Skeletal Age Estimation from Hand Radiographs Using Ensemble Deep Learning

Hirasen, Divyan; Pillay, Verosha; Viriri, Serestina; Gwetu, Mandlenkosi Victor

doi:10.1007/978-3-030-77004-4_17

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…The computational findings provide compelling evidence of the effectiveness of the proposed model in addressing the challenges associated with leaf disease classification. Furthermore, the model's superior performance over existing single-model and homogeneous ensemble models, including DenseNet121, ResNet50, MobileNetV2, EfficientNet-B2, EfficientNet-B3, and Inception-ResNet-v2, demonstrates its potential to become a new state-of-the-art solution for this domain; it can be adapted to classify diverse datasets as mentioned in Bjånes et al [68], Hirasen et al [69], Lee et al [70], Mohammed and Kora [29], and Wei and Liu [71].…”

Section: Advancing Leaf Disease Classification In Cau: a Meta-learner...mentioning

confidence: 96%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

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Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.

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Section: Advancing Leaf Disease Classification In Cau: a Meta-learner...mentioning

confidence: 96%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

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Prediction of On-Road CO2 Emission in Urban Area Using State-of-The-Art Ensemble Machine Learning Model

Subramaniam,

Yusof

2024

Earth and Environmental Sciences Library

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Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country’s immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India’s renewable energy transition and air quality. India’s economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India’s climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government’s must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

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Skeletal Age Estimation from Hand Radiographs Using Ensemble Deep Learning

Cited by 5 publications

References 8 publications

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Prediction of On-Road CO2 Emission in Urban Area Using State-of-The-Art Ensemble Machine Learning Model

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

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