A Empirical Analysis of Intelligent Waste and Junk Segregation Based on Machine Learning Model

Authors

  • Akanksha Ashok Dubey  M.Tech Student, Department of Computer Science & Engineering, Tulsiram Gaikwad Patil College of engineering and technology, Nagpur, Maharashtra, India
  • Jayant Adhikari  Assistant Professor, Department of Computer Science & Engineering, Tulsiram Gaikwad Patil College of engineering and technology, Nagpur, Maharashtra, India
  • Abhimanyu Patil  Assistant Professor, Department of Computer Science & Engineering, Tulsiram Gaikwad Patil College of engineering and technology, Nagpur, Maharashtra, India

Keywords:

Convolution Neural Networks, Deep Learning, Image Processing, Segregation, Support Vector Machine, Waste Classification.

Abstract

Waste management is a widespread concern in today's society, and the problem is getting worse all the time as the world's population continues to grow. Waste management plays an important role in maintaining a healthy ecological environment. It is critical to properly dispose of garbage at dumping sites in order to facilitate sorting at the base level. For the traditional method of sorting garbage, additional time and personnel are required. Waste may be separated into several categories using a variety of procedures and tools. Image processing may be used to analyses and categories rubbish, which can be a very productive technique to deal with waste items in general. The purpose of this study is to examine existing research papers that have been presented throughout the world. This will allow us to identify the issues, the algorithm that was utilised, and the methodology of the papers that were mentioned. It may also be used to determine whether or not a particular algorithm should be employed in a future investigation. These papers discuss the many approaches and planned systems that were used to separate waste throughout the waste segregation process. These also discuss the disadvantages of the already-existing systems and algorithms that were employed in their research. This document provides a plethora of chances for the generation of new knowledge in the process of developing a new system.

References

  1. Adhithya Prasanna .M1 *, S. Vikash Kaushal2, P. Mahalakshmi. (2018). Survey on identification and classification of waste for efficient disposal and recycling. International Journal of Engineering & Technology. 7 (2.8) (2018) 520-523
  2. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Neural Information Processing Systems, pp. 1106–1114, 2014.
  3. Badilla, N. (2017). 45 percent of Metro's garbage not properly disposed of. Special Report. Retrieved December 27, 2017. From https://www.manilatimes.net/45-percent metros-garbage-not- properly-disposed/370791
  4. Carullo A, Parvis M. An Ultrasonic Sensor for Distance Measurement in Automotive Applications. In: IEEE Sensors J. 1(2):143p.
  5. Cortes, C, “Support-vector networks”. Machine Learning. 20 (3): 273–297, 1995.
  6. Garcia, A.T., (2015). Intelligent Waste Separator. Computacion y Sistemas, Vol. 19, No. 3 2015 pp. 487-500. DOI: 10.13053 /CyS-19- 3-2254 ISSN 2007-9737
  7. Garcıa, P.J., Nieto, Garc´ıa-Gonzalo, E., Alvarez Anton, J.C. Gonzalez, V.M. , Suarez, R.
  8. GMA News Online. (2018). PHL 1 of 5 countries that produce half of the world's plastic waste — UN report. Retrieved June 5, 2018. From http://www.gmanetwork.com/news/lifestyle/healthandwellness/6557 44/phl-1of-5-countries-that-produce-half-of-world-s-plastic-waste- un-report/storyhttp://www.xinhuanet.com/english/2017- 10/13/c_136677472.htm
  9. Balagugan, Raja S, Maheswaran T, Savitha S., Implementation of Automated Waste Segregator at Household Level. International Journal of Innovative Research in Science, Engineering, and Technology. ISSN (Online): 2319-8753. ISSN (Print): 2347-6710
  10. Mayo, Bayon, F. Mateos Mart ´ ´ın, A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance, Journal of Computational and Applied Mathematics (2017), http://dx.doi.org/10.1016/j.cam.2017.02.031
  11. Mittal, G., Yagnik, K. B., Garg, M., and Krishnan, N. C., "Spotgarbage: Smartphone app to detect garbage using deep learning,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ser. UbiComp ’16. New York, NY, USA: ACM, 2016, pp. 940–945. [Online]. Available: http://doi.acm.org/10.1145/2971648.2971731
  12. Ranada, P. (2015). Why PH is the world's 3rd biggest dumper of plastics in the ocean. Rappler Blog. Retrieved October 6, 2015. From https://www.rappler.com/science nature/environment/108276- philippines-plastic-pollution-ocean-conservancystudy
  13. Singh, A., Aggarwal P., & Arora, R., (2016). 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), Sep. 7-9, 2016, AIIT, Amity University Uttar Pradesh, Noida, India
  14. Singh, S., et al. (2017). Waste Segregation System Using Artificial Neural Networks. Helix
  15. Singh, S., et al. (2017). Waste Segregation System Using Artificial Neural Networks. Helix The Scientific Explorer. Helix Vol. 7(5): DOI 10.29042/2017-2053-2058
  16. Tarbell, K.A., et.al. (2014). Applying Machine Learning to the Sorting of Recyclable Containers. National-Waste-Processing-Conference
  17. The Scientific Explorer. Helix Vol. 7(5): DOI 10.29042/2017-2053- 2058
  18. (2017). Philippines grapples with 35,000 tons of garbage daily: ADB expert. Special Report. Retrieved October 13. 2017.
  19. Balakrishnan, Kavya & T B Swathy, Rosmi & T D, Subha. (2016). Automatic Waste Segregator and Monitoring System. Journal of Microcontroller Engineering and Applications
  20. Aleena V.J.*, Kavya Balakrishnan, Rosmi T.B., Swathy Krishna K.J., Sreejith S, T.D. Subha. (2016). Automatic Waste Segregator and Monitoring System. Journal of Microcontroller Engineering and Applications ISSN: 2455-197X (online) Volume 3, Issue 2.
  21. George E. Sakr, Maria Mokbel, Ahmad Darwich, Mia Nasr Khneisser, Ali Hadi – 2016 Comparing Deep Learning And Support Vector Machines for Autonomous Waste Sorting by IEEE International Multidisciplinary Conference on Engineering Technology
  22. Yang, Mindy, and Thung, Gary “Classification of Trash for Recyclability Status”Stanford university Available: http://cs229.stanford.edu/proj2016/poster/ThungYangClassificationO fTrashForRecyclabilityStatus-poster.pdf
  23. Balagugan, Raja S, Maheswaran T, Savitha S (2017). Implementation of Automated Waste Segregatorat Household Level. International Journal of Innovative Research in Science, Engineering, and Technology. Vol. 6, Issue 10, October 2017
  24. BL Theraja, AK Theraja, A Text Book of Electrical Technology, volume 2, S Chand &co.,2005
  25. Ashwini D. Awale¹, Akshada A. Margaje², Akshay B. Jagdale3. (2017). Automated Waste Segregator. Journal Of Information, Knowledge And Research In Electronics And Communication Engineering ISSN: 0975 – 6779| NOV 16 TO OCT 17 | VOLUME – 04, ISSUE – 02
  26. Vernise L Tantuco(2018). Why can’t the Philippines solve its trash problem?. Rappler. September 14, 2018 from https://www.rappler.com/newsbreak/in-depth/210292-reason- philippines-cannot-solve-trash-problem.
  27. Jeaz Harobado. Alarming Waste Problem in the Philippines. Personal Blog. Retrieved August 24, 2014 from
  28. https://jeazharobado8.wordpress.com/2014/08/24/hre044ahraugust26 2014/
  29. Christopher Henry Ng. (2014). Environmental Problem Issues of garbage in the Philippines. Ender’s Blog. http://cng0268.blogspot.com/2011/07/environmental-problem-issues- of-garbage.html
  30. Dille, S. (2019). Simple to use, but serious data science knowledge still required. How Microsoft Azure Machine Learning Studio Clarifies Data Science. Retrieved May 12, 2019 from https://towardsdatascience.com/how-microsoft-azure-machine- learning-studio-clarifies-data-science-8e8d3e6ed64e
  31. GoogleDevelopers(2019). Classification: Accuracy. Updated March 5, 2019 from https://developers.google.com/machine-learning/crash- course/classification/accuracy
  32. Shavlik, J. W., Mooney, R. J., & Towell, G. G. (1991). Symbolic and neural learning: An experimental comparison. Machine Learning , 6 , 111-143.
  33. Xin, M., & Wang, Y. (2019). Research on image classification model based on deep convolution neural network. Xin and Wang EURASIP Journal on Image and Video Processing (2019) 2019:40.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-05-30

Issue

Volume 8, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Akanksha Ashok Dubey, Jayant Adhikari, Abhimanyu Patil, " A Empirical Analysis of Intelligent Waste and Junk Segregation Based on Machine Learning Model, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.45-52, May-June-2022.