Fuzzy-based adaptive learning network using search and rescue optimization for e-waste management model: case study

Batoo, Khalid Mujasam; Pandiaraj, Saravanan; Muthuramamoorthy, Muthumareeswaran; Raslan, Emad; Krishnamoorthy, Sujatha

Fuzzy-based adaptive learning network using search and rescue optimization for e-waste management model: case study

2022

Batoo, Khalid Mujasam | Pandiaraj, Saravanan | Muthuramamoorthy, Muthumareeswaran | Raslan, Emad | Krishnamoorthy, Sujatha

In recent days, the expansion of e-waste disposal should be increased due to environmental hazards, contamination of groundwater, an unconcerned consequence on marine life, human health, and decrease in the fertility of the soil. The majority of the developing countries are facing massive issues in implementing sustainable e-waste management schemes. The unofficial e-waste management schemes in the region of Chandigarh, India, have become a serious dispute for the government and several stakeholders due to human health and environmental effects. To overcome such shortcomings, this paper proposes an efficient e-waste management system using fuzzy c-means based adaptive optimal neural network. Here fuzzy c-means clustering approach is employed to classify the household e-wastes and adaptive optimal neural network is employed to analyze the relative weights as well as the grading of the obstructions. Here, the financial and economic limitations are regarded as the most important obstructions of e-waste formalization. The sensitivity analysis is carried out to verify the structure robustness and address the bias effect. This study assists the lawmakers to create organized strategies for an efficient e-waste management system. The sustainable set of e-waste management system advances the e-waste management in India quality thereby raising the recycling rate to 40%.

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