Natural language processing (NLP)'s sentiment analysis subfield has drawn a lot of interest because of its wide range of uses and developing computational methods. This paper offers a comprehensive review of the d...
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Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification so...
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This study digs into the strategic supply chain decision-making process of Bridgestone Tires, a global tyre manufacturing leader, demonstrating the effectiveness of decision tree analysis in dealing with complex uncer...
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This paper explores a novel method in computer security by improving network threat detection through transfer learning. We created a model that effectively recognizes possible dangers by analyzing network behavior us...
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Heart disease is rapidly overtaking other causes of death in India, and it is a major threat to both men and women. Among the top causes of mortality throughout the globe, heart disease ranks first. Therefore, it is c...
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Real-time object detection with remote sensing technologies often requires drones and other battery-driven edge devices in situ to take images at high altitudes. In some cases, tasks are carried out where there exists...
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This research introduces a method for detecting diseases in beans leaves using the well-known deep learning algorithms ResNet50, VGG-19, and Inception. In order to aid in crop management and disease control, it is nec...
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Deep learning models have demonstrated a great effectiveness on the classification of retinal lesions in optical coherence tomography images. However, the performance of these models deteriorates significantly when cl...
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In order to provide an exact comparison between the Novel Deep Belief Networks and the Random Forest based on their ability to detect uterine cancer. Among the groups that are participating in this study, two of them ...
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Contrastive learning is a significant research direction in the field of deep ***,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-tra...
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Contrastive learning is a significant research direction in the field of deep ***,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing *** address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the ***,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters *** experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.
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