Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey

Altin, Fatma Gül; Budak, İbrahim; Özcan, Fatma

doi:10.1016/j.scp.2023.101060

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…In addition to the size of the dataset, the results are also related to the structure of the dataset. Therefore, according to Goliatt et al [51] and Altin et al [52], it is fundamental to choose the correct algorithm for a specific problem.…”

Section: Discussionmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.

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Section: Discussionmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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“…By accurately predicting the filling level of medical waste bins, healthcare facilities can plan waste collections more efficiently, optimizing the use of resources such as transportation and personnel. 55 This can lead to cost savings and reduce the environmental footprint of waste management processes. Additionally, the proper management of medical waste is essential to prevent public health risks.…”

Section: Methodsmentioning

confidence: 99%

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization

Boudanga,

benhadou,

Medromi

2023

F1000Res

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Background The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed. Methods By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization. Results The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers. Conclusions Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

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“…Using Kernel-based SVM and Deep Learning techniques, Altin et al (2023) analyzed the estimated quantity of medical waste produced by a private hospital. For the Kernel-based SVM, we employed the Epanechnikov function, and for the Deep Learning technique, we used the Maxout activation function.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Buffalo Optimizer based Squeeze and Excitation Network for Garbage Classification for a Sustainable Environment

2024

Global NEST: The International Journal

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<p>A Squeeze and Excitation Network is a deep-learning architectural component designed to enhance networks. The "squeeze" step reduces the spatial dimensions of the input feature maps, and the "excitation" step adaptively recalibrates channel-wise feature responses. This allows the network to focus on more educational features and ignore less useful ones. Garbage classification is a crucial task for sustainable environmental management. It involves categorizing waste into recyclables, organics, and non-recyclables, among other classes. Deep learning models, like the proposed Buffalo Optimizer-based SEN, can play a pivotal role in automating this classification process using computer vision techniques. Garbage Classification: There is still a long way to go until countries worldwide have successfully increased public awareness and implemented measures to prevent the rapid degradation of the natural environment. The annual global generation of e-waste is between 20 and 50 million growing components of municipal solid garbage. The disposal of electronic trash presents significant threats to environmental quality. As a result, pollution monitoring and control are crucial for maintaining a healthy ecosystem. In this research, picture-layered characteristics were extracted using an encoder composed of varying numbers of ResBlocks and the Squeeze-and-Excitation (SE) block, which was built on top of the UNet backbone network. UNet's decoder structure was streamlined, and the number of network model parameters was cut in half. In the meantime, the multiscale feature fusion module was developed to enhance the network's detection accuracy by optimizing its parameters with a bespoke loss function in place of the standard function. The African Buffalo Optimisation Algorithm is also used to fine-tune the hyper-parameters.</p>

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Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey

Cited by 13 publications

References 27 publications

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization

A Deep Learning-Based Buffalo Optimizer based Squeeze and Excitation Network for Garbage Classification for a Sustainable Environment

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