Smart Waste Revolution: AI-Powered Biomedical Segregation and Recycling with IoT Integration

International Journal of Mechanical Engineering

© 2024 by SSRG - IJME Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : G. Uthayakumar, Bathrinath Sankaranarayanan, Bhalaji R.K.A

10.14445/23488360/IJME-V11I12P104

How to Cite?

G. Uthayakumar, Bathrinath Sankaranarayanan, Bhalaji R.K.A, "Smart Waste Revolution: AI-Powered Biomedical Segregation and Recycling with IoT Integration," SSRG International Journal of Mechanical Engineering, vol. 11,  no. 12, pp. 38-46, 2024. Crossref, https://doi.org/10.14445/23488360/IJME-V11I12P104

Abstract:

The threat to the environment and health posed by biomedical waste requires a proper approach to its management and recycling. This study aims to develop an advanced smart waste management system that combines IoT sensors for real-time monitoring and an Extreme Learning Machine (ELM) to segregate waste. This system applies IoT sensors to get data from hospital ward dustbins that alert bins' fullness. The ELM uses the information received to categorize the waste as recyclable or non-recyclable. The sorted-out trash items are then directed either to disposal or recycling bins accordingly. Our method improves efficiency in handling biomedical wastes by utilizing state-of-the-art ELM algorithms, which exhibit superior performance over conventional methods in accuracy and computation speed, among others. Our simulation results show that our model has 95% classification accuracy compared with other AI-based approaches such as Cohort Intelligence Algorithm (CIA), Hesitant Fuzzy Weight and Rank Finding (HFWRF), and Deep Learning (DL). This new system not only contributes to efficient waste management but also supports environmental sustainability, promoting effective recycling plans.

Keywords:

Use Segregation, Recycling, Extreme Learning Machine, Deep Learning, Waste management, IoT sensors.

References:

[1] Johnson Retnaraj Samuel Selvan Christyraj et al., Impact of Biomedical Waste Management System on Infection Control in the Midst of COVID-19 Pandemic, The Impact of the COVID-19 Pandemic on Green Societies: Environmental Sustainability, pp. 235-262, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Emeka Chukwu, and Lalit Garg, “A Systematic Review of Blockchain in Healthcare: Frameworks, Prototypes, and Implementations,” IEEE Access, vol. 8, pp. 21196-21214, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Dong-Won Kim, Jin-Young Choi, and Keun-Hee Han, “Medical Device Safety Management Using Cybersecurity Risk Analysis,” IEEE Access, vol. 8, pp. 115370-115382, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nistha Shrestha et al., “The Impact of COVID-19 on Globalization,” One Health, vol. 11, pp. 1-9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Nallapaneni Manoj Kumar et al., “Artificial Intelligence-Based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-Driven Decisions for Smart Circular Economy Practice,” Process Safety and Environmental Protection, vol. 152, pp. 482-494, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Poorva Agrawal, Gagandeep Kaur, and Snehal Sagar Kolekar, “Investigation on Biomedical Waste Management of Hospitals Using Cohort Intelligence Algorithm,” Soft Computing Letters, vol. 3, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] S. Narayanamoorthy et al., “A Novel Assessment of Bio-Medical Waste Disposal Methods using Integrating Weighting Approach and Hesitant Fuzzy MOOSRA,” Journal of Cleaner Production, vol. 275, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jenmei Liu et al., “Cognitive Cloud Framework for Waste Dumping Analysis using Deep Learning Vision Computing in Healthy Environment,” Computers and Electrical Engineering, vol. 110, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Kannan Govindan et al., “Medical Waste Management during Coronavirus Disease 2019 (COVID-19) Outbreak: A Mathematical Programming Model,” Computers & Industrial Engineering, vol. 162, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jie Zhang et al., “Non-Iterative and Fast Deep Learning: Multilayer Extreme Learning Machines,” Journal of the Franklin Institute, vol. 357, no. 13, pp. 8925-8955, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Aliyu Ishaq et al., “Smart Waste Bin Monitoring using IoT for Sustainable Biomedical Waste Management,” Environmental Science and Pollution Research, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] G. Uganya et al., “A Novel Strategy for Waste Prediction Using Machine Learning Algorithm with IoT Based Intelligent Waste Management System,” Wireless Communications and Mobile Computing, vol. 2022, no. 1, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Senlin Zhu et al., “Extreme Learning Machine-Based Prediction of Daily Water Temperature for Rivers,” Environmental Earth Sciences, vol. 78, pp. 1-17, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Yi Zhou et al., “Prediction of Photovoltaic Power Output based on Similar Day Analysis, Genetic Algorithm and Extreme Learning Machine,” Energy, vol. 204, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Somayeh Golbaz, Ramin Nabizadeh, and Haniye Sadat Sajadi, “Comparative Study of Predicting Hospital Solid Waste Generation using Multiple Linear Regression and Artificial Intelligence,” Journal of Environmental Health Science and Engineering, vol. 17, pp. 41-51, 2019.
[CrossRef] [Google Scholar] [Publisher Link]