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Dual-view graph convolutional network for multi-label text classification
Multi-label text classification refers to assigning multiple relevant category labels to each text, which has been widely applied in the real world....
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Revisiting experience replayable conditions
Experience replay (ER) used in (deep) reinforcement learning is considered to be applicable only to off-policy algorithms. However, there have been...
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Discriminative shapelet learning via temporal clustering and matrix factorization
Identifying discriminative patterns, known as shapelets, within time series is a critical step in many time series classification tasks. A major...
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A novel fusion feature imageization with improved extreme learning machine for network anomaly detection
As the complexity and quantity of network data continue to increase, accurate and efficient anomaly detection methods become critical. Deep...
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Deep reinforcement learning based mapless navigation for industrial AMRs: advancements in generalization via potential risk state augmentation
This article introduces a novel Deep Reinforcement Learning (DRL)-based approach for mapless navigation in Industrial Autonomous Mobile Robots,...
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Context-aware cross feature attentive network for click-through rate predictions
Click-through rate (CTR) prediction aims to estimate the likelihood that a user will interact with an item. It has gained significant attention in...
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Multiobjective optimization-based trajectory planning for laser 3D scanner robots
In our industrial material defect detecting processes, the multi criteria is considered in two-level motion planning structure. Firstly, the feed...
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A real-time human bone fracture detection and classification from multi-modal images using deep learning technique
Human bone is an essential structure that allows the body to move. It is a common observation in contemporary society that bone fractures occur...
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Esophagogastroscopy for predicting endoscopic ultrasonography T-stage by utilizing deep learning methods in esophageal cancer
Endoscopic ultrasonography (EUS) is commonly utilized in preoperative staging of esophageal cancer, however with additional pain and cost as well as...
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Hierarchical contrastive representation for zero shot learning
Zero-shot learning aims to identify unseen (novel) objects, using only labeled samples from seen (base) classes. Existing methods usually learn...
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Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou City
AbstractSpatial and temporal predictions of electric vehicle (EV) charging loads provide a basis for further research on synergistic operation of...
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DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation
Dynamic Graph Neural Network (DGNN) models have been widely used for modelling, prediction and recommendation tasks in domains such as e-commerce and...
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Class feature Sub-space for few-shot classification
Few-shot learning is used in the development of models that can acquire novel class concepts from limited training samples, facilitating rapid...
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Label distribution feature selection based on label-specific features
Label distribution learning, where deal with label ambiguity by describing the degree of relevance of each label to a specific instance. As a novel...
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Better electrobiological markers and a improved automated diagnostic classifier for schizophrenia—based on a new EEG effective information estimation framework
Advances in AI techniques have fueled research on using EEG data for psychiatric disorder diagnosis. Despite EEG’s cost-effectiveness and high...
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Learning improvement of spiking neural networks with dynamic adaptive hyperparameter neurons
Spiking neural networks (SNNs) which use spiking neurons as a component, have shown substantial promise in simulating biological neuron mechanisms...
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Prediction of blast-hole utilization rate using structured nonlinear support vector machine combined with optimization algorithms
Blasting is the primary method for ultra-deep roadway engineering, which is facing the challenge of low footage caused by unsatisfactory blasting...
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Exploring object reduction approaches for optimizing decision-making in linguistic concept formal context
Knowledge reduction is a crucial research topic in formal concept analysis. Given the ability of three-way concept analysis to capture attribute...
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MBGNet:Multi-branch boundary generation network with temporal context aggregation for temporal action detection
Temporal action detection is an important and fundamental video understanding task that aims to locate the temporal regions where human actions or...
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QLDT: adaptive Query Learning for HOI Detection via vision-language knowledge Transfer
Human-object interaction detection can be mainly categorized into two core problems, namely human-object association detection and interaction...
