Srusti Management Review

A Journal of Management & IT

ISSN NO: 0974-4274(PRINT), ISSN NO: 2582-1148(ONLINE)Listed in Ulrich's Periodicals Directory, INDEXED IN J-GATE E-JOURNAL GATEWAY, EBSCOHOST, PROQUEST, U.S.A. & GOOGLE SCHOLAR A Peer Reviewed and Refereed Journal

Utilising Machine Learning Techniques For Waste Management

Year 2023
Volume/Issue/Review Month Volume - XVI, Issue - II, Jul. - Dec.
Title Utilising Machine Learning Techniques For Waste Management
Authors Jyotsnarani Tripathy , Manmathnath Das , Rajesh Kumar Ojha
Broad area IT
Abstract

Waste management is one of the biggest challenges facing the world today. The amount of solid garbage created by the growing urban population makes it hard to manage with current technologies. Artificial intelligence methods are used in this paper to identify waste. When waste is found, the system uses the camera as the only data source to determine its location. With greater than 95% certainty, the suggested system can discern between assets and waste in real time.The paper concludes by describing a system that can inspect and gather waste much like a human would. Different programs have been launched by the current Indian government to improve cleanliness and hygienic conditions. Megacities in India, for example, Ahmedabad, Hyderabad, Bangalore, Chennai, Kolkata, Delhi and more noteworthy Mumbai have dynamic monetary development and high wastage per capita. Scratch issues and difficulties such as absence of gathering and isolation at source, shortage of land, dumping of e-Waste, and so on. By using physical labour, the current waste accumulation framework compiles a variety of waste in an unsorted manner. The separation of this waste is a very repetitious, time-consuming, and wasteful task that frequently threatens the safety of the professionals.n order for the junk transfer to be carried out efficiently and productively, a framework that automates the waste isolation process is therefore required. The proposed approach accurately categories the loss into degradable and non-degradable using machine learning techniques like CNN

Description Utilising Machine Learning Techniques For Waste Management
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Referenceses

Narayana, T. (2009). Municipal solid waste management in India: From waste disposal to recovery of resources?. Waste management29(3), 1163-1166.

Pappu, A., Saxena, M., &Asolekar, S. R. (2007). Solid wastes generation in India and their recycling potential in building materials. Building and environment42(6), 2311-2320.

Ferri, G., Manzi, A., Salvini, P., Mazzolai, B., Laschi, C., & Dario, P. (2011, May). DustCart, an autonomous robot for door-to-door garbage collection: From DustBot project to the experimentation in the small town of Peccioli. In 2011 IEEE International Conference on Robotics and Automation (pp. 655-660). IEEE.

Sharholy, M., Ahmad, K., Mahmood, G., & Trivedi, R. C. (2008). Municipal solid waste management in Indian cities–A review. Waste management28(2), 459-467.

Gupta, S., Mohan, K., Prasad, R., Gupta, S., &Kansal, A. (1998). Solid waste management in India: options and opportunities. Resources, conservation and recycling24(2), 137-154.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM60(6), 84-90.

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ...& Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

Hussain, S. B., &Kanwal, F. (2016, August). Design of a 3 DoF robotic arm. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH) (pp. 145-149). IEEE.

Klug, S., Möhl, B., Von Stryk, O., & Barth, O. (2005). Design and application of a 3 DOF bionic robot arm. regulation11, 12.

Clothier, K. E., & Shang, Y. (2010). A geometric approach for robotic arm kinematics with hardware design, electrical design, and implementation. Journal of Robotics2010.

Kim, J. H., & Kumar, V. R. (1990). Kinematics of robot manipulators via line transformations. Journal of Robotic Systems7(4), 649-674.

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27 (pp. 270-279). Springer International Publishing.

India, A. (2017). Waste Management in India-Shifting Gears.

Devi, R. S., Vijaykumar, V. R., &Muthumeena, M. (2018). Waste segregation using deep learning algorithm. Int. J. Innov. Technol. Explor. Eng8, 401-403.

Dev, B., Agarwal, A., Hebbal, C., Aishwarya, H. S., & Gupta, K. A. (2018). Automatic waste segregation using image processing and machine learning. Int. J. Res. Appl. Sci. Eng. Technol6(5), 2617-2618..

Kölsch, A., Afzal, M. Z., Ebbecke, M., &Liwicki, M. (2017, November). Real-time document image classification using deep CNN and extreme learning machines. In 2017 14th IAPR international conference on document analysis and recognition (ICDAR) (Vol. 1, pp. 1318-1323). IEEE.Stefan van der Walt et al., “scikit-image: image processing in Python”, PeerJ 2:e453; DOI 10.7717/peerj.453.

van der Walt, S., Schönberger, J., Nunez-Iglesias, J., Boulogne, F., Warner, J., Yager, N., ... & Yu, T. t contributors (2014). scikit-image: Image processing in python.

Majchrowska, S., Miko?ajczyk, A., Ferlin, M., Klawikowska, Z., Plantykow, M. A., Kwasigroch, A., &Majek, K. (2022). Deep learning-based waste detection in natural and urban environments. Waste Management138, 274-284..

Yu, K. H., Zhang, Y., Li, D., Montenegro-Marin, C. E., & Kumar, P. M. (2021). Environmental planning based on reduce, reuse, recycle and recover using artificial intelligence. Environmental Impact Assessment Review86, 106492.Hussain, A., Draz, U., Ali, T., Tariq, S., Irfan, M., Glowacz, A., ...& Rahman, S. (2020). Waste management and prediction of air pollutants using IoT and machine learning approach. Energies13(15), 3930.

Ziouzios, D., Tsiktsiris, D., Baras, N., &Dasygenis, M. (2020). A distributed architecture for smart recycling using machine learning. Future Internet12(9), 141.

Rahman, M. W., Islam, R., Hasan, A., Bithi, N. I., Hasan, M. M., & Rahman, M. M. (2022). Intelligent waste management system using deep learning with IoT. Journal of King Saud University-Computer and Information Sciences34(5), 2072-2087.

Wang, C., Qin, J., Qu, C., Ran, X., Liu, C., & Chen, B. (2021). A smart municipal waste management system based on deep-learning and Internet of Things. Waste Management135, 20-29.

Nowakowski, P., &Pamu?a, T. (2020). Application of deep learning object classifier to improve e-waste collection planning. Waste Management109, 1-9.

Lin, K., Zhao, Y., Kuo, J. H., Deng, H., Cui, F., Zhang, Z.& Wang, T. (2022). Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches. Journal of Cleaner Production, 130943.