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This study introduces a smart community big data model using logistic regression to improve prediction accuracy and efficiency. The model addresses issues like high costs and system independence, creating a new intelligent community management approach.

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Area of Science:

  • Computer Science
  • Data Science
  • Urban Planning

Background:

  • Smart community systems face challenges including high costs, limited intelligence, system fragmentation, and management difficulties.
  • Existing smart community infrastructure often lacks unified management and efficient data integration.

Purpose of the Study:

  • To propose a smart community big data dynamic analysis model to address current system limitations.
  • To enhance the intelligence, efficiency, and management of smart communities through data-driven insights.

Main Methods:

  • Developed a big data research architecture for smart communities using Internet of Things (IoT) technology, encompassing Infrastructure as a Service (IaaS), Data as a Service (DaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) layers.
  • Designed a high-dimensional random matrix model for big data measurement and abnormal data detection.
  • Utilized a logistic regression model for predicting smart community development trends.

Main Results:

  • The proposed logistic regression model significantly improves prediction efficiency and accuracy for smart community development.
  • The model facilitates the detection of high-dimensional abnormal data within the smart community's big data.
  • Simulation results demonstrate the model's effectiveness in avoiding resource duplication and waste.

Conclusions:

  • The developed model offers a robust framework for smart community big data analysis and prediction.
  • It enables a new, intelligent, and information-based social management and service model for communities.
  • The approach enhances resource optimization and overall community operational efficiency.