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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Fuzzy-based adaptive learning network using search and rescue optimization for e-waste management model: case study.

Khalid Mujasam Batoo1, Saravanan Pandiaraj2, Muthumareeswaran Muthuramamoorthy3

  • 1King Abdullah Institute for Nanotechnology, College of Science, King Saud University, Riyadh, Saudi Arabia. kbatoo@ksu.edu.sa.

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Summary
This summary is machine-generated.

This study introduces an efficient electronic waste (e-waste) management system for India, utilizing a fuzzy c-means based adaptive optimal neural network. The proposed system aims to improve e-waste handling and increase recycling rates to 40%.

Keywords:
BarriersE-waste managementElectronic wasteFuzzy C-meansNeural networkSearch and rescue optimization algorithm

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Growing e-waste poses significant environmental risks, including groundwater contamination and harm to marine life and human health.
  • Developing nations struggle with implementing effective sustainable e-waste management strategies.
  • Informal e-waste practices in Chandigarh, India, create substantial environmental and health challenges.

Purpose of the Study:

  • To propose an efficient electronic waste (e-waste) management system for India.
  • To address the challenges of formalizing e-waste management by analyzing key obstructions.
  • To provide a framework for policymakers to develop organized and effective e-waste strategies.

Main Methods:

  • Classification of household e-waste using a fuzzy c-means clustering approach.
  • Analysis of relative weights and grading of obstructions using an adaptive optimal neural network.
  • Sensitivity analysis to ensure structural robustness and mitigate bias effects.

Main Results:

  • Financial and economic limitations identified as primary obstructions to formalizing e-waste management.
  • The proposed system demonstrates robustness and addresses potential biases.
  • The study provides a foundation for enhancing e-waste management quality in India.

Conclusions:

  • The developed e-waste management system offers a sustainable solution for India.
  • Implementation of this system is projected to increase the national recycling rate to 40%.
  • This research supports policymakers in creating structured and efficient e-waste management policies.