scholarly journals Real Life Smart Waste Management System [DRY, WET, RECYCLE, ELECTRONIC & MEDICAL]

2021 ◽  
pp. 631-640
Author(s):  
Dr. A. Radhika
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Waste Management ◽  
Object Detection ◽  
Management System ◽  
Real Life ◽  
Waste Management System ◽  
Waste Classification ◽  
Waste Segregation ◽  
Respective Category

The problem of real-life smart waste management system can be solved using automatic waste segregation. In particular, the focus of the article is on the problem of detection (i.e., waste classification). In these 5 classes of waste are taken and segregated them into 5 categories namely dry, wet, recycle, electronic and medical. This system will automatically detect the waste object and segregate it into the respective category. The use of machine learning allowed improving the model with more accuracy. Convolutional Neural Networks (CNN) algorithm which is best used for image classification is used for object detection. The models that was trained are ResNet50, VGG16, InceptionV3 and MobileNetV2. Finally, when compared to the results of all these models, MobileNetV2 has given us the best and highest accuracy of about 98% and 99% respectively.

scholarly journals Household Waste Management System Using IoT and Machine Learning

2020 ◽  
Vol 167 ◽  
pp. 1950-1959 ◽  
Author(s):  
Sonali Dubey ◽  
Pushpa Singh ◽  
Piyush Yadav ◽  
Krishna Kant Singh
Keyword(s):  
Machine Learning ◽  
Waste Management ◽  
Management System ◽  
Household Waste ◽  
Waste Management System ◽  
Household Waste Management

scholarly journals Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

Recycling ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 65
Author(s):  
Ali Hewiagh ◽  
Kannan Ramakrishnan ◽  
Timothy Tzen Vun Yap ◽  
Ching Seong Tan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Waste Management ◽  
Management System ◽  
Learning Algorithms ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Waste Management System

Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.

scholarly journals Artificial Intelligence and Machine Learning in Waste Management and Recycling

2020 ◽  
Vol 8 (1) ◽  
pp. 43-52
Author(s):  
Alim Al Ayub Ahmed ◽  
ABM Asadullah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Waste Management ◽  
Waste Disposal ◽  
Management System ◽  
Open Space ◽  
Construction Material ◽  
Human Communication ◽  
Public Places ◽  
Waste Management System

Waste management is one of the biggest problems facing the world in any developed or developing country. An important aspect of waste management is that the waste bin in the open space is properly filled before the next cleaning process begins. This can eventually lead to various hazards such as dirt and bad odor in the area, which can lead to the spread of various diseases. Population growth has significantly reduced toilets through the waste management system. Laying garbage in public places creates a polluted environment. To eliminate or reduce waste and maintain good hygiene, it requires a waste-based waste management system. The need for proper waste management is not limited to proper collection and disposal of waste. It continues to be a waste disposal and recyclable level. Recycling is considered a major benefit because in addition to waste disposal, our reliance on immature materials is declining. By recycling metal, plastic and glass, the use of decomposing waste can extend beyond compost and manure. Metals can be reused and plastic can be mixed with clay filler, which can lead to soil compaction. After deep cleaning the glass construction material can be broken down and re-melted into new articles. This article is about machine learning and the use of artificial intelligence in the most viable areas and understanding the full need for human communication.

Waste Management in the Czech Republic

2005 ◽  
Vol 2 (6) ◽  
pp. 502-511 ◽  
Author(s):  
Eva Valentova
Keyword(s):  
Waste Management ◽  
Management System ◽  
Waste Treatment ◽  
Fundamental Principle ◽  
New Developments ◽  
The Czech Republic ◽  
Waste Management System ◽  
Waste Classification ◽  
Management Plans ◽  
New Strategies

AbstractMajor changes have taken place in the Czech waste management system based on national waste treatment legislation adopted in 2001. This legislation revised the interpretation of essential terms, including "waste", so as to reflect new developments in the field of EU waste management. It also adopted new strategies in the area of waste classification and waste management; as a fundamental principle, waste recovery is now given priority over waste disposal. Waste management plans have become a critical component of the Czech waste management system. The powers and responsibilities of municipal councils and State authorities have also been re-organised.

Healthcare waste management in a Brazilian university public hospital

2018 ◽  
Vol 37 (3) ◽  
pp. 278-286 ◽  
Author(s):  
Elci de Souza Santos ◽  
Karla Magna dos Santos Gonçalves ◽  
Marcos Paulo Gomes Mol
Keyword(s):  
Waste Management ◽  
Management System ◽  
Safety Management ◽  
Risk Identification ◽  
University Hospital ◽  
Healthcare Waste ◽  
Waste Management System ◽  
Infectious Risk ◽  
Healthcare Waste Management ◽  
Waste Segregation

Some healthcare waste presents hazardousness characteristics and requires specific procedures to ensure the safety management. Waste segregation is an important action to control the risks of each type of waste. Healthcare waste indicators also may improve the waste management system. The aim of this article was to evaluate the healthcare waste management in a Brazilian university hospital, as well as the waste indicators, quantifying and qualifying the waste generation. Weighing of wastes occurred by sampling occurred sampling of seven consecutive days or daily, between 2011 and 2017. General wastes represent more than 55.6% of the total generated, followed by infectious, sharps and chemicals wastes, respectively, 39.1%, 2.9% and 2.4%. The generation rate in 2017 was 4.09 kg bed−1 day−1, including all types of wastes. Non-dangerous wastes represented around 93.3%, including infectious wastes with low potential risks, while dangerous was represented by high infectious risk (1.4%), chemicals (2.4%) and sharps (2.9%). Healthcare waste indicators may favour the risk identification and improve the waste management system, in particular when involving hazardous wastes. Failures in healthcare waste segregation could represent, in addition to the health risks, unnecessary expenses.

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