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Related Concept Videos

Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

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There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
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Gas Chromatography: Types of Detectors-II01:19

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In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
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Related Experiment Video

Updated: Oct 3, 2025

Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice
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CNN-Based Smoker Classification and Detection in Smart City Application.

Ali Khan1, Somaiya Khan2, Bilal Hassan3

  • 1College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

This study introduces an AI-based system for detecting smokers in no-smoking zones within smart cities. The novel framework achieves high accuracy, aiding public health regulations.

Keywords:
AI-based surveillancesmoker classificationsmoker detection datasettransfer learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Public Health

Background:

  • Smoking in restricted areas poses public health challenges.
  • Existing surveillance methods lack efficiency for real-time enforcement.
  • Smart city initiatives require advanced technological solutions for urban management.

Purpose of the Study:

  • To develop an AI-based system for detecting smokers in no-smoking areas.
  • To create a diverse dataset for smoker detection in various environments.
  • To evaluate the system's performance against established methods.

Main Methods:

  • A novel AI-based framework for smoker detection was proposed.
  • A new image dataset with 'Smoking' and 'NotSmoking' classes was curated.
  • Transfer learning using the InceptionResNetV2 model was employed for classification.

Main Results:

  • The AI system achieved 96.87% accuracy, 97.32% precision, and 96.46% recall.
  • Performance was validated on a challenging, newly created dataset.
  • The system demonstrated robust prediction capabilities for smoking behavior.

Conclusions:

  • The AI-based smoker detection system is effective for smart city surveillance.
  • The developed dataset will facilitate future research in this domain.
  • The system shows promise for real-time application in enforcing no-smoking policies.