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International Journal on Artificial Intelligence Tools最新文献

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Metaheuristic Algorithms in Optimal Power Flow Analysis: A Qualitative Systematic Review 最优潮流分析中的元启发式算法:定性系统综述
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-02-23 DOI: 10.1142/s021821302350032x
M. M. Farag, R.A. Alhamad, A. Nassif
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引用次数: 0
An Explainable AI Model for ICU Admission Prediction of COVID-19 Patients 新冠肺炎患者ICU入院预测的可解释人工智能模型
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-02-23 DOI: 10.1142/s0218213023500318
E. Dazea, P. Stefaneas
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引用次数: 0
Deep Learning Approach for Multi-class Semantic Segmentation of UAV Images 无人机图像多类语义分割的深度学习方法
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-02-23 DOI: 10.1142/s0218213023500331
A. Chouhan, D. Chutia, S. Aggarwal
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引用次数: 0
Data Expansion Approach with Attention Mechanism for Learning with Noisy Labels 基于注意机制的带噪声标签学习数据扩展方法
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-02-14 DOI: 10.1142/s0218213023500276
Yuichiro Nomura, Takio Kurita
In recent years, the development of deep learning has contributed to various areas of machine learning. However, deep learning requires a huge amount of data to train the model, and data collection techniques such as web crawling can easily generate incorrect labels. If a training dataset has noisy labels, the generalization performance of deep learning significantly decreases. Some recent works have successfully divided the dataset into samples with clean labels and ones with noisy labels. In light of these studies, we propose a novel data expansion framework to robustly train the models on noisy labels with the attention mechanisms. First, our method trains a deep learning model with the sample selection approach and saves the samples selected as clean at the end of training. The original noisy dataset is then extended with the selected samples and the model is trained on the dataset again. To prevent over-fitting and allow the model to learn different patterns of the selected samples, we leverage the attention mechanism of deep learning to modify the representation of the selected samples. We evaluated our method with synthetic noisy labels on CIFAR-10 and CUB-200-2011 and real-world dataset Clothing1M. Our method obtained comparable results to baseline CNNs and state-of-the-art methods.
近年来,深度学习的发展为机器学习的各个领域做出了贡献。然而,深度学习需要大量的数据来训练模型,而网络爬行等数据收集技术很容易产生错误的标签。如果训练数据集有噪声标签,深度学习的泛化性能会显著下降。最近的一些工作已经成功地将数据集分为带有干净标签的样本和带有噪声标签的样本。针对这些研究,我们提出了一种新的数据扩展框架,利用注意机制对噪声标签上的模型进行鲁棒训练。首先,我们的方法使用样本选择方法训练深度学习模型,并在训练结束时将选择的样本保存为干净的。然后用选择的样本扩展原始噪声数据集,并在数据集上再次训练模型。为了防止过度拟合并允许模型学习所选样本的不同模式,我们利用深度学习的注意机制来修改所选样本的表示。我们用CIFAR-10和CUB-200-2011以及真实数据集Clothing1M上的合成噪声标签来评估我们的方法。我们的方法获得了与基线cnn和最先进方法相当的结果。
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引用次数: 0
Efficient Multimodal Biometric Recognition for Secure Authentication Based on Deep Learning Approach 基于深度学习方法的高效多模态生物特征安全认证
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-02-04 DOI: 10.1142/s0218213023400171
V. Rajasekar, M. Saracevic, M. Hassaballah, D. Karabašević, D. Stanujkić, M. Zajmovic, Umair Ullah Tariq, P. Jayapaul
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引用次数: 3
Alternative to Buy-and-Hold: Predicting Indices Direction and Improving Returns Using a Novel Hybrid LSTM Model 买入和持有的替代方案:使用一种新的混合LSTM模型预测指数方向和提高收益
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-01-18 DOI: 10.1142/s0218213023500288
M. Beniwal, Anuradha Singh, N. Kumar
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引用次数: 0
Improving McDiarmid Tree Performance for Predicting Heart Disease from Data Streams with Missing and Meaningless Values 从具有缺失和无意义值的数据流中改进McDiarmid树预测心脏病的性能
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-01-18 DOI: 10.1142/s021821302350029x
M. Benllarch, S. E. Hadaj, M. Benhaddi
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引用次数: 0
Reservoir Computing for Solving Ordinary Differential Equations 求解常微分方程的油藏计算
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-01-18 DOI: 10.1142/s0218213023500306
M. Mattheakis, H. Joy, P. Protopapas
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引用次数: 0
Text2Color Networks: Deep Learning Models for Color Generation from Compositional Color Descriptions Text2Color网络:从成分颜色描述中生成颜色的深度学习模型
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-01-17 DOI: 10.1142/s0218213023500264
K. Jyothi, M. Okade
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引用次数: 0
Learning from Highly Imbalanced Big Data with Label Noise 从带有标签噪声的高度不平衡大数据中学习
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-01-17 DOI: 10.1142/s0218213023600035
J. M. Johnson, Robert K. L. Kennedy, T. Khoshgoftaar
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引用次数: 0
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International Journal on Artificial Intelligence Tools
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