International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
www.irjet.net
An Automated Machine Learning Approach For Smart Waste Management System Nagaveni C M1 Dept. of MCA, Vidya Vikas Institute Of Engineering And Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------IoT . The main purpose of this study has been to establish Abstract - A waste management system is the concept in
models for specific and accurate waste prediction and classification in the industrial environment by comparing two classification algorithms that are CNN and SVM. In this article , We focused mainly on three types of waste paper, plastic and metal which are abundant in industrial manufacturing and production facilities, also a subset of MSW and are commonly found in the everyday household waste which require excess attention for waste management.
an organization that is used to dispose, reduce, reuse, and prevent waste. Some of the waste disposal methods are recycling, composting, incineration, landfills, bioremediation, waste of energy, and waste minimization. This article shows the use of automated machine learning for solving a problem of real life waste management strategies.
Good and effective waste management practices have become difficult because of our consumption behavior and the changing socio-economic conditions. The waste management is a problem that requires technology, economics, and sociocultural and political activities to work together and get good results.
1.1 Objectives The major objective of this project is to prepare a userfriendly input files panels that can handle large volumes of data to fulfill it. Smart waste management is a idea where we can control lots of problems which disturb the society in pollution and diseases and produce a lot more harmful effects to be faced . There will be validity checks, which will be applied to the data as soon as the input is given.
In specific terms,this article focus on the detection of recycling container using sensor measurements.the methods that we have investigated had existing manually designed model and its modifications and also the conventional machine learning algorithms and procedures.
Prime objectives of design are as follows: Monitoring the waste management, Avoiding of human intervention in it, Reducing human time and effort to provide ease in each process of management.
The solution that is implemented has used a Random forest classifier on a bunch of features that are based on filling level at unique time spans.
1.2 Scope The proposed system will focus on finding technical solutions to recycle the waste .The maintenance of sanity in the society and preventing pollution in our surroundings might result in well defined waste management .if not taken well care it maay result in severe health complications to the inhabitants of the areas where garbage and environment conservation is not taken seriously.
Key Words: Waste management, Waste disposal, Waste minimization.
1. INTRODUCTION Gathering and dumping of waste in dumping sites was a common practice in every household in ancient Athens. Self-waste management was most important thing. People should have gone to all streets daily and take the garbage away from the town. Today, it has become a common practice to handle the waste automatically many processes which used to be operated manually. Across almost all essential aspects of life, the method of making things automated is being used ,which is making the process way more easier.
2. EXISTING SYSTEM The current garbage collection management involves individuals who walk from in every household giving receipt to show payments was many for garbage collection service. To get the service of the individuals or company, a resident or flat caretaker has to look for them and request for their service.
Automotive industries, electronics manufacturing, medical, welding, food service, law enforcement and transportation are the example of industries that have invested in improving and making full use of AI, machine learning and
© 2022, IRJET
|
Impact Factor value: 7.529
|
ISO 9001:2008 Certified Journal
|
Page 1843