Recurrent Context Window Networks for Italian Named Entity Recognizer by Daniele Bonadiman, Alessandro Moschitti, Aliaksei Severyn

“…These choices make our network simple and computationally efficient. Unlike previous best NERs for Italian, our model does not require manual-designed features, external parsers or additional resources. …”
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Channel computation based on multi-scale attention residual network by Wengang Li, Deli Zhou, Qiong Ye

“…Firstly, a multi-scale channel feature extraction module is designed, which applies multi-dimensional feature extraction to the channel matrix, thereby bolstering the capability to capture features at diverse scales. …”
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A Two-Level Routing Scheme for Wireless Sensor Network by Jinglun Shi, Zhilong Shan, Xuxun Liu

“…A new two-level routing scheme is proposed using the energy features of wireless sensor network. The proposed scheme consists of two levels: (i) sink node level and (ii) sensor node level. …”
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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

“…To address these limitations, we propose an adaptive spatiotemporal dynamic graph convolutional network (AST-DGCN) for traffic prediction. Under the encoder-decoder architecture, the proposed model leverages node embedding techniques to extract high-dimensional features, generating time-evolving adaptive graphs through self-attention mechanisms. …”
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Survey of QoS Routing Protocols in Wireless Multimedia Sensor Networks by Shailendra Aswale, Vijay R. Ghorpade

“…The emergence of Wireless Multimedia Sensor Networks (WMSNs) has stimulated the refocusing of research from conventional scalar Wireless Sensor Networks (WSNs) to WMSNs. …”
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Identification of the financial potential of the franchised tourist network: gnoseological discourse by S.V. Bardash, О.М. Petruk

“…It is determined that today the research is being done on the concept of «financial potential», the search for methods of its evaluation, as well as the disclosure of management features. …”
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Improved generative adversarial networks model for movie dance generation. by Zhiqun Lin, Kexin Feng

“…To address the challenges of innovation and efficiency in film choreography, this study proposes a dance generation model based on the generative adversarial networks. The model is trained using the AIST++ dance motion dataset, incorporating data from multiple dance styles to ensure that the generated dance sequences accurately mimic various stylistic and technical characteristics. …”
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Dysarthria Severity detection Using Recurrent and Convolutional Neural Networks by Amina Hamza, Djamel Addou

“…Using neural network models, specifically recurrent neural networks (RNN) and convolutional neural networks (CNN), this paper offers a thorough investigation of how to distinguish dysarthric voices among a collection of normal voice samples and categorize the severity of dysarthria. …”
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THANTD: Triplet Hybrid Attention Network for Hyperspectral Target Detection by Ju Liu, Haoqian Wang, Xiangai Cheng, Zhongyang Xing, Zhongjie Xu

“…To address these challenges, we propose a triplet hybrid attention network for HTD (THANTD). It features a backbone with three parallel, weight-sharing hybrid attention blocks that integrate the multihead self-attention mechanism and the channel attention module. …”
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Lightweight Siamese Network with Global Correlation for Single-Object Tracking by Yuxuan Ding, Kehua Miao

“…This study presents a novel lightweight Siamese network tracker, termed SiamGCN, which incorporates global feature fusion alongside a lightweight network architecture to improve tracking performance on devices with limited resources. …”
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Application of improved graph convolutional network for cortical surface parcellation by Jia Tan, Xiaomei Ren, Yong Chen, Xianju Yuan, Feiba Chang, Rui Yang, Chengqun Ma, Xiaoyu Chen, Miao Tian, Wei Chen, Zihong Wang

“…ADGCN consists of a deep graph convolutional layer with a symmetrical U-shaped structure, which enables it to effectively transmit detailed information of the original brain map and learn the complex graph structure, help the network enhance feature extraction capability. What’s more, we introduce the Squeeze and Excitation (SE) module, which enables the network to better capture key features, suppress unimportant features, and significantly improve parcellation performance with a small amount of computation. …”
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DMCM: Dwo-branch multilevel feature fusion with cross-attention mechanism for infrared and visible image fusion. by Xicheng Sun, Fu Lv, Yongan Feng, Xu Zhang

“…In response to the limitations of current infrared and visible light image fusion algorithms-namely insufficient feature extraction, loss of detailed texture information, underutilization of differential and shared information, and the high number of model parameters-this paper proposes a novel multi-scale infrared and visible image fusion method with two-branch feature interaction. …”
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Cancelable speaker identification system for the handicapped access to healthcare networks by Samia A. El-Moneim Kabel, Walid El-Shafai, Naglaa F. Soliman, Reem Alkanhel, Fathi E. Abd El-Samie

“…While these results demonstrate the potential effectiveness of the framework for securing networks through cancelable biometrics and fast processing time, the study does not include an evaluation of computational complexity and scalability in resource-constrained IoT environments. …”
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Autonomous QoS Management and Policing in Unmanaged Local Area Networks by Christopher Köhnen, Christian Überall, Muttukrishnan Rajarajan, Rudolf Jäger, Veselin Rakočević

“…This new QoS framework enables link based, access-medium independent bandwidth management without network support. We provide evaluation results for the novel bandwidth prediction algorithm as well as for the QoSiLAN framework and its protocol, which highlight the features, robustness, and the effectiveness of the proposed system.…”
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Hybrid attentive prototypical network for few-shot action recognition by Zanxi Ruan, Yingmei Wei, Yanming Guo, Yuxiang Xie

“…Our framework utilizes the R(2+1)D backbone network, coupling the extraction of integrated temporal and spatial features to ensure a comprehensive understanding of video content. …”
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Combining handcrafted and learned features using deep learning to improve protein-protein interaction prediction performance by Tran Hoai Nhan, Nguyen Phuc Xuan Quynh, Le Anh Phuong

“…Additionally, the generalizability of our model is evaluated through independent tests, and our findings indicate that the proposed feature combination method correctly classifies experimentally validated interactions in eight datasets, including cross-species and PPI networks. …”
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Product Innovation Design Method Based on BP Neural Network by Shihui Huang

“…Convolutional neural network and neural style transfer model are used to extract, reconstruct, and integrate the color features of style diagrams, and transfer them to product modeling to design new product images. …”
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Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area by Xiang Li, Fa Zhang, Ziyi Liu, Yao Wei, Runlong Dai, Zhiyue Qiu, Yuxin Gu, Hong Yuan

“…This research develops a multimodal feature extraction and evaluation framework specifically designed for the large-scale analysis of HSR station areas. …”
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Dispositional optimism and social networks: A systematic literature review by Amănălăchioaie Andreea, Diaconu-Gherasim Loredana R., Curseu Petru L.

“…This review advances the previous literature regarding how personality traits are related to the features of social networks by investigating how dispositional optimism is linked with the structure of personal social networks. …”
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Intracranial Aneurysm Segmentation with a Dual-Path Fusion Network by Ke Wang, Yong Zhang, Bin Fang

“…The network also integrates a detail aggregation module for effective fusion of multi-scale detail features. …”
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