Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corros...
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In urban areas of Japan, such as Tokyo, overcrowding in condominiums and office buildings leaves limited flat land for installing solar panels, resulting in inadequate installation of renewable energy. To advance phot...
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Alzheimer’s Disease (AD) is a dangerous disease that is known for its characteristics of eroding memory and destroying the brain. The classification of Alzheimer's disease is an important topic that has recently ...
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The world communities have suffered from the COVID-19 pandemic for the last two years. Even though many countries have started to normalise the situation, the COVID-19 still becomes a severe threat in the future. Heal...
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With heterogeneous programming continuously on the rise, performance portability is still to be improved. SYCL provides the nd-range parallel-for paradigm for writing data-parallel kernels. This model allows barriers ...
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Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching ...
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Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker se...
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The world communities have suffered from the COVID-19 pandemic for the last two years. Even though many countries have started to normalise the situation, the COVID-19 still becomes a severe threat in the future. Heal...
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ISBN:
(纸本)9781450385244
The world communities have suffered from the COVID-19 pandemic for the last two years. Even though many countries have started to normalise the situation, the COVID-19 still becomes a severe threat in the future. Healthy habits, such as complete and frequent handwashing, still need to be practised. These habits can minimise the transmission risks. The paper proposed a single-board computer system that aims to assess the handwashing steps. The standardised handwashing procedure is used to validate the acquired video of hand movement. The system is installed in a Raspberry Pi and receives video data from the connected mini camera. The deep learning model is implemented to provide classification capabilities. The assessment result is summarised according to the movement completeness and total duration. The testing stages found that the proposed system can provide accuracy and F1-score values of 82.55% and 86.66%, respectively.
The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. Howev...
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Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods...
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Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.
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