MoNetViT: an efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention by Liliek Triyono, Liliek Triyono, Rahmat Gernowo, Prayitno

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Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model by Abdelmajid Bousselham, Omar Bouattane, Mohamed Youssfi, Abdelhadi Raihani

“…A dataset of 3192 images was created to train U-Net using k-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. …”
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An enhanced CNN with ResNet50 and LSTM deep learning forecasting model for climate change decision making by Ahmed M. Elshewey, Mona M. Jamjoom, Eman H. Alkhammash

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VMC-Net: multi-scale feature aggregation and distribution with contextual attention guided fusion for aerial object detection by Haodong Li, Haicheng Qu

“…Existing methods usually use multi-scale feature fusion or attention mechanism to improve performance, but they often ignore the role of object feature perception in the image and have problems such as insufficient use of context information. To address these problems, we propose the VMC-Net framework to optimize the aerial image object detection task. …”
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Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans by Amr Elsawy, PhD, Tiarnan D.L. Keenan, PhD, MD, Alisa T. Thavikulwat, MD, Amy Lu, MD, Sunil Bellur, MD, Souvick Mukherjee, PhD, Elvira Agron, MS, Qingyu Chen, PhD, Emily Y. Chew, MD, Zhiyong Lu, PhD

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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

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Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers by Umar Islam, Yasser A. Ali, Muna Al-Razgan, Hanif Ullah, Mohmmed Amin Almaiah, Zeeshan Tariq, Khalid Mohammad Wazir

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GHMSA-Net: Gated Hierarchical Multi-Scale Self-Attention for Perceptually-Guided AV1 Post-Processing by Bopu Zhao, Woowoen Gwun, Kiho Choi

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SSC-Net: A multi-task joint learning network for tongue image segmentation and multi-label classification by Xiaopeng Sha, Zheng Guan, Ying Wang, Jinglu Han, Yi Wang, Zhaojun Chen

“…However, most learning-based methods often address a narrow scope of the tongue’s attributes, failing to fully exploit the information contained within the tongue images. Objective To classify multifaceted tongue characteristics, and fully utilize the latent correlation information between tongue segmentation and classification tasks, we proposed a multi-task joint learning network for simultaneous tongue body segmentation and multi-label Classification, named SSC-Net. …”
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CCD-Net: Color-Correction Network Based on Dual-Branch Fusion of Different Color Spaces for Image Dehazing by Dongyu Chen, Haitao Zhao

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A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets by Hyungwoo Choi, Taehwa Kim, Hong-Shik Park, Jun Kyun Choi

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A 3D Dual Encoder Mirror Difference ResU-Net for Multimodal Brain Tumor Segmentation by Qiwei Xing, Zhihua Li, Yongxia Jing, Xiaolin Chen

“…Additionally, residual blocks are employed to substitute the original convolutional blocks in the encoder section of the U-Net structure. This modification not only streamlines network training but also mitigates issues related to network degradation and the loss of detailed information. …”
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NRGS-Net: A Lightweight Uformer with Gated Positional and Local Context Attention for Nighttime Road Glare Suppression by Ruoyu Yang, Huaixin Chen, Sijie Luo, Zhixi Wang

“…To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. …”
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Advanced gastrointestinal tract organ differentiation using an integrated swin transformer U-Net model for cancer care by Neha Sharma, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman

“…This study presents an innovative semantic segmentation approach that integrates the Swin Transformer Block with the U-Net model to delineate healthy GI organs accurately using MRI data. …”
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Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification by Mohammed Al-Shalabi, Mohammed A. Mahdi, Malik Braik, Mohammed Azmi Al-Betar, Shahanawaj Ahamad, Sawsan A. Saad

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EFFResNet-ViT: A Fusion-Based Convolutional and Vision Transformer Model for Explainable Medical Image Classification by Tahir Hussain, Hayaru Shouno, Abid Hussain, Dostdar Hussain, Muhammad Ismail, Tatheer Hussain Mir, Fang Rong Hsu, Taukir Alam, Shabnur Anonna Akhy

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ENERGY CONSUMPTION MODEL OF WIRELESS SENSOR NET NODES AND ITS APPLICATION FOR INCREASING NETWORK OFFLINE OPERATION TIME by Mohammed Neamah Mohsen, Igor Vladimirovich Boguslavskiy

“…A proposed new model of the wireless sensor net can be applied for the network optimized energy efficiency. …”
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TSFF-Net: A deep fake video detection model based on two-stream feature domain fusion. by Hangchuan Zhang, Caiping Hu, Shiyu Min, Hui Sui, Guola Zhou

“…The code used in this study is available at: https://github.com/hwZHc/TSFF-Net.git.…”
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A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition by Yuanyuan Tian, Sen Lin, Hejun Xu, Guangchong Chen

“…Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. …”
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Research on the Method of Crop Pest and Disease Recognition Based on the Improved YOLOv7-U-Net Combined Network by Wenchao Xiang, Zitao Du, Xinran Liu, Zehui Lu, Yuna Yin

“…This paper proposes an improved YOLOv7-U-Net combined network for crop pest and disease recognition, aiming to address the issue of insufficient accuracy in existing methods. …”
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