35,461 Result(s)
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**hu Wang
Artificial Intelligence
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Article
ConDA: state-based data augmentation for context-dependent text-to-SQL
The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for...
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A new uncertainty processing method for trajectory prediction
In many domains, trajectory prediction a crucial task. Uncertain information, such as complementary and correlated information between multiple features, complex interactive information, weather and temperatur...
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A general framework for improving cuckoo search algorithms with resource allocation and re-initialization
Cuckoo search (CS) has currently become one of the most favorable meta-heuristic algorithms (MHAs). In this article, a simple yet effective framework is proposed for CS algorithms to reinforce their performanc...
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Fast Shrinking parents-children learning for Markov blanket-based feature selection
High-dimensional data leads to degraded performance of machine learning algorithms and weak generalization of models, so feature selection is of great importance. In a Bayesian network (BN), the Markov blanket...
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Combining core points and cluster-level semantic similarity for self-supervised clustering
Contrastive learning utilizes data augmentation to guide network training. This approach has attracted considerable attention for clustering, object detection, and image segmentation. However, previous studies...
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Dual flow fusion graph convolutional network for traffic flow prediction
In recent decades, motor vehicle ownership has increased worldwide year by year, which causes that the accurate prediction of traffic flow on urban road networks becomes more important. However, the dual depen...
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TAENet: transencoder-based all-in-one image enhancement with depth awareness
Recently, CNN-based all-in-one image enhancement methods have been proposed to solve multiple image degradation tasks. However, these CNN-based methods usually have two limitations. One limitation is that they...
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Probabilistic load forecasting based on quantile regression parallel CNN and BiGRU networks
In the dynamic smart grid landscape, accurate probabilistic forecasting of electric load is critical. This paper presents a novel 24-hour-ahead probabilistic load forecasting model by integrating quantile regr...
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Dual stage black-box adversarial attack against vision transformer
Relying on wide receptive fields, Vision Transformers (ViTs) are more robust than Convolutional Neural Networks (CNNs). Consequently, some transfer-based attack methods that perform well on CNNs perform poorly...
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An evolutionary feature selection method based on probability-based initialized particle swarm optimization
Feature selection is a common data preprocessing technique that aims to construct better models by selecting the most predictive features. Existing particle swarm optimization-based feature selection algorithm...
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Novel multi-label feature selection via label enhancement and relative maximal discernibility pairs
Multi-label feature selection is an effective solution to the multi-label data dimensionality disaster problem. However, there are few studies on multi-label feature selection considering label enhancement met...
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Deep bilinear Koopman realization for dynamics modeling and predictive control
The data-driven approaches based on the Koopman operator theory have promoted the analysis and control of the nonlinear dynamics by providing an equivalent Koopman-based linear system associated with nonlinear...
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Open AccessReview of few-shot learning application in CSI human sensing
Wi-Fi sensing has garnered increasing interest for its significant advantages, primarily leveraging Wi-Fi signal fluctuations induced by human activities and advanced neural network algorithms. However, its ap...
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Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansion
Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm...
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Maximum a posteriori estimation and filtering algorithm for numerical label noise
Data quality, especially label quality, may have a significant impact on the prediction accuracy in supervised learning. Training on datasets with label noise causes a degradation in performance and a reductio...
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Optimization of music education strategy guided by the temporal-difference reinforcement learning algorithm
To make up for the shortcomings of traditional music teaching strategies and improve the intelligence of music teaching, this study uses a reinforcement learning (RL) algorithm to conduct an intelligent explor...
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The global Mittag-Leffler synchronization problem of Caputo fractional-order inertial memristive neural networks with time-varying delays
This paper investigates the global Mittag-Leffler synchronization problem of Caputo fractional-order inertial memristive neural networks with time-varying delays. First, the model of fractional-order inertial ...
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On weak convergence of quantile-based empirical likelihood process for ROC curves
The empirical likelihood (EL) method possesses desirable qualities such as automatically determining confidence regions and circumventing the need for variance estimation. As an extension, a quantile-based EL ...
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Article
MFDNet: Multi-Frequency Deflare Network for efficient nighttime flare removal
When light is scattered or reflected accidentally in the lens, flare artifacts may appear in the captured photographs, affecting the photographs’ visual quality. The main challenge in flare removal is to elimi...
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Evaluating renewable energy projects using fuzzy bipolar soft aggregation and entropy weights
The fuzzy bipolar soft set (FBPSS) introduces a novel approach that surpasses the information capacity of conventional fuzzy soft set. The core aim of FBPSS is to simultaneously incorporate two weight vectors,...
