Chinese medical named entity recognition integrating adversarial training and feature enhancement by Xu Zhang, Youchen Kao, Shengbing Che, Juan Yan, Sha Zhou, Shenyi Guo, Wanqin Wang

“…Firstly, the model integrates various advanced technologies, such as Bidirectional Long Short-Term Memory networks (BiLSTM), Iterative Deep Convolutional Neural Networks (IDCNN), and Conditional Random Fields (CRF), to improve the accuracy of named entity recognition. …”
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Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques by Wasim Khan, Mohammad Ishrat, Mohammad Nadeem Ahmed, Shafiqul Abidin, Mohammad Husain, Mohd Izhar, Abu Taha Zamani, Mohammad Rashid Hussain, Arshad Ali

“…To address this, we propose a novel approach that combines a Graph Convolution Auto encoder (GCAE) with self-supervised learning, proximity preservation, and adversarial training using Generative Adversarial Networks (GAN). …”
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The 3D tooth model segmentation method based on GAC+PointMLP network by Jianjun Chen, Liyuan Zheng, Huilai Zou, Jiafa Mao, Weiguo Sheng

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Research on High-Value Patent Prediction Method Based on Technology Component Mapping Network by Yuepeng Li

“…Subsequently, patent document metric features are utilized as node attributes, while the mapped patent document relationships serve as edges to form a patent semantic association network. Ultimately, a graph convolutional neural network (GCN) model is employed to predict high-value patents. …”
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MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction by Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang

“…This model employs Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to extract features from the drug and protein sequences, respectively. …”
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GOMFuNet: A Geometric Orthogonal Multimodal Fusion Network for Enhanced Prediction Reliability by Yi Guo, Rui Zhong

“…GOMFuNet synergistically combines two core mathematical principles: (1) It utilizes geometric deep learning, specifically Graph Convolutional Networks (GCNs), within its Cross-Modal Label Fusion Module (CLFM) to perform fusion in a high-level semantic label space, thereby preserving inter-sample topological relationships and enhancing robustness to inconsistencies. (2) It incorporates a novel Label Confidence Learning Module (LCLM) derived from optimization theory, which explicitly enhances prediction reliability by enforcing mathematical orthogonality among the predicted class probability vectors, directly minimizing output uncertainty. …”
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Event Camera Denoising Using Asynchronous Spatio-Temporal Event Denoising Neural Network by W. Wu, H. Yao, C. Zhai, Z. Dai, X. Zhu

“…Drawing upon principles from graph encoding and temporal convolutional networks, we incorporate spatiotemporal feature attention mechanisms to capture the temporal and spatial correlations between events. …”
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Fish feeding behavior recognition via lightweight two stage network and satiety experiments by Shilong Zhao, Kewei Cai, Yanbin Dong, Guanbo Feng, Yuqing Wang, Hongshuai Pang, Ying Liu

“…This network utilizes pose detection to extract key features, while a graph convolutional network (GCN) effectively models the topological relationships between fish posture and distribution, achieving a satiety classification accuracy of 98.1%. …”
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STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network by Nafiul Hasan, Md. Masud Rana, Md Mahmudul Hasan, AKM Azad, Dil Afroz, Md Mostafizur Rahman Komol, Mousumi Aktar, Mohammad Ali Moni

“…The proposed methodology was benchmarked against Support Vector Machine (SVM), K‐Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Graph Convolutional Neural Network (GCN), demonstrating superior classification performance across different tumor sizes. …”
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The use of artificial intelligence-based Siamese neural network in personalized guidance for sports dance teaching by Yi Xie, Yao Yan, Yuwei Li

“…First, a human skeletal graph is constructed. A graph convolutional network (GCN) is employed to extract features from the nodes (joints) and edges (bone connections) in the graph structure, capturing both spatial relationships and temporal dynamics between joints. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. …”
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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…After selecting keyframes based on point feature counts and line feature overlap angles, we integrate convolutional neural networks (CNNs) and graph neural networks (GNNs) to enhance sparse matching, thereby improving both accuracy and computational efficiency. …”
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Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data by Jian Liu, Xinzheng Xue, Pengbo Wen, Qian Song, Jun Yao, Shuguang Ge

“…SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. …”
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A GPT-Based Approach for Cyber Threat Assessment by Fahim Sufi

“…It utilizes a hybrid methodology combining spectral residual transformation and Convolutional Neural Networks (CNNs) to identify anomalies in time-series cyber event data, alongside regression models for evaluating the significant factors associated with cyber events. …”
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A strategy for network multi-layer information fusion based on multimodel in user emotional polarity analysis by Ronghua Wang, Peng Zhuang

“…In addition, based on models such as graph convolutional neural networks for message passing, contextual information of nodes was obtained to improve emotional analysis performance. …”
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Unmasking insider threats using a robust hybrid optimized generative pretrained neural network approach by P. Lavanya, H. Anila Glory, Manuj Aggarwal, V. S. Shankar Sriram

“…The proposed approach is composed of an Adabelief Wasserstein Generative Adversarial Network (ABWGAN) with Expected Hypervolume Improvement (EHI) of hyperparameter optimization for adversarial sample generation and an L2-Starting Point (L2-SP) regularized pretrained Attention Graph Convolutional Network (AGCN) to detect insiders in the network infrastructure. …”
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ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…In the proposed model, structure-based and location-based localized spatial features are obtained simultaneously by Graph Convolutional Networks (GCNs) and DeepWalk. …”
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Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

“…The proposed framework preprocesses and merges multiple datasets (WSN, NSL-KDD, and CICIDS2017), extracts features using Principal Component Analysis (PCA), and constructs a knowledge graph to embed expert-defined rules via Graph Convolutional Networks (GCNs). …”
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Understanding and classification of innate immune response through weighted edge representation learning with dual hypergraph transformation by Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan, Marion McAfee

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Sustainable Material Cutting Optimization Using Deep Q-Networks: A Reinforcement Learning Approach for Resource Efficiency by Chen Linxuan

“…The GNN model is embedded with Graph Convolutional Networks (GCN) layers, while the DRL model is structured with Deep Q-network (DQN). …”
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