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A Semi Smart Adaptive Approach for Trash Classification
2021
International Journal of Computers Communications & Control
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Garbage sorting into different categories plays an important role in recycling and waste management; but unfortunately, most garbage sorting still depends on labor which has a reverse impact on mankind ...
Waste management and recycling play a crucial factor in world economy sustainability as they prevent the squander of useful materials which can lead in garbage landfill reduction and cost reduction respectively ...
The smart multimedia image classification technology is done by convolutional neural network image classifier that using deep learning convolutional neural network for image classification. ...
doi:10.15837/ijccc.2021.4.4172
fatcat:udlitlonz5atdkwc7cgro25euq
A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System
2021
IEEE Access
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Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. ...
INDEX TERMS Deep learning, embedded Linux system, intelligent garbage classification, MobileNetV3, transfer learning. ...
A prediction model trained using garbage data can be utilized to execute garbage recognition and classification tasks. ...
doi:10.1109/access.2021.3114496
fatcat:qwqnvvpljjf7bnpraqex57qe3y
A LIGHTWEIGHT MODEL FOR PAVEMENT GARBAGE CLASSIFICATION BASED ON DEEP LEARNING
2021
International Journal of Mechatronics and Applied Mechanics
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In response to the requirement of urban environmental health to classify and recycle citizens' garbage, a lightweight model for pavement garbage classification based on deep learning is proposed considering ...
pavement garbage classification and the ordinary convolution layers in the path aggregation network (PANet) are replaced by the depthwise separable convolution layers. ...
Therefore, it is effective and convenient to use deep learning technology to automatically identify the garbage scattered area. ...
doi:10.17683/ijomam/issue10/v1.30
fatcat:6pzgntwo2nggdfwk5hbihgoqxq
Survey onidentification and classification of waste for efficient disposal and recycling
2018
International Journal of Engineering & Technology
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These also talk about the drawbacks faced by the already existing systems and ways to overcome it. ...
Analysing and classifying the garbage using image processing can be a very productive way to process waste materials. ...
Mahalakshmi of the Computer Science and Engineering Department at SRM University. The door to Asst. Prof. P. ...
doi:10.14419/ijet.v7i2.8.10513
fatcat:dzdoewqmx5cmnei36yk2agqjfa
Real-Time Trash Detection for Modern Societies using CCTV to Identifying Trash by utilizing Deep Convolutional Neural Network
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2109.09611v1 fatcat:3hea4htvdndplfcmmnl46sdhe4
Artificial intelligence (AI) evolution, especially in Deep Learning, gives an excellent opportunity to develop real-time trash detection using CCTV cameras. ...
After detecting the trash, the camera records the video of that person for ten seconds who throw trash in society. ...
Acknowledgment This form is about making conformation for the acknowledge of the submitted manuscript, Real-Time Trash ...
arXiv:2109.09611v2
fatcat:aylnbc4pzngobelcc2p5cb2fl4
Improved Feature Extraction Method for Sound Recognition Applied to Automatic Sorting of Recycling Wastes
2020
Journal of Information Processing
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Based on this characteristic, we propose an improved method of MFCC suitable for sounds, rather than voice recognition for identifying recycling wastes and show the results of the automatic sorting of ...
In many types of research for voice recognition, Mel Frequency Cepstral Coefficient (MFCC) has been used as an algorithm for extracting features used for machine learning Support Vector Machines (SVMs) ...
This result calls for smart garbage collection services with an automatic sorting function for recycling wastes. ...
doi:10.2197/ipsjjip.28.658
fatcat:4mj7we7jtnfnjoo375bzyfzwvi
WasNet: A Neural Network-based Garbage Collection Management System
2020
IEEE Access
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In response to the increasing pollution caused by unseparated garbage, classification systems for garbage separation have become very popular. ...
Furthermore, we transplanted the model to the hardware platform and assembled an intelligent trash can; we developed a garbage recognition application to facilitate users to directly identify and receive ...
ACKNOWLEDGMENT The authors thank the Huawei Cloud Waste Classification Competition for providing the data set, which significantly contributed to the proposed waste classification and recycling system. ...
doi:10.1109/access.2020.2999678
fatcat:x7m3gowvvjcdzdy5z5b74dir64
CloudBridge Waste Segregator Automation using Machine Learning
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Our research is done on finding efficient way to segregate waste followed by recycling of wastes. The difficulties in isolation of various products are dealt using machine learning approach. ...
The waste has been dumped to rivers, underground and mixed with soil and by other methods. But all these methods are harmful to environment in long term. ...
Shankaraiah and our project guide Assistant Professor of Department of Electronics and Communication, Ms. Kavyashree M K for their time involved in the discussion and support afforded. ...
doi:10.35940/ijitee.i8511.078919
fatcat:pxyr6ldmqzch3aicxxhrcuelbu
Robot for plastic garbage recognition
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
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One option is to use deep learning and a convolutional neural network (CNN). ...
The main problem that we considered in this article is creating a method for automatically segregating plastic waste into seven specific subcategories based on the camera image. ...
ACKNOWLEDGEMENTS This research was funded by the Polish Minister of Science and Higher Education named "Regional Initiative of Excellence" in the years 2019-2022 project number 020/RID/2018/19, the amount ...
doi:10.11591/ijece.v12i3.pp2425-2431
fatcat:pgkhyrcjhjgc7fqadladmocjdq
An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles
2021
Mobile Information Systems
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At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual ...
In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a ...
In recent years, many researchers have used deep learning technology in the research of garbage identification, classification, and detection. ...
doi:10.1155/2021/5231092
fatcat:brsii4o5vvemxdtongx6qy2nl4
Research on Industrial Waste Recovery Network Optimization: Opportunities Brought by Artificial Intelligence
2020
Mathematical Problems in Engineering
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So, as to realize this new intelligent classification center and explain its value, this paper explains the key implementation technology of this intelligent classification center and validates it by constructing ...
In order to solve this problem, this paper relied on artificial intelligence's prediction technology and image recognition technology to intelligently upgrade the traditional industrial waste planning ...
Realization Technology of Waste Recognition and Detection Based on Deep Learning. Deep learning is an emerging research field of machine learning. ...
doi:10.1155/2020/3618424
fatcat:hpykuezqkbdo5pyfnjvd7dz7ku
Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification
2022
Computers Materials & Continua
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Due to the recent developments of deep learning (DL) and deep reinforcement learning (DRL), waste object classification becomes possible by the identification and detection of wastes. ...
In this aspect, this paper designs an intelligence DRL based recycling waste object detection and classification (IDRL-RWODC) model for smart cities. ...
[8] proposed a cloud based classification approach for automatic machines in recycling factories with ML algorithm. ...
doi:10.32604/cmc.2022.024431
fatcat:f5vs3atalvex3d7mu2g75tvqxm
Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials
2022
Computational Intelligence and Neuroscience
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The usage of compostable trash may be expanded outside fertilizers and dung after the metallic, chemicals, and glass items have been recycled. ...
The system's grabbing and object categorization success rates and computation time are calculated as metrics for evaluation. ...
Acknowledgments e authors deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number (TURSP-2020/344), Taif University, Taif, Saudi Arabia ...
doi:10.1155/2022/2073482
pmid:35571702
pmcid:PMC9106483
fatcat:gj3irfwhqbb4fjsk5yaknq42ea
Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron
2021
Sensors
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Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. ...
Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. ...
Acknowledgments: The authors acknowledge the support from Najran University Saudi Arabia and AGH University of Science and Technology, for the grant No. 16.16.120.773. ...
doi:10.3390/s21144916
fatcat:z5u7vspgobbr3aud6dczdimfvm
IncepX-Ensemble: Performance Enhancement Based on Data Augmentation and Hybrid Learning for Recycling Transparent PET Bottles
2022
IEEE Access
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A garbage classification was proposed [20] using the YOLOv3 network to reduce pollution and maximize recycling resources, introduced the classification concept, and achieved a recognition rate of 95.33% ...
application named SpotGarbage using deep learning architecture to detect and segment garbage in images. ...
doi:10.1109/access.2022.3174076
fatcat:laek36z4afc2tpljmyr6veosb4
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