MFI-Net: multi-level feature invertible network image concealment technique by Dapeng Cheng, Minghui Zhu, Bo Yang, Xiaolian Gao, Wanting Jing, Yanyan Mao, Feng Zhao

“…In this article, we propose a novel image hiding method based on invertible networks, called MFI-Net. The method introduces a new upsampling convolution block (UCB) and combines it with a residual dense block that employs the parametric rectified linear unit (PReLU) activation function, effectively utilizing multi-level information (low-level and high-level features) of the image. …”
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CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network by Jinglin Zhang, Pu Liu, Feng Zhang, Qianqian Song

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Distance Based Korean WordNet(alias. KorLex) Embedding Model by SeongReol Park, JoongMin Shin, Sanghyun Cho, Hyuk-Chul Kwon, Jung-Hun Lee

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MRCS-Net: Multi-Radar Clustering Segmentation Networks for Full-Pulse Sequences by Tao Chen, Yu Lei, Limin Guo, Boyi Yang

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MPCNet: Improved MeshSegNet Based on Position Encoding and Channel Attention by Hanqing Hu, Zhengxun Li, Weichao Gao

“…In this paper, we propose a novel end-to-end tooth segmentation method, MPCNet, which adds multi-scale mesh density information to the input layer, uses position encoding and channel attention mechanism to improve MeshSegNet, and uses graph-cut post-processing to perform 3D tooth segmentation in real scenes. …”
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ICL-Net: Inverse Cognitive Learning Network for Remote Sensing Image Dehazing by Weida Dong, Chunyan Wang, Xiping Xu

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A Dual-Branch U-Net for Staple Crop Classification in Complex Scenes by Jiajin Zhang, Lifang Zhao, Hua Yang

“…In our method, the corn and rice accuracies are 89.72% and 88.61%, and the mean intersection over union (mIoU) is 85.61%, which is higher than the compared models (U-Net, SegNet, and DeepLabv3+). Our method provides a novel solution for the classification of staple crops in complex scenes using high-resolution images, which can help to obtain accurate information on staple crops in larger regions in the future.…”
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Identification Method of Mature Wheat Varieties Based on Improved DenseNet Model by Zihang Liu, Yuting Zhang, Guifa Teng

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NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithms by Sašo Pavlič, Iztok Fister Jr., Sašo Karakatič

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Automatic Annotation of Map Point Features Based on Deep Learning ResNet Models by Yaolin Zhang, Zhiwen Qin, Jingsong Ma, Qian Zhang, Xiaolong Wang

“…Simultaneously, it has significantly enhanced the map’s esthetic appeal and the information’s clarity.…”
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FisherNet: AI-Driven Socio-Economic and Market Prediction for the Dry Fish Industry by Md Masud Rana, Mohammad Bodrul Munir, Shakik Mahmud

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Improving Real-Time Performance of U-Nets for Machine Vision in Laser Process Control by Przemysław Dolata, Jacek Reiner

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Actual evapotranspiration dominated net primary productivity loss caused by concurrent droughts by Zice Ma, Donghua Chen, Peng Sun, Yufeng Liu, Lizao Ye

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EFM-ResNet: A Feature Enhanced Network for Tobacco Strips Classification by Bo Zhang, Shanshan Zheng, Leyuan Han, Yucan Qiu

“…In this paper, we propose a tobacco strip image classification model based on multi-scale fusion attention mechanism and feature enhancement improved ResNet, named EFM-ResNet, to address the issues of detail information loss during feature extraction and the difficulty in capturing long-range dependencies. …”
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IS THE PATIENT PARTNERSHIP INFORMATIVE IN PHARMACOVIGILANCE? by Yu. V. Muravyev

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DAM-Net: Domain Adaptation Network With Microlabeled Fine-Tuning for Change Detection by Hongjia Chen, Xin Xu, Fangling Pu

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ResSAXU-Net for multimodal brain tumor segmentation from brain MRI by Zheyuan Xiong

“…The enhanced version of Ressaxu-Net presented in this work has two new features: Ressaxu-Net improves feature information extraction and solves brain tumour classification problems by using deep residual networks to reduce network damage. …”
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SceEmoNet: A Sentiment Analysis Model with Scene Construction Capability by Yi Liang, Dongfang Han, Zhenzhen He, Bo Kong, Shuanglin Wen

“…To address this issue, we propose the SceEmoNet model. This model endows text classification models with imagination through Stable diffusion, enabling the model to generate corresponding visual scenes from input text, thus introducing a new modality of visual information. …”
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STEAM in Information Technology Education by Daniela Kozhuharova

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Dual U-Net with multi-task attention for automated eyelid curvature quantification by Jimei Wu, Yang Yang, Cheng Wan, Cheng Wan, Meina Yang, Weihua Yang, Wei Chi

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