We design and analyze an iterative two-grid algorithm for the finite element discretizations of strongly nonlinear elliptic boundary value problems in this *** propose an iterative two-grid algorithm,in which a nonlin...
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We design and analyze an iterative two-grid algorithm for the finite element discretizations of strongly nonlinear elliptic boundary value problems in this *** propose an iterative two-grid algorithm,in which a nonlinear problem is first solved on the coarse space,and then a symmetric positive definite problem is solved on the fine *** main contribution in this paper is to establish a first convergence analysis,which requires dealing with four coupled error estimates,for the iterative two-grid *** also present some numerical experiments to confirm the efficiency of the proposed algorithm.
This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulati...
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This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)***-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting *** paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and *** results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.
Fraud Transactions are haunting the economy of many individuals with several factors across the *** research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the...
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Fraud Transactions are haunting the economy of many individuals with several factors across the *** research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital *** research proposes a novel methodology through three ***,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced ***,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature *** has performed well when compared with the other ***,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and *** efficiency of BinHHO is analyzed with other popular MH *** BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.
In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingex...
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In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the *** works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing *** a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).
Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail ite...
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Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).
Depth images are often used to improve the geometric understanding of scenes owing to their intuitive distance properties. Although there have been significant advancements in semantic segmentation tasks using red-gre...
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Image captioning has seen significant research efforts over the last *** goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically *** real-world appl...
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Image captioning has seen significant research efforts over the last *** goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically *** real-world applications rely on image captioning,such as helping people with visual impairments to see their *** formulate a coherent and relevant textual description,computer vision techniques are utilized to comprehend the visual content within an image,followed by natural language processing *** approaches and models have been developed to deal with this multifaceted *** models prove to be stateof-the-art solutions in this *** work offers an exclusive perspective emphasizing the most critical strategies and techniques for enhancing image caption *** than reviewing all previous image captioning work,we analyze various techniques that significantly improve image caption generation and achieve significant performance improvements,including encompassing image captioning with visual attention methods,exploring semantic information types in captions,and employing multi-caption generation ***,advancements such as neural architecture search,few-shot learning,multi-phase learning,and cross-modal embedding within image caption networks are examined for their transformative *** comprehensive quantitative analysis conducted in this study identifies cutting-edgemethodologies and sheds light on their profound impact,driving forward the forefront of image captioning technology.
Neurological disorders such as Alzheimer’s disease(AD)are very challenging to treat due to their sensitivity,technical challenges during surgery,and high *** complexity of the brain structures makes it difficult to d...
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Neurological disorders such as Alzheimer’s disease(AD)are very challenging to treat due to their sensitivity,technical challenges during surgery,and high *** complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification ***,conventional approaches take a lot of time and might not always be ***,a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of *** in this paper,an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five ***,a network based on Unet++is built to segment the tissues of the ***,using the segmented tissue components as input,the Xception-based deep learning technique is employed to extract high-level ***,the optimized Fractalnet framework is used to categorize the disease condition using the acquired *** proposed strategy is tested on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset that accurately segments brain tissues with a 98.45%of dice similarity coefficient(DSC).Additionally,for themulticlass classification of AD,the suggested technique obtains an accuracy of 99.06%.Moreover,ANOVA statistical analysis is also used to evaluate if the groups are significant or *** findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.
The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance...
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Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machi...
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