A deep classification model to assess environment following hazards using remote sensing images by Madhusmita Sahu, Rasmita Dash

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StrawberryNet: Fast and Precise Recognition of Strawberry Disease Based on Channel and Spatial Information Reconstruction by Xiang Li, Lin Jiao, Kang Liu, Qihuang Liu, Ziyan Wang

“…First, to decrease the number of parameters, instead of standard convolution, a partial convolution is selected to construct the backbone for extracting the features of strawberry disease, which can significantly improve efficiency. …”
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DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis by Zeeshan Ahmad, Shudi Bao, Meng Chen

“…For evaluation, we use five different datasets, covering both speech synthesis and music generation tasks, to demonstrate the efficiency of the DPN-GAN. …”
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A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction by Jiaxin Huang, Huicong Wang, Yuhan Li, Shijian Liu

“…Furthermore, deformable convolution is utilized to fuse features extracted from different branches, enabling adaptation to targets of various shapes. …”
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The potential role of synthetic computed tomography in spinal surgery: generation, applications, and implications for future clinical practice by Shreya Sankar, Jake Michael McDonnell, Stacey Darwish, Joseph Simon Butler

“…The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. …”
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Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM by Vladyslav Yavtukhovskyi, Violeta Tretynyk

“…Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. …”
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Fault diagnosis method for rigid guides in vertical shaft hoisting systems by WANG Jianfeng, JIN Yuanzhi, ZHANG Yong, WANG Yongzhen, HE Jiacong

“…The network extracted multi-scale features through parallel multi-scale convolutions, enhancing its ability to perceive signal features at different scales. …”
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Vegetation classification in a subtropical region with Sentinel-2 time series data and deep learning by Ming Zhang, Dengqiu Li, Guiying Li, Dengsheng Lu

“…Conv1D model based on one-dimensional convolution, GoogLeNet model based on two-dimensional convolution, and CGNet model which fused Conv1D and GoogLeNet) for vegetation classification, respectively. …”
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Consistency regularization based semi-supervised plant disease recognition by Murat Ilsever, Ipek Baz

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A Multi-Architecture Approach for Offensive Language Identification Combining Classical Natural Language Processing and BERT-Variant Models by Ashok Yadav, Farrukh Aslam Khan, Vrijendra Singh

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Comparative Study of Deep Learning-Based Sentiment Classification by Seungwan Seo, Czangyeob Kim, Haedong Kim, Kyounghyun Mo, Pilsung Kang

“…Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives.…”
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Application of big data and artificial intelligence in visual communication art design by Ailing Zhang

“…This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. …”
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Dual-stream dynamic graph structure network for document-level relation extraction by Yu Zhong, Bo Shen

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Assessment of Deep Neural Network Models for Direct and Recursive Multi-Step Prediction of PM10 in Southern Spain by Javier Gómez-Gómez, Eduardo Gutiérrez de Ravé, Francisco J. Jiménez-Hornero

“…The models were also assessed here for recursive multi-step prediction over different forecast periods in three different situations: background concentration, a strong dust event, and an extreme dust event. …”
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Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN by Burak Duran, Saeed Eftekhar Azam, Masoud Sanayei

“…Finite element models of bridge-type structures with varying geometry were simulated using the OpenSeesPy platform. Different levels of damage states were introduced at the midspans of these models, and Gaussian-based load time histories were applied at mid-span for dynamic time-history analysis to calculate acceleration data. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.…”
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An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory by Yanbo Sun, Wenxing Bao, Wei Feng, Kewen Qu, Xuan Ma, Xiaowu Zhang

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A novel network with enhanced edge information for left atrium segmentation from LGE-MRI by Ze Zhang, Zhen Wang, Xiqian Wang, Kuanquan Wang, Yongfeng Yuan, Qince Li

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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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Multi-S3P: Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model by M. M. Mohamed Mufassirin, M. A. Hakim Newton, Julia Rahman, Abdul Sattar

“…Also, predicting secondary structures in the boundary regions between different types of SS is challenging. This study presents Multi-S3P, which employs bidirectional Long-Short-Term-Memory (BILSTM) and Convolutional Neural Networks (CNN) with a self-attention mechanism to improve the secondary structure prediction using an effective training strategy to capture the unique characteristics of each type of secondary structure and combine them more effectively. …”
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