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Titlebook: Artificial Intelligence and Mobile Services – AIMS 2022; 11th International C Xiuqin Pan,Ting Jin,Liang-Jie Zhang Conference proceedings 20

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發(fā)表于 2025-3-26 23:42:44 | 只看該作者
32#
發(fā)表于 2025-3-27 04:09:56 | 只看該作者
SATMeas - Object Detection and Measurement: Canny Edge Detection Algorithmedge outline. This is done after edge detection and closing any gaps between edges. We determine pixels per metric variable by relying on a reference object. The Euclidean distance between sets of center points was then determined to get the calculations. Putting it all together, we developed an app
33#
發(fā)表于 2025-3-27 08:04:44 | 只看該作者
Multi-Classification of Electric Power Metadata based on Prompt-tuningediction task on the unlabeled text dataset of the power industry to enable the pre-training model to acquire new vocabulary and knowledge of the industry; 2. The prompt-tuning model uses the continuous depth prompt technology as the backbone, which helps to bring the pre-training model closer to th
34#
發(fā)表于 2025-3-27 11:39:57 | 只看該作者
Dual-Branch Network Fused with Attention Mechanism for Clothes-Changing Person Re-identificationular clothes-changing person re-identification dataset PRCC, and the experimental results show that the method in this paper is more advanced than popular methods. This paper also conducts experiments on LaST, an ultra-large-scale cross-space-time dataset, and also achieves competitive result result
35#
發(fā)表于 2025-3-27 15:36:43 | 只看該作者
Infant Cry Classification Based-On Feature Fusion and Mel-Spectrogram Decomposition with CNNsssification error rate compared with the result using single mel-spectrogram images with CNN model on Baby Chillanto database and our testing accuracy reaches 99.26%, which outperforms all other methods with this five-category classification task. The gender classification experiment on Baby2020 dat
36#
發(fā)表于 2025-3-27 19:05:36 | 只看該作者
37#
發(fā)表于 2025-3-28 00:51:53 | 只看該作者
Gesetz über elektronische Wertpapiere (eWpG) a long time. Based on this, we propose a Dual-channel Recurrent Neural Network with Xgboost (DCRNNX) to solve emotion recognition using nonspeech vocalizations. The DCRNNX mainly combines two Backbone models. The first model is a two-channel neural network model based on the Deep Neural Network (DN
38#
發(fā)表于 2025-3-28 05:27:12 | 只看該作者
39#
發(fā)表于 2025-3-28 06:17:49 | 只看該作者
Christian Conreder,Johannes Meierstomers and employers to quickly gain knowledge of them and provides a potential corpus for question-answering robots. While previous work focuses on web texts (e.g., Wiki), we generate FAQ pairs from the formal and verbose rule and regulation documents, which is significant in real scenarios. To ta
40#
發(fā)表于 2025-3-28 13:48:46 | 只看該作者
Christian Conreder,Johannes Meierormation have achieved state-of-the-art performance in most downstream natural language processing (NLP) tasks, including named entity recognition (NER), English text classification and sentiment analysis. For Chinese text classification, the existing methods have also tried such kinds of models. Ho
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