Energy demand is increased day by day, so there is a need for energy management, and it plays a vital part in the 21st century. At present, non-renewable energy is utilised most of the time. The people are not aware o...
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Retinal abnormalities, such as CNV, DME, and DRUSEN, significantly impact vision and require timely diagnosis for effective treatment. This study explores a machine learning-based approach to classify retinal abnormal...
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The capacity to foresee future stock prices can substantially augment the potential profits of investors;thus, the subject of stock price prediction assumes significance within the investment industry. The present stu...
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This research study aims to create a full time winner predictor program for Counter-Strike Global Offensive tournaments that can predict the round winner of the game being spectated. The algorithm creates accurate pre...
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Smart agriculture leverages cutting-edge information and communication technologies to optimize agricultural practices, and machine learning plays a significant role in achieving this goal. However, due to the convent...
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This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease data...
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Aspect extraction plays a crucial role in understanding the fine-grained nuances of text data, allowing businesses and researchers to gain deeper insights into customer opinions, sentiment distributions, and preferenc...
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Dragon fruit cultivation in Bangladesh is steadily growing. Farmers are increasingly interested in planting dragon fruit in cropland at the marginal level. The cost is little and the yield is simply achieved with grea...
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With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current *** graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the applica...
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With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current *** graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the application of GNN to hyperspectral images has attracted much ***,in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features,which does not take full advantage of various graph neural networks(graph filters).Moreover,the traditional GNNs have the problem of *** alleviate these shortcomings,we introduce a deep hybrid multi-graph neural network(DHMG),where two different graph filters,i.e.,the spectral filter and the autoregressive moving average(ARMA)filter,are utilized in two *** former can well extract the spectral features of the nodes,and the latter has a good suppression effect on graph *** network realizes information interaction between the two branches and takes good advantage of different graph *** addition,to address the problem of oversmoothing,a dense network is proposed,where the local graph features are *** dense structure satisfies the needs of different classification targets presenting different ***,we introduce a GraphSAGEbased network to refine the graph features produced by the deep hybrid *** experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-ofthe-art models.
This paper introduces a novel healthcare system based on blockchain and IPFS technologies, designed to securely store and facilitate the sharing of patient health records. The system is structured into three layers: b...
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