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Prediction of servo industry development in China by an optimized reverse Hausdorff fractional discrete grey power model
In order to accurately predict the development of the servo industry in China, this study proposes a Hausdorff fractional reverse accumulated grey...
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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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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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Exploring Brazilian Teachers’ Perceptions and a priori Needs to Design Smart Classrooms
Smart classrooms offer innovative opportunities to enhance teaching and learning. However, most existing research in this field predominantly focuses...
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Jetting-based bioprinting: process, dispense physics, and applications
Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial...
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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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Clinical characteristics and impact of pseudo-lumen blood flow on long-term vessel dilatation in spontaneous isolated dissection of superior mesenteric/celiac artery
This study aimed to identify the clinical characteristics associated with spontaneous isolated dissection of superior mesenteric artery/celiac artery...
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Convective instability analysis of the flow due to a linearly shrinking sheet with uniform suction
We explore the linear stability of a two-dimensional flow induced due to streamwise linear shrinking of a flat surface. Dual solutions for mean flow...
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PET Images Enhancement Using Deep Training of Reconstructed Images with Bayesian Penalized Likelihood Algorithm
PurposeTo adopt the merits of the Bayesian Penalized Likelihood (BPL) reconstruction algorithm (incl. improved contrast recovery), a deep learning...
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L2XGNN: learning to explain graph neural networks
Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a...
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Vector-based uncertain ordered density weighted averaging: a family of incentive-oriented aggregation operators
Incentive is a common phenomenon in the process of decision management. It is important and necessary to integrate incentive requirement into the...
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Optimal analysis and design of large-scale problems using a Modified Adolescent Identity Search Algorithm
This research uses a new method called the Modified Adolescent Identity Search Algorithm (MAISA) to solve optimization problems. The identity of a...
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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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A crosstalk-free dual-mode sweat sensing system for naked-eye sweat loss quantification via changes in structural reflectance
Sweat loss monitoring is important for understanding the body’s thermoregulation and hydration status, as well as for comprehensive sweat analysis....
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Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework
Noninvasive, accurate, and simultaneous grading of liver fibrosis, inflammation, and steatosis is valuable for reversing the progression and...
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A multiobjective multiperiod portfolio selection approach with different investor attitudes under an uncertain environment
Though there are several studies on uncertain single-period portfolio selection, the uncertain multiperiod portfolio selection literature is still in...
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Temporal analysis of computational economics: a topic modeling approach
This study offers a comprehensive investigation into the thematic evolution within computational economics over the past two decades, leveraging...
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Snn and sound: a comprehensive review of spiking neural networks in sound
The rapid advancement of AI and machine learning has significantly enhanced sound and acoustic recognition technologies, moving beyond traditional...