data annotation is an important milestone in a variety of domains where Machine Learning (ML) approaches are applied. Although automated and semi-automated methods for data labeling exist, in certain scenarios manual ...
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The empirical loss, commonly referred to as the average loss, is extensively utilized for training machine learning models. However, in order to address the diverse performance requirements of machine learning models,...
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Fairness is a critical aspect of Artificial Intelligence (AI) techniques. The ability of multimodal AI systems for scene understanding and visual reasoning to make unbiased decisions is crucial for their acceptance an...
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This paper studies the default assessment strategy of individual users of online loans based on artificial intelligence and data mining technology, with the aim of discovering high-default groups and reducing lending ...
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The computational paradigm of a graph neural network (GNN) can be abstracted as a computation graph (CG). Directly constructing CGs for large-scale graphs is computationally expensive and memory-intensive. Consequentl...
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The saturation effects, which originally refer to the fact that kernel ridge regression (KRR) fails to achieve the information-theoretical lower bound when the regression function is over-smooth, have been observed fo...
Over the last few years, use of credit card for online purchase has increased exponentially, which resulted in increase in fraud related to it. It is very difficult to detect fraudulent transactions from the banking t...
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This work focuses on applying Gas Dynamic Analogous Exposure (GDAE) to assess exposure levels in traffic areas. The original GDAE method faced challenges in obtaining accurate vehicle collision angle parameters. To ad...
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By employing neural networks (NN) to learn input-solution mappings and passing a new input through the learned mapping to obtain a solution instantly, recent studies have shown remarkable speed improvements over itera...
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The challenge of detecting malware in cybersecurity necessitates the exploration of diverse machine learning models for effective solutions. In this investigation, we assess the performance of several models, includin...
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