2022 International Conference on Edge Computing and Applications (ICECAA) 2022
DOI: 10.1109/icecaa55415.2022.9936351
|View full text |Cite
|
Sign up to set email alerts
|

Automatic Waste Management based on IoT using a Wireless Sensor Network

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
7
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8
2

Relationship

1
9

Authors

Journals

citations
Cited by 25 publications
(7 citation statements)
references
References 14 publications
0
7
0
Order By: Relevance
“…The test involved training and evaluating each architecture using the same dataset of liver CT scan images. We measured various performance metrics, including accuracy, precision, recall, and F-measure, to assess their effectiveness in predicting liver tumors [20][21][22][23][24][25]. The results of the ablation test demonstrated that ver3, with the inclusion of the attention mechanism, achieved the highest accuracy of 97.99%.…”
Section: Proposed Methodologiesmentioning
confidence: 99%
“…The test involved training and evaluating each architecture using the same dataset of liver CT scan images. We measured various performance metrics, including accuracy, precision, recall, and F-measure, to assess their effectiveness in predicting liver tumors [20][21][22][23][24][25]. The results of the ablation test demonstrated that ver3, with the inclusion of the attention mechanism, achieved the highest accuracy of 97.99%.…”
Section: Proposed Methodologiesmentioning
confidence: 99%
“…During comparative testing of both optical and ultrasonic sensors, each sensor type manifested distinctive accuracy profiles contingent upon specific material under scrutiny [10]. Numerous research efforts address the advancement of IoT ("Internet of Things")-enabled smart waste management systems, aiming to address the issue of overflowing garbage bins and its associated environmental pollution [13]. This research involves augmenting traditional waste containers with a combination of ultrasonic, laser, and weight sensors, facilitating the real-time monitoring of waste levels [14].…”
Section: Usage Of Sensorsmentioning
confidence: 99%
“…Filters of varying sizes, called kernels, are used in every convolution for detecting features like edge, blur, etc. The filter slides over the input image, and the feature map as a matrix is acquired [22][23][24][25].…”
Section: Convolutional Layermentioning
confidence: 99%