1,801 Result(s)
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An overview of semantic-based process mining techniques: trends and future directions
Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or enhancement. Domain experts have developed several process mining al...
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Improving graph-based recommendation with unraveled graph learning
Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph Neural Networks. Furthermore, the integration of contrastive lea...
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Retraction Note: Reality of virtual damage identification based on neural networks and vibration analysis of a damaged bridge under a moving vehicle
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Retraction Note: Lip segmentation using localized active contour model with automatic initial contour
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Residual deep fuzzy system with randomized fuzzy modules for accurate time series forecasting
The data-driven modular deep fuzzy model has demonstrated excellent forecasting performance due to its clear architecture and powerful fuzzy inference ability. However, the fixed structure predesigned for spec...
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M-Mix: Patternwise Missing Mix for filling the missing values in traffic flow data
Real-world traffic flow data often contain missing values, which can limit its usability. Although existing deep learning-based imputation methods have shown promising results by reconstructing observed values...
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Open AccessMulti-class vulnerability prediction using value flow and graph neural networks
In recent years, machine learning models have been increasingly used to detect security vulnerabilities in software, due to their ability to achieve high performance and lower false positive rates compared to ...
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A new neighbourhood-based diffusion algorithm for personalized recommendation
Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user...
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A novel metric learning method based on constructing a uniform data hypersphere via simulated forging approach
Non-uniformly distributed data in unbalanced datasets have the phenomenon of data stacking and data scattering. However, most traditional metric learning algorithms often overemphasize the intra-class compactn...
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C22MP: the marriage of catch22 and the matrix profile creates a fast, efficient and interpretable anomaly detector
Many time series data mining algorithms work by reasoning about the relationships the conserved shapes of subsequences. To facilitate this, the Matrix Profile is a data structure that annotates a time series by r...
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Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints
The primary emphasis of this work is on investigating the practical prescribed time tracking issue for a type of fractional-order state constrained system with immeasurable states. These unknown system states ...
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Counterfactual contextual bandit for recommendation under delayed feedback
The recommendation system has far-reaching significance and great practical value, which alleviates people’s troubles about choosing from a huge amount of information. The existing recommendation system usuall...
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Siamada: visual tracking based on Siamese adaptive learning network
Recently, Siamese trackers based on region proposal networks (RPN) have gained a lot of popularity. However, the design of RPN requires manual tuning of parameters such as object-anchor intersection over union...
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Online content-based sequential recommendation considering multimodal contrastive representation and dynamic preferences
The online content, including live streaming and short videos, provides abundant visual and textual product information to users, which offers insights into users’ multiple and changeable preferences toward pr...
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Invisible backdoor learning in regional transform domain
The rapid develo** deep learning is highly required by resources and computing resources, which easily leads to backdoor learnings. It is difficult for existing schemes to strike a balance among trigger conc...
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Contrastive feature decomposition for single image layer separation
The key challenge of image layer separation stems from recognizing different components in a single image. Typical methods optimize the modeling of different components by performing low-level supervision on t...
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On the adversarial robustness of generative autoencoders in the latent space
The generative autoencoders, such as the variational autoencoders or the adversarial autoencoders, have achieved great success in lots of real-world applications, including image generation and signal communic...
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BotCL: a social bot detection model based on graph contrastive learning
The proliferation of social bots on social networks presents significant challenges to network security due to their malicious activities. While graph neural network models have shown promise in detecting soci...
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Open AccessPINN-CHK: physics-informed neural network for high-fidelity prediction of early-age cement hydration kinetics
Cement hydration kinetics, characterized by heat generation in early-age concrete, poses a modeling challenge. This work proposes a physics-informed neural network (PINN) named PINN-CHK designed for cement hyd...
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Adaptive semi-supervised learning from stronger augmentation transformations of discrete text information
Semi-supervised learning is a promising approach to dealing with the problem of insufficient labeled data. Recent methods grouped into paradigms of consistency regularization and pseudo-labeling have outstandi...
