Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite

Handhayani, Teny; Hendryli, Janson

doi:10.1007/978-3-031-16075-2_4

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Furthermore, EfficientNet has consistently demonstrated superior accuracy while consuming fewer computational resources and achieving faster computation speeds [21]. Given its performance advantages, EfficientNet is the foundational architecture for our waste classification task, contributing to enhanced accuracy, efficiency, and model adaptability [22]. Figure 3 provides an overview of the EfficientNet architecture.…”

Section: B Efficientnetmentioning

confidence: 99%

A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction

Santoso,

Hanif,

Magdalena

et al. 2024

JOIV : Int. J. Inform. Visualization

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The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.

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Section: B Efficientnetmentioning

confidence: 99%

A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction

Santoso,

Hanif,

Magdalena

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Ahmed et al [23] ran deep learning algorithms on the terminal user's smartphone using the lightweight MobileNet network with TensorFlow Lite compression and quantization to reduce the edge device's memory usage and computation time. Handhayani et al [24] developed a practical Android mobile application for garbage classification using the EfficientNet Lite model.…”

Section: Scene Recognition Models and Their Deploymentmentioning

confidence: 99%

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Zhao,

Shi,

Han

et al. 2024

Applied Sciences

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Image aesthetics processing (IAP) is used primarily to enhance the aesthetic quality of images. However, IAP faces several issues, including its failure to analyze the influence of visual scene information and the difficulty of deploying IAP capabilities to mobile devices. This study proposes an automatic IAP system (IAPS) for mobile devices that integrates machine learning and traditional image-processing methods. First, we employ an extremely computation-efficient deep learning model, ShuffleNet, designed for mobile devices as our scene recognition model. Then, to enable computational inferencing on resource-constrained edge devices, we use a modern mobile machine-learning library, TensorFlow Lite, to convert the model type to TFLite format. Subsequently, we adjust the image contrast and color saturation using group filtering, respectively. These methods enable us to achieve maximal aesthetic enhancement of images with minimal parameter adjustments. Finally, we use the InceptionResNet-v2 aesthetic evaluation model to rate the images. Even when employing the benchmark model with an accuracy of 70%, the score of the IAPS processing image is verified to be higher and more effective compared with a state-of-the-art smartphone’s beautification function. Additionally, an anonymous questionnaire survey with 100 participants is conducted, and the result shows that IAPS enhances the aesthetic appeal of images based on the public’s preferences.

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Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite

Cited by 2 publications

References 20 publications

A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction

A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

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