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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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Tensor discriminant analysis on grassmann manifold with application to video based human action recognition
Representing videos as linear subspaces on Grassmann manifolds has made great strides in action recognition problems. Recent studies have explored the convenience of discriminant analysis by making use of Gras...
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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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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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Drfnet: dual stream recurrent feature sharing network for video dehazing
The primary effects of haze on captured images/frames are visibility degradation and color disturbance. Even though extensive research has been done on the tasks of video dehazing, they fail to perform better ...
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Aspect category sentiment classification via document-level GAN and POS information
The purpose of aspect-category sentiment classification (ACSC) is to determine the sentiment polarity of the predefined aspect category from the texts. Current methods for ACSC have two main limitations. Since...
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Data-driven quantification and intelligent decision-making in traditional Chinese medicine: a review
Traditional Chinese medicine (TCM) originates from the practical experience of human beings’ constant struggle with nature. In five thousand years, TCM has gradually risen from empirical medicine to modern evi...
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BPSO-SLM: a binary particle swarm optimization-based self-labeled method for semi-supervised classification
The self-labeled methods have been favored by scholars in semi-supervised classification. Mislabeling is a great challenge for self-labeled methods and one of the reasons for mislabeling is that high-confidenc...
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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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Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications
Knowledge Graph (KG) has attracted more and more companies’ attention for its ability to connect different types of data in meaningful ways and support rich data services. However, due to privacy concerns, dif...
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Unsupervised domain adaptation via feature transfer learning based on elastic embedding
Supervised classification algorithms usually require a large quantity of well-labeled samples for training to achieve satisfied performance. Nevertheless, it is prohibitively difficult to create such datasets ...
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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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Open AccessA hospitalization mechanism based immune plasma algorithm for path planning of unmanned aerial vehicles
Unmanned aerial vehicles (UAVs) and their specialized variants known as unmanned combat aerial vehicles (UCAVs) have triggered a profound change in the well-known military concepts and researchers from differe...
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Efficient evolutionary neural architecture search based on hybrid search space
Manually designed convolutional neural networks have demonstrated excellent performance in various domains, but designing neural networks suitable for specific tasks poses significant challenges, and the emerg...
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Long-short interest network with graph-based method for sequential recommendation
In recommender systems, sequence information is crucial. Sequence data contains user preferences and reflects the evolution of user interests over time. Therefore, how to utilize sequence information to captur...
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A fast DBSCAN algorithm using a bi-directional HNSW index structure for big data
The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is one of the most popular and effective density-based clustering algorithms at present. Although it can effectively identify ...
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Advancing ASD detection: novel approach integrating attention graph neural networks and crossover boosted meerkat optimization
Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the lives of many children due to its hidden symptoms. Early detection of ASD is challenging because of its complex a...
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Open AccessDBHC: Discrete Bayesian HMM Clustering
Sequence data mining has become an increasingly popular research topic as the availability of data has grown rapidly over the past decades. Sequence clustering is a type of method within this field that is in ...
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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...
