Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network by Nisa Vuran Sarı, Mehmet Acı, Çiğdem İnan Acı

“…Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. …”
Get full text

Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Koki Hirooka, Md. Al Mehedi Hasan, Yoichi Tomioka, Yong Seok Hwang

Subjects: Get full text

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

Subjects: Get full text

Hyperspectral target detection based on graph sampling and aggregation network. by Tie Li, Hongfeng Jin, Zhiqiu Li

“…Notably, before this study, graph sampling aggregation networks had scarcely been employed in the realm of hyperspectral target detection. …”
Get full text

Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…To address these challenges, we propose the dynamic spatial-temporal accident risk network (DSTAR-Net). Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
Get full text

Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…First, our framework considers each feature as the node to construct the graph structure and adopts the graph attention module to learn the complex dependencies among features. …”
Get full text

Drug discovery and mechanism prediction with explainable graph neural networks by Conghao Wang, Gaurav Asok Kumar, Jagath C. Rajapakse

“…XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. …”
Get full text

Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers by Dingge Liang, Junliang Li, Junping Yin

“…To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. …”
Get full text

Author name disambiguation based on heterogeneous graph neural network. by Ge Wang, Zikai Sun, Weiyang Hu, MengHuan Cai

“…The current mainstream methods for author disambiguation are mainly divided into two methods: feature-based clustering and connection-based clustering, but none of the current mainstream methods can efficiently deal with the author name disambiguation problem, For this reason, this paper proposes the author name ablation method based on the relational graph heterogeneous attention neural network, first extract the semantic and relational information of the paper, use the constructed graph convolutional embedding module to train the splicing to get a better feature representation, and input the constructed network to get the vector representation. …”
Get full text

Knowledge graph reasoning: modern methods and applications by Wenguang WANG

“…Knowledge reasoning over knowledge graph aims to discover new knowledge according to the existing knowledge.It is a pivotal technology to realize the human reasoning and decision-making ability of machine.The modern methods of knowledge reasoning over knowledge graph were studied systematically.And the methods based on vector representations with a unified framework were introduced, including the methods based on embedding into Euclidean space and hyperbolic space, and based on deep learning methods such as convolution neural network, capsule network, graph neural network, etc.Simultaneously, the applications of knowledge reasoning in various technical fields and industries were presented, and the existing challenges and opportunities were pointed out as well.…”
Get full text

GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions by Haixiong Ye, Wei Wang, Xiliang Zhang

Subjects: Get full text

Behaviour recognition of housed sheep based on spatio-temporal information by Lina Zhang, Bin Zhao, Chunming He, Fan Yang, Jue Zhang

Subjects: Get full text

PD Recognition for Typical Cardboard Insulation Defect with CNN by ZHAO Jing-he, XIU Da-yuan, WANG Jin-long, CHI Ming-he

“…The convolutional neural network， which is constructed and optimized， obtains the correct rate of about 96.5% and 89.9% respectively in the training set and the test set, showing that convolutional neural networks are suitable for local discharge recognition based on PRPD images.…”
Get full text

Improving healthy food recommender systems through heterogeneous hypergraph learning by Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh

“…These devices generate vast amounts of dynamic, personalized data, which traditional Graph Neural Network (GNN) models — limited to simple pairwise connections — fail to capture effectively. …”
Get full text

Detection and recognition of unsafe behaviors of underground coal miners based on deep learning by GUO Xiaoyuan, ZHU Meiqiang, TIAN Jun, ZHU Beibei

Subjects: Get full text

STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. …”
Get full text

Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction by Hongkai Zhang, Hongxuan Yuan, Minghao Shao, Junxin Wang, Suhong Liu

“…This paper introduces a novel dual-stream collaborative architecture for remote sensing road segmentation, designed to overcome multi-scale feature conflicts, limited dynamic adaptability, and compromised topological integrity. Our network employs a parallel “local–global” encoding scheme: the local stream uses depth-wise separable convolutions to capture fine-grained details, while the global stream integrates a Swin-Transformer with a graph-attention module (Swin-GAT) to model long-range contextual and topological relationships. …”
Get full text

Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles by Fatma S. Alrayes, Nazir Ahmad, Asma Alshuhail, Menwa Alshammeri, Ali Alqazzaz, Hassan Alkhiri, Jehad Saad Alqurni, Yahia Said

“…Moreover, the graph convolutional neural network (GCN) technique is employed for long-term target detection and tracking models. …”
Get full text

A dual path graph neural network framework for dementia diagnosis by Denghui Zhang, Chenxuan Zhu

“…Abstract Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have been widely employed to model complex brain networks. …”
Get full text

Rail Transit Prediction Based on Multi-View Graph Attention Networks by Li Wang, Xin Wang, Jiao Wang

“…Specifically, the proposed model maps multiple relationships into multiple views. A graph convolutional neural network of multiple views with multi-layer attention learns the optimal regression of nodes. …”
Get full text