Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Hindarto, Djarot; Afarini, Nihayah; Esthi H, Endah T

doi:10.33387/jiko.v6i3.7008

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…CNNs are ideal for Javanese script classification because of their processing image data. CNN applies convolutional layers to Javanese script imagery to simulate human vision and understand the features of selfconfidence [17].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Classification of Javanese Nglegena Script Using Complexvalued Neural Network

Rahmawati,

Muhaimin,

Prasetya

2024

jiko

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Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Classification of Javanese Nglegena Script Using Complexvalued Neural Network

Rahmawati,

Muhaimin,

Prasetya

2024

jiko

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“…Techniques ranging from Support Vector Machines (SVMs) to Random Forests have been utilized to classify sleep stages from polysomnography data [13]- [15]. More recently, advanced deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) variants [16], have emerged as powerful tools for uncovering complex patterns, and especially in sleep-related data [17]. These models have shown a capacity to significantly enhance accuracy and efficiency over traditional statistical methods, adeptly managing large datasets enriched with multiple input variables [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Hybrid Neural Network Architectures for Predicting Sleep Disorders From Structured Data

Airlangga

2024

jiko

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The accurate diagnosis of sleep disorders is crucial for effective treatment and management, yet current methods often rely on subjective assessments and are not always reliable. This research examines the efficacy of various neural network architectures, including dense networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and innovative hybrid models, in predicting sleep disorders from structured health data. Our study focuses on comparing the performance of these models using metrics such as accuracy, precision, recall, and F1 score across a dataset comprising 400 individuals with detailed sleep and lifestyle data. Our findings demonstrate that while traditional models like dense networks and CNNs for structured data yield robust results, hybrid models, particularly the CNN-Transformer, significantly outperform others. This model effectively integrates convolutional layers with Transformer’s attention mechanisms, excelling in handling complex data interactions and providing superior predictive accuracy with an F1 score and accuracy reaching as high as 0.91. Conversely, RNN models, designed to capture temporal data dependencies, showed less efficacy, underscoring the importance of model selection aligned with data characteristics. This suggests that for datasets not exhibiting strong temporal features, models leveraging spatial relationships or advanced attention mechanisms are more suitable. This study not only advances our understanding of neural network applications in medical diagnostics but also highlights the potential of hybrid models in enhancing diagnostic accuracy. These insights could lead to significant improvements in the early detection and treatment of sleep disorders, thereby enhancing patient outcomes and contributing to the broader field of medical informatics.

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“…On the other hand, VGG16 (Hindarto & Afarini, 2023), (Hindarto, 2023b) due to its intricate and complex structure, has a higher capacity to comprehend intricate aspects of images, potentially enhancing the precision of fire detection. However, it is still necessary to conduct a comprehensive assessment of the performance of these two models in the specific context of identifying forest fires.…”

Section: Introductionmentioning

confidence: 99%

Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

Hindarto

2023

CNAHPC

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The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.

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Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Cited by 5 publications

References 11 publications

Classification of Javanese Nglegena Script Using Complexvalued Neural Network

Classification of Javanese Nglegena Script Using Complexvalued Neural Network

Evaluating Hybrid Neural Network Architectures for Predicting Sleep Disorders From Structured Data

Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

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