Titlebook: Artificial Intelligence XL; 43rd SGAI Internatio Max Bramer,Frederic Stahl Conference proceedings 2023 The Editor(s) (if applicable) and Th

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書(shū)目名稱Artificial Intelligence XL影響因子(影響力)




書(shū)目名稱Artificial Intelligence XL影響因子(影響力)學(xué)科排名




書(shū)目名稱Artificial Intelligence XL網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Artificial Intelligence XL網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Artificial Intelligence XL被引頻次




書(shū)目名稱Artificial Intelligence XL被引頻次學(xué)科排名




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書(shū)目名稱Artificial Intelligence XL年度引用學(xué)科排名




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書(shū)目名稱Artificial Intelligence XL讀者反饋學(xué)科排名

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Exploring Multilingual Word Embedding Alignments in?BERT Models: A Case Study of?English and?Norwegialysis also shows that embedding a word encodes information about the language to which it belongs. We, therefore, believe that in pre-trained multilingual models’ knowledge from one language can be transferred to another without direct supervision and help solve the data sparsity problem for minor

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Deep Despeckling of?SAR Images to?Improve Change Detection Performancesed method demonstrate superior performance compared to state-of-the-art methods such as DDNet and LANTNet performance. Our method significantly increased the change detection accuracy from a baseline of 86.65% up to 90.79% for DDNet and from 87.16% to 91.1% for LANTNet in the Yellow River dataset.

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Profiling Power Consumption for?Deep Learning on?Resource Limited Devicesa common approach to facilitate such deployments. This paper investigates the power consumption behaviour of CNN models from the DenseNet, EfficientNet, MobileNet, ResNet, ConvNeXt & RegNet architecture families, processing imagery on board a Nvidia Jetson Orin Nano platform. It was found that energ

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