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Design and Analysis for Fall Detection System Simplification
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Machine Learning and IoT-Based Waste Management Model.

Rijwan Khan1, Santosh Kumar2, Akhilesh Kumar Srivastava3

  • 1Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, U.P., India.

Computational Intelligence and Neuroscience
|September 6, 2021
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Summary

This study proposes a smart waste management system using machine learning (ML) and the Internet of Things (IoT) to address global waste challenges. The system utilizes sensors and image processing for efficient, clean, and pollution-free cities.

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Rapid global population growth exacerbates waste management issues, leading to health problems and pollution.
  • Current waste collection schedules (1-2 times/week) are insufficient, causing waste spillage on streets.

Purpose of the Study:

  • To propose an efficient smart waste management solution using machine learning (ML) and the Internet of Things (IoT).
  • To develop a system that monitors waste levels and optimizes collection routes for cleaner urban environments.

Main Methods:

  • Utilized an Arduino UNO microcontroller, ultrasonic sensors, and moisture sensors for data collection.
  • Implemented image processing techniques to determine a waste index for dumping grounds.
  • Developed a hardware prototype to demonstrate the proposed framework's functionality.

Main Results:

  • The integrated system effectively monitors waste accumulation in real-time.
  • Image processing provides a quantifiable waste index for targeted management.
  • The hardware prototype validates the feasibility of the smart waste management approach.

Conclusions:

  • The proposed ML and IoT-based solution offers an efficient strategy for smart waste management.
  • Successful implementation can lead to reduced environmental pollution and healthier urban living.
  • The system aims to establish cleaner and pollution-free cities through optimized waste collection.