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Related Concept Videos

Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Automatic approach for mask detection: effective for COVID-19.

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  • 1Odisha, India School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University.

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Summary

This study introduces a deep learning system to automatically detect improper face mask usage, crucial for public health compliance and safety. The technology aids in monitoring mask-wearing, promoting adherence, and ensuring secure environments during the COVID-19 pandemic.

Keywords:
Boundary-layer meteorologyCNN (Convolutional neural network)COVID-19Grad CAMMobileNetV2

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Public Health

Background:

  • The COVID-19 pandemic highlighted the importance of public health measures like face mask usage.
  • Manual monitoring of face mask compliance is impractical for large-scale environments.
  • Technological solutions are needed to enforce and track face mask adoption.

Purpose of the Study:

  • To develop and present a deep learning-based system for detecting improper face mask usage.
  • To aid in monitoring safety protocol adherence in public and private spaces.
  • To promote consistent face mask use for mitigating viral transmission.

Main Methods:

  • Utilized a dual-stage convolutional neural network (CNN) architecture.
  • Developed a system capable of recognizing both masked and unmasked faces.
  • Proposed a variant of a multi-face detection model for group analysis.

Main Results:

  • The system effectively detects instances of improper face mask use.
  • The dual-stage CNN accurately classifies masked and unmasked individuals.
  • The multi-face detection model can identify mask status within groups.

Conclusions:

  • Deep learning offers a viable technological solution for monitoring face mask compliance.
  • The developed system supports the maintenance of safe working and public environments.
  • Automated detection systems can enhance the enforcement of public health guidelines.