With the continuous development of Internet technology, using multimedia for virtual application has become a new way. In this paper, by introducing the bayesian classification algorithm, multimedia graphics and video...
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It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms o...
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Parkinson's disease(PD)is a progressive neurodegenerative disorder characterized primarily by classical motor signs of bradykinesia,rigidity,tremors,and postural instability usually manifesti ng unilate rally or a...
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Parkinson's disease(PD)is a progressive neurodegenerative disorder characterized primarily by classical motor signs of bradykinesia,rigidity,tremors,and postural instability usually manifesti ng unilate rally or at least *** disease involves a loss of dopaminergic neurons projecting from the substantia nigra pars compacta to the dorsal striatum.
Lung cancer can be lethal if it is not found in the initial phases. Lung cancer, nevertheless, is challenging to identify early due to the dimensions and form of the nodules. Imaging specialists require the assistance...
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Recent advancements in text-to-image, text-to-video, and large language models have significantly enhanced the performance of various downstream tasks. In the field of Story Visualization, models have been developed t...
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Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specime...
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Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-...
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Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees(SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression(SLR) models,which is much more efficient and flexible than the traditional grid search method. Along this way,we propose to use the conditional Kendall's τ correlation coefficient to select the underlying change points. From the non-parametric view, we propose an efficient greedy splitting method that selects the splits by analyzing the association between residuals and each candidate split variable. Further, with the SLRT as a single-tree predictor, we propose a linear random forest approach that aggregates the SLRTs by a weighted average. Both simulation and empirical studies showed significant improvements than the CART trees and even the random forest.
In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node at...
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In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.
This research paper presents a comprehensive exploration of short-term stock market trend prediction using state-of-the-art machine learning techniques, anchored in a decade-long analysis of the S&P 500 Index. Lev...
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RNA are essential biomolecules and are potent drug targets, as many small RNA molecules are known to play significant roles in many disease pathways. Predicting drug binding sites in RNA molecules is thus an essential...
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