APPLICATION INFORMATION MODELING AND MACHINE LEARNING ALGORITHM FOR CLASSIFICATION OF WASTE USING SUPPORT VECTOR MACHINE

D. Chinassylov, A. Kozhamseitova, M. Kalen, R. Omirgaliyev

Аннотация


The ecological state of the world is deteriorating for the worse every year. One of the main problems is inadequate waste disposal and inadequate sorting by waste type, which has led to inadequate treatment of bulk waste in landfills throughout the world. The issue of improper disposal of municipal solid waste (MSW) in Kazakhstan has been raised since 2013, to solve this problem, the first President of the Republic of Kazakhstan, Nursultan Abishevich Nazarbayev, issued a decree on the transition to a green economy. Under the leadership of the Ministry of Energy, it was planned to reduce the amount of inappropriate waste by 40% in the territory of Kazakhstan by 2030. There are a lot of problems in India like inadequate waste collection, transport, treatment, and disposal. Poorly recyclable garbage has a global impact, fouling oceans, obstructing sewers, and creating flooding, transferring infections, increasing respiratory problems due to burning, injuring animals that inadvertently consume waste, and affecting economic development. To classify garbage, researchers utilized a combination of mixed modeling and machine learning techniques. Using machine learning technology, the data obtained can be used to classify and redistribute garbage for any sector around the world.

Ключевые слова


image classification, support vector machines, principal component analysis

Полный текст:

PDF (English)

Литература


Shi, C., Tan, C., Wang, T., & Wang, L. (2021). A waste classification method based on a multilayer

hybrid convolution neural network. Applied Sciences, 11(18), 8572.

Kennedy, T. (2018). OscarNet: Using transfer learning to classify disposable waste. CS230 Report:

Deep Learning. Stanford University, CA, Winter.

Donovan, J. (2016). Auto-trash sorts garbage automatically at the techcrunch disrupt

hackathon. Techcrunch Disrupt Hackaton, San Francisco, CA, USA, Tech. Rep. Disrupt SF, 2016.

Batinić, B., Vukmirović, S., Vujić, G., Stanisavljević, N., Ubavin, D., & Vukmirović, G. (2011). Using ANN

model to determine future waste characteristics in order to achieve specific waste management

targets-case study of Serbia.

Dong-e, Z., Rui, W., Bao-guo, Z., & Yuan-yuan, C. (2019). Research on Garbage Classification and

Recognition Based on Hyperspectral Imaging Technology. Spectroscopy and Spectral Analysis, 39(3),

-922.

Glouche, Y., & Couderc, P. (2013, June). A smart waste management with self-describing objects.

In the Second International Conference on Smart Systems, Devices and Technologies (SMART’13).

Kennedy, T. (2018). OscarNet: Using transfer learning to classify disposable waste. CS230 Report:

Deep Learning. Stanford University, CA, Winter.

Prashant, B. (2019). SVM Classifier Tutorial. https://www.kaggle.com/prashant111/svm-classifiertutorial

Khanna, D., Sahu, R., Baths, V., & Deshpande, B. (2015). Comparative study of classification

techniques (SVM, logistic regression and neural networks) to predict the prevalence of heart disease. International Journal of Machine Learning and Computing, 5(5), 414.

Auria, L., & Moro, R. A. (2008). Support vector machines (SVM) as a technique for solvency analysis.


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