Haar transform, the simplest wavelet transformation, converts the data into wavelet coefficients. The output of the transformation generates larger numbers of Zeros. It acts as a basic step for data compression. The s...
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Cloud computing enabled Wi-Fi networks provide an outstanding opportunity for corporations to distribute extensive area community (WAN) site visitors and advantage from reduced latency because of the proliferation of ...
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Automated email handling has become an essential aspect of managing emails in today's digital age. However, to support this automation, specific types of data and computation are required, and data management is c...
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Unmanned aerial vehicle (UAV) hyperspectral imagery, distinguished by its exceptional spatial granularity and rich spectral diversity, is widely utilized in urban planning, vegetation monitoring. UAV hyperspectral ima...
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ISBN:
(纸本)9798350379860;9798350379877
Unmanned aerial vehicle (UAV) hyperspectral imagery, distinguished by its exceptional spatial granularity and rich spectral diversity, is widely utilized in urban planning, vegetation monitoring. UAV hyperspectral images classification is a crucial application for facilitating feature monitoring. However, the complex textures and low signal-to-noise ratio inherent in UAV hyperspectral images make their classification a formidable challenge. Hence, in this article, an innovative classification method that integrates a Graph Convolutional Network (GCN) with Linear Discriminant Analysis-Felzenszwalb (LDA-Felzenszwalb) superpixel segmentation method was proposed. Firstly, the UAV hyperspectral images are clustered based on the LDA-Felzenszwalb algorithm, which employs downscaling and spectral-spatial similarity. Then, the encoded superpixel images are subjected to feature extraction via graph convolutional networks, aiming to uncover the latent spatial topological relationships within the data. Ultimately, the UAV hyperspectral images are classified with high precision. The effectiveness of the proposed method is demonstrated using the WHU-HI-HongHu dataset, where it achieves an overall classification accuracy of 92.41%, outperforming comparison methods by at least 11.38%. The results show the classification method is highly effective when applied to high spatial resolution hyperspectral images.
This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing ...
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Driven by the wave of digital information, the interior design industry is facing unprecedented changes. Traditional design methods can no longer meet the needs of modern consumers for efficient, accurate and intuitiv...
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We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with eval...
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Sentiment analysis is a useful way to gain insights from text, helping organizations make informed decisions based on the emotions in the information they collect. This project aims to create a sentiment analysis syst...
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The dynamic changes in the global business landscape are being driven by cutting-edge technologies such as artificial intelligence and machine learning, blockchain, and high-performance computing. Recognizing the pivo...
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Predicting student dropout is crucial for early intervention and support, addressing a significant loss of potential human capital in the education system. This paper presents a machine-learning approach to predict st...
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