evaluate novel video frame management models for efficient data processing in surveillance systems.

Main Methods:

  • Developed a framework with three modules: smart temperature screening, infection footprint tracking, and social distancing monitoring.
  • Employed Histogram of Oriented Gradients (HOG) for identifying infection hotspots.
  • Utilized Haar Cascade and Local Binary Pattern Histogram (LBPH) for facial recognition and social distancing enforcement.
  • Introduced and compared Event-Triggered Video Framing (ETVF) and Real-Time Video Framing (RTVF) models for video frame management.
  • Conducted autocorrelation and choropleth analysis on COVID-19 data for India to understand epidemiological spread.

Main Results:

  • The proposed Event-Triggered Video Framing (ETVF) model demonstrated superior performance with optimal processing costs compared to Real-Time Video Framing (RTVF) due to redundant data elimination.
  • The image processing framework successfully identified infection hotspots and monitored social distancing.
  • Autocorrelation and choropleth analyses provided insights into the epidemiological spread of COVID-19 in India.

Conclusions:

  • The integrated smart image processing framework offers an effective technological solution for real-time monitoring and control of infectious disease spread.
  • The ETVF model presents a significant advancement in efficient video data management for public health surveillance systems.
  • The study highlights the utility of advanced image processing and data analysis techniques in understanding and combating pandemics.