Image classification has grown increasingly popular due to the growing significance of machine learning and deep learning. Flower images may sometimes exhibit resemblances in terms of hue, form, and visual characteris...
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ISBN:
(数字)9798350354423
ISBN:
(纸本)9798350354430
Image classification has grown increasingly popular due to the growing significance of machine learning and deep learning. Flower images may sometimes exhibit resemblances in terms of hue, form, and visual characteristics. The problem lies in the classification of flowers. This work employs a hybrid approach that integrates deep learning and machine learning techniques to classify 17 discrete flower species. In order to do this, we utilised the ResNet_50, PCA, and SVM architecture to classify several species from the “Oxford-17” dataset. With this goal in mind, we have made efforts to improve our model in order to get more accuracy compared to similar methods. Prior to inputting our images into our pretrained model, we resized them, and subsequently fine-tuned the model. The dataset was partitioned into two distinct sets: a training set and a testing set. We attained a precision rate of 95.58% while utilising the “Oxford-17” dataset. Our approach outperformed previous machine learning and deep learning-based methods on this dataset.
With technological advancements, smart health monitoring systems have become increasingly vital and popular. The rise of smart homes, appliances, and medical systems, along with the pivotal role of the Internet of Thi...
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As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional methods for optimal control incur significa...
An accurate pulmonary embolism segmentation from computed tomography pulmonary angiography (CTPA) images is very important in pulmonary embolism diagnosis. However, the segmentation process nowadays is done manually b...
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With the improvement of people's living standards, the annual global production of waste continues to rise, but the traditional household waste classification methods are burdened with a heavy task due to the wide...
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The presence of noisy annotations in large-scale facial expression datasets has been a key challenge to Facial expression recognition (FER) performance in the wild. Convolutional neural networks tend to fit clean data...
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Glaucoma, a progressive eye disease known as the 'Silent thief of sight,' poses a significant challenge in early detection and treatment. The conventional method of diagnosing glaucoma involves manual evaluati...
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Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using an intelligent technique to authenticate human by fusion of palm and...
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With the continuous development of social economy, artificial intelligence technology has gradually become an indis-pensable part of various industries, and convolutional neural network shows its powerful learning abi...
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Establishing a sustainable the healthcare system necessitates the development of social, economic, and environmental norms. Although many studies concentrate on these elements separately, an integrated method of handl...
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