„Computer Science - Applied“
Suchergebnisse
10.000+ Treffer
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Type-2 Trapezoidal Pythagorean fuzzy number with novel entropy measure and aggregation operators extended to MCDM
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A novel decision-making approach using three-way decision in fractional fuzzy environment
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MWA-Net: multi-scale wavelet-guided attention network for single image dehazing
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Enhancing multimodal fault diagnosis in mechanical systems via mixture of experts
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From micro to macro: multi-scale causal emergent complexity analysis in traffic dynamics
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Defending deep learning models: a hybrid algorithm employing de-noising and coordinate-disruption techniques against adversarial attacks
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Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference
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HRL-MOEA: a hybrid reinforcement learning-enhanced multi-objective recommendation algorithm with dynamic policy orchestration
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A novel federated learning approach for IoT botnet intrusion detection using SHAP-based knowledge distillation
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Named entity recognition for chinese scientific literature based on adversarial training and dual-masked global pointer
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DBSQFusion: a multimodal image fusion method based on dual-channel attention
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks
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A multiobjective evolutionary algorithm incorporating neighborhood detection for the vehicle routing problem with soft time windows
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Motion planning for hyper-redundant manipulator systems: combining path following and improved APF-RRT* Path finding
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Bootstrapping OTS-Funcimg pre-training model (Botfip): a comprehensive multimodal scientific computing framework and its application in symbolic regression task
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Forecasting blink behavior during VR experiences via a multi-scale multi-modal fusion approach
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Cross-modal knowledge distillation for enhanced depression detection
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Robot movement based inference of teleoperator state for multi-robot search
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A novel mechanism-guided residual network for accurate modelling of scroll expander under noisy and sparse data conditions
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JDA-attack: leveraging joint multimodal data augmentation to enhance adversarial transferability of vision-language pre-training models