A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…The method combines the structures of temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to extract spatial and temporal features from digital library network security data. …”
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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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A hybrid learning approach for MRI-based detection of alzheimer’s disease stages using dual CNNs and ensemble classifier by Sepideh Zolfaghari, Atra Joudaki, Yashar Sarbaz

“…Abstract Alzheimer’s Disease (AD) and related dementias are significant global health issues characterized by progressive cognitive decline and memory loss. …”
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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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SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting by Zhuang Li, Zhenyu Lu, Yizhe Li, Xuan Liu

“…Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. …”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Hierarchical vision transformers are a big family for better efficiency in computer vision, but in order to obtain global dependencies, their design is often complex. …”
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Adaptive Pixel-Level and Superpixel-Level Feature Fusion Transformer for Hyperspectral Image Classification by Wei Huang, Dazhan Zhou, Le Sun, Qiqiang Chen, Junru Yin

“…However, graph convolutional networks (GCNs) can effectively extract features from the global structure. …”
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Predicting peak ground acceleration using the ConvMixer networkKey points by Mona Mohammed, Omar M. Saad, Arabi Keshk, Hatem M. Ahmed

“…The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions. …”
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An Effective Feature Extraction Method for Tomato Leafminer - Tuta Absoluta (Meyrick) (Lepidoptera: Gelechiidae) Classification by Tahsin Uygun, Serhat Kiliçarslan, Cemil Közkurt, Mehmet Metin Ozguven

“…Abstract Global warming caused by climate change causes some problems in agricultural production. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…Abstract The global population contains a substantial number of individuals who experience autism spectrum disorder, thus requiring immediate identification to enable successful intervention approaches. …”
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Transformer-based ensemble model for dialectal Arabic sentiment classification by Omar Mansour, Eman Aboelela, Remon Talaat, Mahmoud Bustami

“…The complexity arises from the language’s intricate semantic and morphological structures, along with the existence of multiple dialects. …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…Abstract Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. …”
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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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Enhancing Cross-Domain Remote Sensing Scene Classification by Multi-Source Subdomain Distribution Alignment Network by Yong Wang, Zhehao Shu, Yinzhi Feng, Rui Liu, Qiusheng Cao, Danping Li, Lei Wang

“…To alleviate these issues, we present a Multi-Source Subdomain Distribution Alignment Network (MSSDANet), which introduces novel network structures and loss functions for subdomain-oriented MSDA. …”
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One Health interventions and challenges under rural African smallholder farmer settings: A scoping review by Evanson R. Omuse, Honest Machekano, Bonoukpoè M. Sokame, Daniel M. Mutyambai, Thomas Dubois, Sevgan Subramanian, Frank Chidawanyika

“…The global human population is rapidly increasing, escalating interactions of people, animals and the environment. …”
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MSDCA: A Multi-Scale Dual-Branch Network with Enhanced Cross-Attention for Hyperspectral Image Classification by Ning Jiang, Shengling Geng, Yuhui Zheng, Le Sun

“…First, a multiscale 3D spatial–spectral feature extraction module (3D-SSF) employs parallel 3D convolutional branches with diverse kernel sizes and dilation rates, enabling hierarchical modeling of spatial–spectral representations from large-scale patches and effectively capturing both fine-grained textures and global context. …”
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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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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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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. …”
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