AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms by Chuanyan Wu, Bentao Lin, Jialin Zhang, Rui Gao, Rui Song, Zhi-Ping Liu

“…Spatial characteristics of proteins are obtained from the protein-protein interaction (PPI) network by employing Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). …”
Get full text

Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) by Ramadhani Anisa, Alya Nurul Fitri

“…The Mask Region-based Convolutional neural network (Mask R-CNN) has shown recent improvements in object detection and extraction for updating data, which are superior to other methods. …”
Get full text

PassAI: An Explainable Machine Learning Framework for Predicting Soccer Pass Outcomes Using Multimodal Match Data by Ryota Takamido, Jun Ota, Hiroki Nakamoto

“…In experiments involving 6,349 passes from professional Japanese soccer league games, PassAI outperformed state-of-the-art models—including convolutional neural network-based and graph neural network-based approaches, as well as existing pass prediction models—by more than 5% in accuracy. …”
Get full text

A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses by Pengpeng Li, Qing Zhu, Jiping Liu, Tao Liu, Ping Du, Shuangtong Liu, Yuting Zhang

“…Then, multi-semantic features of the addresses are extracted using a Text Recurrent Convolutional Neural Network (Text-RCNN) and a Graph Attention Network (GAT). …”
Get full text

mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals by Jiake Tian, Yi Zou, Jiale Lai

“…The second stage refines these estimates using a skeletal topology module based on graph convolutional networks, which captures spatial dependencies among joints to enhance localization accuracy. …”
Get full text

Multi-criteria path rationalization in the conditions of multi-type passenger transport systems by V. V. Egorov

“…As a result, the study obtained algorithms for solving single-criteria and multi-criteria problems on graphs. For multicriterial problems, the author used the convolution method and the method of ordering criteria by the degree of decreasing their significance. …”
Get full text

A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-Level Wind Environment Based on Deep Learning by Zhen-Zhong Hu, Yan-Tao Min, Shuo Leng, Sunwei Li, Jia-Rui Lin

“…This framework integrates Graph Convolutional Networks and Long Short-Term Memory networks to extract and fuse multiple factors and create an end-to-end neural network model capable of directly predicting wind fields. …”
Get full text

Multi-anchor adaptive fusion and bi-focus attention for enhanced gait-based emotion recognition by Jincheng Li, Xuejing Dai, Ruiao Yan, Chengqing Tang, Yunpeng Li

“…To address these issues, we propose a novel temporal graph convolutional network (MDT-GCN) that integrates multi-anchor (MAAF) and bi-focus attention (BFA) mechanisms. …”
Get full text

Evaluation Model Based on the SGCNiFormer for the Influence of Different Storage Environments on Wheat Quality by Qingchuan Zhang, Zexi Song, Mingwen Bi

“…The system incorporates a graph convolutional network (GCN) and a dynamic gating module, enabling precise simulation of the multidimensional evolution of wheat quality under the interaction of moisture and temperature. …”
Get full text

Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction by Yi Wang

“…This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. …”
Get full text

Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention by Yash Jakhmola, Madhurima Panja, Nitish Kumar Mishra, Kripabandhu Ghosh, Uttam Kumar, Tanujit Chakraborty

“…Traditional statistical and machine learning methods struggle to handle both temporal and spatial dependencies in such datasets. While graph convolutional networks and multi-head attention mechanisms have been widely adopted in this field, they often fail to accurately model dynamic temporal patterns and effectively differentiate noise from signals in traffic datasets, leading to potential overfitting. …”
Get full text

A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living by Fahmid Al Farid, Ahsanul Bari, Abu Saleh Musa Miah, Sarina Mansor, Jia Uddin, S. Prabha Kumaresan

“…Furthermore, we explore a wide range of machine learning and deep learning models—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and Graph Convolutional Networks (GCNs)—along with lightweight transfer learning methods suitable for environments with limited computational resources. …”
Get full text

A novel ST-GCN model based on homologous microstate for subject-independent seizure prediction by Wei Shi, Yi Shi, Fangni Chen, Lei Zhang, Jian Wan

“…Homologous microstate dynamic attributes were extracted using a novel spatiotemporal graph convolutional network (ST-GCN) model for subject-independent seizure prediction. …”
Get full text

Crop field extraction from high resolution remote sensing images based on semantic edges and spatial structure map by Liegang Xia, Ruiyan Liu, Yishao Su, Shulin Mi, Dezhi Yang, Jun Chen, Zhanfeng Shen

“…In recent years, deep convolutional neural networks (CNNs) have gained significant attention for edge detection tasks. …”
Get full text

Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer by Muhammad Shoaib Saleem, Javed Rashid, Sajjad Ahmad, Ali M. Al‐Shaery, Saad Althobaiti, Muhammad Faheem

“…The performance of the proposed TFT is more authentic as compared with the gated recurrent unit (GRU), the long short‐term memory (LSTM), deep autoregression (DeepAR), and the meta graph‐based convolutional recurrent network (MegaCRN). …”
Get full text

Spatiotemporal hybrid deep learning for estimating and analyzing carbon stocks: a case study in Jiangsu province, China by Lizhi Miao, Jvmin Wang, Kaiwen Wu, Heng Xu, Xiying Sun, Gang Lu, Mei-Po Kwan

“…This research applies GCN (Graph Convolutional Network) to extract spatial features and BiLSTM (Bidirectional Long Short Term Memory Network) to capture temporal features, considering the impact of various factors on carbon stocks. …”
Get full text

Electricity theft detection in integrated energy systems considering multi-energy loads by Wenlong Liao, Dechang Yang, Leijiao Ge, Yixiong Jia, Zhe Yang

“…Furthermore, a Chebyshev graph convolutional network (ChebGCN) is proposed to detect malicious users by capturing latent features and correlations from the graphs. …”
Get full text

Application of Deep Learning in Classification and Diagnosis of Mild Cognitive Impairment by ZHOU Qixiang, WANG Xiaoyan, ZHANG Wenkai, HE Xin

“…Then it focuses on the application of deep learning models and methods in the classification and diagnosis of mild cognitive impairment, including but not limited to automatic encoders, deep belief networks, generative adversarial networks, convolutional neural networks, and graph convolutional neural networks, and points out the model interpretability techniques used in the research. …”
Get full text

Prognostic and therapeutic potential of disulfidptosis-related genes in colon adenocarcinoma: a comprehensive multi-omics study by Ye Song, Haoran Zhu, Junyang Wei, Shanxue Yin

“…The ProjecTILs algorithm identified a higher proportion of Th1 cells, while Graph Convolutional Network (GCN) analysis showed no significant differences in T cell subtype proportions across different phenotypes. …”
Get full text

Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

“…Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. …”
Get full text