Garbage Classification Using Ensemble DenseNet169

Oktaviana, Ulfah Nur; Azhar, Yufis

doi:10.29207/resti.v5i6.3673

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…DenseNet121 achieved 93.1 accuracy, 94.08% precision, 94.00% recall, 94.03% F1 score and 1 minute 34 seconds training time as the best among other DenseNet types. Further research, also discussing waste classification with DenseNet architecture169 [3]. achieved superior results when dealing with unbalanced classes, showing a 1% increase in accuracy, reaching 91% compared to models using unbalanced data distribution.…”

Section: -03mentioning

confidence: 96%

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Implementation of DenseNet121 Architecture for Waste Type Classification

Zulhusni,

Sari,

Rachmawanto

2024

ASSET

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The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency

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Section: -03mentioning

confidence: 96%

“…People usually utilize organic waste as material for making compost and biogas. However, there are still very few types of inorganic waste that manage it [3]. From the KLHK SIPSN data based on the source, the most waste is generated from households with a percentage of 37.9% and traditional markets with 22.7% data [4].…”

Section: Introductionmentioning

confidence: 99%

Implementation of DenseNet121 Architecture for Waste Type Classification

Zulhusni,

Sari,

Rachmawanto

2024

ASSET

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“…Augmentation can increase the accuracy of the CNN model being trained because using the augmentation method the model obtains more diverse data so as to increase the sensitivity of the model to create an optimal model. Apart from increasing the variety of images, augmentation is also useful for balancing data [9]. It is necessary to balance the data because the data that researchers use are unbalanced between classes.…”

Section: Pre-processingmentioning

confidence: 99%

“…After carrying out the next test, accuracy measurements will be carried out. To increase accuracy, researchers use data augmentation so that the input data is more diverse, balances the data [9] and has the potential to increase the sensitivity of the program.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images

Hapsari,

Purwinarko

2023

Recursive J. of Informatics

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Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications. Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results. Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore. Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested. Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.

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Efficient pneumonia detection using Vision Transformers on chest X-rays

Singh,

Kumar,

Kumar

et al. 2024

Sci Rep

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Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.

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Garbage Classification Using Ensemble DenseNet169

Cited by 4 publications

References 11 publications

Implementation of DenseNet121 Architecture for Waste Type Classification

Implementation of DenseNet121 Architecture for Waste Type Classification

Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images

Efficient pneumonia detection using Vision Transformers on chest X-rays

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