Enhancing TextGCN for depression detection on social media with emotion representation by Huimin Mao, Qing Han

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Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections by Asadulla Y. Ashurov, Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Mohammed Saleh Ali Muthanna

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Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy by Chunlin Zhao, Zhipeng Yin, Yushuo Tan, Wenbin Zhang, Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu, Quan Lu

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Hyperspectral Image Classification Based on Two-Branch Feature Fusion Network by Qiongdan Huang, Liang Li, Mengyang Zhao, Jiapeng Wang, Shilin Kang

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Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism by Yahong Ma, Zhentao Huang, Yuyao Yang, Shanwen Zhang, Qi Dong, Rongrong Wang, Liangliang Hu

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Advanced Brain Tumor Segmentation With a Multiscale CNN and Conditional Random Fields by Ala Guennich, Mohamed Othmani, Hela Ltifi

“…In this study, we present a novel 9-layer multiscale architecture designed specifically for the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution incorporates several significant enhancements, including the use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer. …”
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Multi-modal expert system for automated durian ripeness classification using deep learning by Santi Sukkasem, Watchareewan Jitsakul, Phayung Meesad

“…We present a multi-modal approach that integrates Convolutional Neural Networks (CNNs) for image-based classification and Recurrent Neural Networks (RNNs) for automatic textual descriptions. …”
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R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals by Kamirul Kamirul, Odysseas A. Pappas, Alin M. Achim

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Tillage and biomass detection for estimating winter-time cropland management practices with satellite remote sensing by Maria Yli-Heikkilä, Arto Klami, Samantha Wittke, Markku Luotamo, Pinja Mero, Petri Pellikka, Janne Heiskanen, Mwaba Hiltunen, Kari Luojus, Golda Prakasam, Mikko Strahlendorff, Markus Törmä, Mi Sulkava

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Hyperspectral imaging-driven deep learning approach: Asymptomatic stage detection and severity grading of tomato yellow leaf curl disease by Junzhi Chen, Wenshan Zhong, Xuejun Yue, Ziyu Ding, Mengdan Du, Xuantian Li, Biao Chen, Haifeng Li, ZiFu He, Xiaoman She, Yafei Tang

“…Furthermore, ablation studies validated the contributions of the key components of SSDBRN, namely the spectral channel attention module and the deformable convolution module, while generalization capability studies confirmed the model's robustness to complex backgrounds and lighting variations. …”
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Computational methods and technical means of processing signals of side electromagnetic emanation by Danil A. Shinyaev, Leonid N. Kessarinskiy, Egor A. Simakhin

“…The aim of this work is to develop a method to improve the quality of the reconstructed image based on the signals of side electromagnetic fields using post-image processing. To do this, the paper considers the problem of analyzing the side electromagnetic radiation from video displays and ways to solve it. …”
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More Accurate Constraints for Self-Supervised Learning in Remote Sensing Images-Based Object Detection by Shangdong Zheng, Zebin Wu, Yang Xu, Qian Liu, Zhihui Wei

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RiceLeafClassifier‐v1.0: A Quantized Deep Learning Model for Automated Rice Leaf Disease Detection and Edge Deployment by Oluwaseun O. Martins, Christiaan C. Oosthuizen, Dawood A. Desai

“…This study presents RiceLeafClassifier‐v1.0, a lightweight quantized convolutional neural network (CNN) that classifies five rice leaf conditions: blast, bacterial blight, brown spot, healthy, and red stripe, with high accuracy and real‐time performance. …”
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Semantic air segmentation in core with noise reduction and post-processing by Mikhail I. Volkov, Polina S. Kargina

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COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization by Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi, Jack Xin, Xin Li, Penghang Yin

“…Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. …”
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A deep learning model for prediction of lysine crotonylation sites by fusing multi-features based on multi-head self-attention mechanism by Yunyun Liang, Minwei Li

“…Abstract Lysine crotonylation (Kcr) is an important post-translational modification, which is present in both histone and non-histone proteins, and plays a key role in a variety of biological processes such as metabolism and cell differentiation. …”
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A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification by Xin Deng, Huaxiang Huo, Lijiao Ai, Daijiang Xu, Chenhui Li

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A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model by QIU Xinhua, WANG Wenkun, JI Yuwen, LI Jia

“…Therefore, an image recognition method based on YOLOv3 was proposed, which could still ensure good recognition accuracy in the face of different illumination, complex background and different forms of image. Firstly, a convolutional neural network is used to learn image features and obtain network parameters of each layer. …”
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Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks by Hang Wei, Zulin Wang, Yuanhan Ni

“…Convolutional neural network (CNN)-based synthetic aperture radar (SAR) ship detection models operating directly on satellites can reduce transmission latency and improve real-time surveillance capabilities. …”
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Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques by Marwa K. Elteir

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