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Machine learning techniques based on security management in smart cities using robots.

Mengqi Zhang1, Xi Wang2, V E Sathishkumar3

  • 1School of Law, Shenyang Institute of Engineering, Shenyang, China.

Work (Reading, Mass.)
|February 22, 2021
PubMed
Summary
This summary is machine-generated.

Smart cities face security challenges. This study uses deep reinforcement learning (DRL) with a modular deep neural network (MDNN) to predict and manage unwanted activities, enhancing urban security.

Keywords:
Cobalt robotsdeep reinforcement learningmodular neural networksafe environment

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Urban Planning

Background:

  • Smart cities leverage information and communication technologies to improve services.
  • Security management remains a critical challenge due to shared threats in intelligent cities.
  • Continuous analysis of security factors is essential for eliminating unwanted activities and enhancing service quality.

Purpose of the Study:

  • To predict service quality and manage security issues in smart cities using active machine learning.
  • To apply deep reinforcement learning (DRL) for understanding smart city activities.
  • To examine new features in smart cities using a modular neural network (MDNN).

Main Methods:

  • Active machine learning techniques for service quality prediction.
  • Deep reinforcement learning (DRL) to learn smart city features and activities.
  • Modular neural network (MDNN) for examining incoming features and security.
  • Data segmentation into smaller subsets to reduce complexity.

Main Results:

  • The system successfully predicts unwanted activities in intelligent cities.
  • Data segmentation improved overall security management by reducing complexity.
  • Experimental analysis confirmed the system's efficiency in enhancing security.

Conclusions:

  • The developed deep reinforcement learning with modular deep neural network (DRL with MDNN) approach achieved maximum results in security maintenance.
  • The study involved an exploratory setup with 200 obstacles within the smart city environment.
  • The findings highlight the effectiveness of the proposed DRL with MDNN approach for smart city security.