A Distributed Architecture for Smart Recycling Using Machine Learning

Ziouzios, Dimitris; Tsiktsiris, Dimitris; Baras, Nikolaos; Dasygenis, Minas

doi:10.3390/fi12090141

Cited by 38 publications

(22 citation statements)

References 29 publications

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“…Ziouzios et al [8] proposed a cloud based classification approach for automatic machines in recycling factories with ML algorithm. They trained an effective MobileNet method, capable of classifying 5 distinct kinds of waste.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Duhayyim¹,

Eisa²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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The Smart City concept revolves around gathering real time data from citizen, personal vehicle, public transports, building, and other urban infrastructures like power grid and waste disposal system. The understandings obtained from the data can assist municipal authorities handle assets and services effectually. At the same time, the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic. Besides, the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability. Few of the commonly available wastes are paper, paper boxes, food, glass, etc. In order to classify the waste objects, computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash. 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. The goal of the IDRL-RWODC technique is to detect and classify waste objects using the DL and DRL techniques. The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network (Mask RCNN) based object detection and DRL based object classification. In addition, DenseNet model is applied as a baseline model for the Mask RCNN model, and a deep Q-learning network (DQLN) is employed as a classifier.Moreover, a dragonfly algorithm (DFA) based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Duhayyim¹,

Eisa²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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“…Many studies have shown that the consumption of products is a crucial factor in the amount of SWM (Bosire et al, 2017). The challenges in the SWM is correlated environmental effect and also economic impacts on the world (Ziouzios et al, 2020). Around the planet, solid waste contributes to climate change and is one of the leading causes of carbon emissions.…”

Section: Machine Learning Approaches In Swmmentioning

confidence: 99%

“…• Edge devices and fog nodes support performing analytics; however, dedicated hardware needs to be designed and developed with high computational power for performing the AI models (Zou et al, 2019). However, with recent advancements, low power machine learning processors are embedding in the local server, gateways, and routers.…”

Section: Future Directionsmentioning

confidence: 99%

Internet of things and machine learning‐based approaches in the urban solid waste management: Trends, challenges, and future directions

Joshi¹,

Bharti²,

Singh

2021

Expert Systems

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Solid waste management (SWM) is a crucial management entity in urban cities to handle the waste from its generation to disposal to accomplish a clean environment. The waste management operation mainly encompasses various climatic, demographic, environmental, legislative, technological, and socioeconomic dimensions. The traditional approaches deliver limitations in the process of predict and optimizing such composite non-linear operations. The integration of the internet of things (IoT) and artificial intelligence (AI) methods have progressively gained attention by delivering potential alternatives for resolving the difficulties in SWM. This article presents a review of the significance of the amalgamation of IoT and machine learning (ML) in the SWM to predict waste generation, waste classification, route optimization, estimation of methane emissions, and so forth. The article covers the application of each ML model for the activities, including SWM, compositing, incineration, pyrolysis, gasification, landfill, and anaerobic digestion.Moreover, it is concluded that the decision tree and random forest (DT-RF) algorithm is minor implemented, and artificial neural network (ANN) is implemented majorly in the SWM. The large number of data sets covered in the publication are secured and hidden; it limits replicating the AI models; this is also one key constraint of the non-implementation of AI models in SWM. Scarcity of data, accurate data, rare availability of customized AI models for tackling the activities in SWM are the limitations identified from the previous studies. Implementation of low-power ML processors, edge and fog computing-based devices is the future direction for overcoming SWM limitations.

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“…Smart waste sorting can be achieved using an object detection model and by engaging the householders using machine learning-based gamification recycling applications [2,5]. Further, a cloud-based smart waste segregation architecture can be built using an object detection model to sort recyclable waste [3]. The ability of Convolution Neural Networks (CNNs) to provide reliable accuracy, learn new unique and abstract features shows promise in the domain of computer vision [10,11,12].…”

Section: Related Workmentioning

confidence: 99%

“…Recycling centers use mechanical and chemical technology for plastic waste sorting. Current research is focusing on image-based smart waste recycling [1,2,3,4,5]. Plastic waste is sorted into its individual types namely, Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polypropylene (PP), Polystyrene (PS), Polyvinyl Chloride (PVC), and Low-Density Polyethylene (LDPE).…”

Section: Introductionmentioning

confidence: 99%

An Object Detection and Scaling Model for Plastic Waste Sorting

Padalkar¹,

Pathak²,

Stynes³

2021

Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bolog

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Plastic waste sorting involves the separation of plastic into its individual plastic types. This research proposes an Object Detection and Scaling Model for plastic waste sorting to detect four types of plastics using the WaDaBa dataset. This research compares the Object Detection and Scaling Models Scaled-Yolov4 and EfficientDet. Results demonstrate that Scaled-Yolov4-CSP outperforms the state of the art, Colour-Histogram based Canny-Edge-Gaussian Filter, by 21% accuracy.

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A Distributed Architecture for Smart Recycling Using Machine Learning

Cited by 38 publications

References 29 publications

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Internet of things and machine learning‐based approaches in the urban solid waste management: Trends, challenges, and future directions

An Object Detection and Scaling Model for Plastic Waste Sorting

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