MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…Second, the global–local Transformer block (GLTB) decoder uses multi-head self-attention mechanisms to dynamically fuse multi-scale features across layers, effectively restoring the topological structure of fragmented farmland boundaries. …”
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DASNet a dual branch multi level attention sheep counting network by Yini Chen, Ronghua Gao, Qifeng Li, Hongtao Zhao, Rong Wang, Luyu Ding, Xuwen Li

“…DASNet is built on a modified VGG–19 architecture, where a dual-branch structure is employed to integrate both shallow and deep features. …”
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A Medical Image Semantics Segmentation Method Based on Image Pre-processing and Image Transformer by Li Zhaopeng

“…Existing models in this field pay more attention to editing U-Net’s structure and convolutional layers. In this paper, an image pre-processing technique to enrich the potential of images and an image transformer involved in network TrUNet are proposed to tackle these issues. …”
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Financial accounting management strategy based on business intelligence technology for sustainable development strategy by Jianben Feng

“…The model firstly transforms the corporate financial data into graph structure, and extracts the features of complex financial relationships through graph convolutional neural network, and at the same time combines with the dynamic time regularization method to enhance the adaptability to the dynamic change of time. …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…Specifically, the Context-Aware Module (CAM) employs a multi-branch dilated convolutional structure to enhance feature richness and expand the local receptive field, thereby strengthening the feature extraction capability. …”
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A deep fusion‐based vision transformer for breast cancer classification by Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza

“…Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception‐V1, and VGG‐16, while useful in many applications, cannot capture the patterns of cell layers and staining properties. …”
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Real-time diagnosis of multi-category skin diseases based on IR-VGG by Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA

“…Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.…”
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Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents by Yipeng Zhou, Jiaxin Fan, Qingchuan Zhang, Lin Zhu, Xingchen Sun

“…By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. …”
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Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning by Teemu Koivisto, Marcin Mińkowski, Lasse Laurson

“…We first train a convolutional neural network using data from molecular dynamics simulations to learn the mapping from the sample-specific initial atomic structure to features of the stress–strain curve. …”
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Bearing fault diagnosis based on improved DenseNet for chemical equipment by Wu Huiyong, Jiang Kuan, Wang Yanyu

“…The alternating stacking strategy of channel and spatial attention further improves the feature extraction ability at different scales. This optimized structure increases the diversity and discriminative power of feature representations, enhancing the model’s performance in fault diagnosis tasks. …”
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MFMamba: A Mamba-Based Multi-Modal Fusion Network for Semantic Segmentation of Remote Sensing Images by Yan Wang, Li Cao, He Deng

“…Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). …”
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Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy by Renyuan Liu, Yunyu Shi, Xian Tang, Xiang Liu

“…In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. …”
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A Spatiotemporal-Adaptive-Network-Based Method for Predicting Axial Forces in Assembly Steel Struts with Servo System of Foundation Pits by Weiwei Liu, Jianchao Sheng, Jian Zhou, Jinbo Fu, Wangjing Yao, Kuan Chang, Zhe Wang

“…Due to its high sensitivity to temperature variations and direct influence on the lateral deformation of the foundation pit enclosure structure, accurate prediction is essential for safety monitoring and early warning. …”
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Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification by Ming Sun, Jie Zhang, Xiaoou He, Yihe Zhong

“…Then, a dual-branch CNN structure, with the fused features from spectral–spatial features by 3D-CNN and spatial features by 2D-CNN, is used to extract shallow spectral–spatial features. …”
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A secured accreditation and equivalency certification using Merkle mountain range and transformer based deep learning model for the education ecosystem by Sumathy Krishnan, Surendran Rajendran, Mohammad Zakariah

“…TCRN employs Bi-GRU to retain long-term academic trends, Depth-wise separable convolutions (DSC) to concentrate on course-specific information, and BERT to capture global semantic context. …”
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Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma, Shuangzhe Liu

“…Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. …”
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A method for identifying gully-type debris flows based on adaptive multi-scale feature extraction by Qiuyu Liu, Ting Wang, Zhijie Zheng, Baoyun Wang

“…First, the feature extraction component consists of a dual-branch structure with a global feature extraction part based on self-attention mechanisms and a local feature extraction part based on multi-scale methods, designed to extract gully features at different scales and establish connections among them. …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. …”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n’s backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n’s neck structure. …”
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Extensive identification of landslide boundaries using remote sensing images and deep learning method by Chang-dong Li, Peng-fei Feng, Xi-hui Jiang, Shuang Zhang, Jie Meng, Bing-chen Li

“…SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block (ASPC) with a coding structure that reduces model complexity. …”
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