As data grows in size,search engines face new challenges in extracting more relevant content for users’*** a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relev...
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As data grows in size,search engines face new challenges in extracting more relevant content for users’*** a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s ***,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate *** a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher *** paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various *** Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences ***findings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.
The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for ...
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In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and comple...
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Accurate age detection is a crucial aspect in various sectors including security, marketing, and demographic analysis, where understanding the age of individuals can lead to better service delivery and enhanced user e...
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Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redunda...
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laptop engineering and intellectual belongings regulation are beautiful fields, however their intersection has grow to be increasingly more extensive in current years. With technology advancing speedy, computer engine...
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It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of *** selection especially the unsup...
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It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of *** selection especially the unsupervised ones are the right way to deal with this challenge and realize the ***,two unsupervised spectral feature selection algorithms are proposed in this *** group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as *** two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be *** effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer *** extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking *** statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs *** detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and *** is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.
Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,t...
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Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by...
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Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS **...
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Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.
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