Enhancing Smart City Waste Management through LBBOA based RIAN Classification
DOI:
https://doi.org/10.54392/irjmt2365Keywords:
Smart city management, Rotation-invariant attention network, Center spectral attention, Rectified spatial attention, Ladybug beetle optimization algorithmAbstract
Effective trash management has become a top environmental priority, especially in urban areas with significant population growth where garbage output is on the rise. As cities work to manage garbage properly, innovative waste management programmes have the potential to increase effectiveness, cut costs, and improve the aesthetic appeal of public places. This article introduces SCM-RIAN, a powerful "Smart City Management and Classification System" built on the Internet of Things (IoT) and deep learning (DL) technologies. Convolutional neural networks are used in the garbage classification model that is implemented within this smart city management and classification framework. This system for classifying waste is intended to categorise rubbish into several classes at waste collection sites, encouraging recycling. The Rotation-Invariant Attention Network (RIAN) is a unique approach presented for the categorization process to address a prevalent problem in smart city management (SCM). A Centre Spectral Attention (CSpeA) module built within RIAN isolates spectral bands from other categories of pixels' influence, reducing redundancy. As an alternative to the conventional 3 3 convolution, to obtain rotation-invariant spectral-spatial data contained in SCM patches, the Rectified Spatial Attention (RSpaA) module is also introduced. The suggested RIAN for SCM classification is built on the integration of the CSpeA, 11 convolution, and RSpaA modules. The Ladybird Beetle Optimisation Algorithm (LBBOA) is used to optimise hyperparameters. With improved results compared to other current models, this suggested SCM-RIAN achieved 98.12% accuracy (ACC) with high sensitivity (SEN), specificity (SPEC), and kappa index (KI) using the garbage classification dataset.
References
M. Karthik, L. Sreevidya, R.N. Devi, M. Thangaraj, G. Hemalatha, R. Yamini, An efficient waste management technique with IoT based smart waste system, Materials Today: Proceedings, 80 (2023) 3140-3143. https://doi.org/10.1016/j.matpr.2021.07.179
J. Liu, A.S. Prabuwono, A.W. Abulfaraj, S. Miniaoui, N. Taheri, Cognitive cloud framework for waste dumping analysis using deep learning vision computing in healthy environment, Computers and Electrical Engineering, 110 (2023) 108814. https://doi.org/10.1016/j.compeleceng.2023.108814
M. I. B. Ahmed, R.B. Alotaibi, R.A. Al-Qahtani, R.S. Al-Qahtani, S.S. Al-Hetela, K. A. Al-Matar, N.K. Al-Saqer, A. Rahman, L. Saraireh, M. Youldash, G. Krishnasamy, Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management. Sustainability, 15 (2023) 11138. https://doi.org/10.3390/su151411138
N.C.A. Sallang, M.T. Islam, M.S. Islam, H. Arshad, A CNN-Based SWM System Using TensorFlow Lite and LoRa-GPS Shield in Internet of Things Environment, IEEE Access, 9 (2021) 153560-153574. https://doi.org/10.1109/ACCESS.2021.3128314
M.K. Hasan, M.A. Khan, G.F. Issa, A. Atta, A.S. Akram, & M. Hassan, (2022) SWM and classification system for smart cities using deep learning, In 2022 International Conference on Business Analytics for Technology and Security (ICBATS) IEEE, 1-7. https://doi.org/10.1109/ICBATS54253.2022.9759087
R. Sidharth, P. Rohit, S. Vishagan, R. Karthika, M. Ganesan, (2020). Deep learning based smart garbage classifier for effective waste management. In 2020 5th International Conference on Communication and Electronics Systems (ICCES), IEEE. 1086-1089. https://doi.org/10.1109/ICCES48766.2020.9137938
M.W. Rahman, R. Islam, A. Hasan, N.I. Bithi, M.M. Hasan, M.M. Rahman, Intelligent waste management system using deep learning with IoT, Journal of King Saud University-Computer and Information Sciences, 34 (2022) 2072-2087. https://doi.org/10.1016/j.jksuci.2020.08.016
A.U. Gondal, M.I. Sadiq, T. Ali, M. Irfan, A. Shaf, M. Aamir, M. Shoaib, A. Glowacz, R. Tadeusiewicz, E. Kantoch, Real time multipurpose smart waste classification model for efficient recycling in smart cities using multilayer convolutional neural network and perceptron. Sensors, 21 (2021) 4916. https://doi.org/10.3390/s21144916
A.U. Gondal, M.I. Sadiq, T. Ali, M. Irfan, A. Shaf, M. Aamir, M. Shoaib, A. Glowacz, R. Tadeusiewicz, E. Kantoch, Real time multipurpose smart waste classification model for efficient recycling in smart cities using multilayer convolutional neural network and perceptron. Sensors, 21 (2021) 4916. https://doi.org/10.3390/s21144916
Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su, X. Liu, Waste image classification based on transfer learning and convolutional neural network. Waste Management, 135 (2021) 150-157. https://doi.org/10.1016/j.wasman.2021.08.038
K. Lin, Y. Zhao, J.H. Kuo, H. Deng, F. Cui, Z. Zhang, M. Zhang, C. Zhao, X. Gao, T. Zhou, T. Wang, Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches. Journal of Cleaner Production, 346 (2022) 130943. https://doi.org/10.1016/j.jclepro.2022.130943
J. Li, J. Chen, B. Sheng, P. Li, P. Yang, D. D. Feng, J. Qi, Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Transactions on Industrial Informatics, 18 (2021) 163-173. https://doi.org/10.1109/TII.2021.3085669
W. Xia, Y. Jiang, X. Chen, R. Zhao, Application of machine learning algorithms in municipal solid waste management: A mini review, Waste Management & Research: The Journal for a Sustainable Circular Economy, 40 (2022) 609-624. https://doi.org/10.1177/0734242X211033716
N.H. Mohamed, S. Khan, S. Jagtap, Modernizing Medical Waste Management: Unleashing the Power of the Internet of Things (IoT). Sustainability, 15 (2023) 9909. https://doi.org/10.3390/su15139909
M. Farjana, A.B. Fahad, S.E. Alam, M.M. Islam, An iot-and cloud-based e-waste management system for resource reclamation with a data-driven decision-making process, IoT, 4 (2023) 202-220. https://doi.org/10.3390/iot4030011
A.I. Alzahrani, S.H. Chauhdary, A.A. Alshdadi, Internet of Things (IoT)-Based Wastewater Management in Smart Citiesm, Electronics, 12 (2023) 2590. https://doi.org/10.3390/electronics12122590
P. Ramya, V. Ramya, M. Babu Rao, (2023) Optimized Deep Learning-Based E-Waste Management in IoT Application via Energy-Aware Routing. Cybernetics and Systems, 1-30. https://doi.org/10.1080/01969722.2023.2175119
M. Al Duhayyim, Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT, Sustainability, 15 (2023) 7321. https://doi.org/10.3390/su15097321
S. Thaseen Ikram, V. Mohanraj, S. Ramachandran, A. Balakrishnan, An Intelligent Waste Management Application Using IoT and a Genetic Algorithm–Fuzzy Inference System. Applied Sciences, 13 (2023) 3943. https://doi.org/10.3390/app13063943
Kaggle, (2022) Garbage Classification Data, Kaggle.
H. Sun, X. Zheng, X. Lu, S. Wu, Spectral–spatial attention network for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 58 (2020) 3232–3245. https://doi.org/10.1109/TGRS.2019.2951160
N. Audebert, B.L. Saux, S. Lefevre, Deep learning for classification of hyperspectral data: A comparative review, IEEE Geoscience and Remote Sensing Magazine, 7 (2019) 159–173. https://doi.org/10.1109/MGRS.2019.2912563
H. Sun, X. Zheng, and X. Lu, A supervised segmentation network for hyperspectral image classification, IEEE Transactions on Image Processing, 30 (2021) 2810–2825. https://doi.org/10.1109/TIP.2021.3055613
M. Imani, H. Ghassemian, An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges, Information Fusion, 59 (2020) 59–83. https://doi.org/10.1016/j.inffus.2020.01.007
L. Mou, X.X. Zhu, learning to pay attention on spectral domain: A spectral attention module-based convolutional network for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 58 (2020) 110-122. https://doi.org/10.1109/TGRS.2019.2933609
S. Safiri, A. Nikoofard, Ladybug Beetle Optimization algorithm: application for real-world problems, The Journal of Supercomputing, 79 (2023) 511-3560. https://doi.org/10.1007/s11227-022-04755-2
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Copyright (c) 2023 Sankar K, Gokula Krishnan V, Venkata Lakshmi S, Kaviarasan S, Arockia Abins A (Author)
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