Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms by Xin Zeng, Guang-Peng Su, Wen-Feng Du, Bei Jiang, Yi Li, Zi-Zhong Yang

“…MM-IDTarget integrates some cutting-edge deep learning techniques such as graph transformer, multi-scale convolutional neural networks (MCNN), and residual edge-weighted graph convolutional network (EW-GCN) to extract sequence and structure modal features of drugs and targets. …”
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Review of Research on Trajectory Prediction of Road Pedestrian Behavior by YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu

“…Special emphasis is placed on deep learning methods, categorized by network architecture into sequential models, convolutional neural networks, graph convolutional networks,  generative adversarial networks, etc. …”
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MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting by Sunlei Qian, Xiaorong Zhu

“…To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. …”
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The best angle correction of basketball shooting based on the fusion of time series features and dual CNN by Meicai Xiao

“…Segmenting the shooting video, taking the video frame as the input of the key node extraction network of the shooting action, obtaining the video frame with the sequence information of the bone points, extracting the continuous T-frame video stack from it, and inputting it into the spatial context feature extraction network in the shooting posture prediction model based on dual stream CNN (MobileNet V3 network with multi-channel attention mechanism fusion module), extract the space context features of shooting posture; The superimposed optical flow graph of continuous video frames containing sequence information of bone points is input into the time convolution network (combined with Bi-LSTM network of multi-channel attention mechanism fusion module), extract the skeleton temporal sequence features during the shooting movement, using the spatial context features and skeleton temporal sequence features extracted from the feature fusion module, and realizing the prediction of shooting posture through Softmax according to the fusion results, calculate the shooting release speed under this attitude, solve the shooting release angle, and complete the correction of the best shooting release angle by comparing with the set conditions. …”
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STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai

“…However, current approaches relying on Convolutional Neural Networks (CNNs) for Euclidean data and Graph Convolutional Networks (GCNs) for non-Euclidean structures struggle to simultaneously capture heterogeneous data properties and complex spatio-temporal dependencies. …”
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Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI by Sajedeh Talebi, Neda Abdolvand

“…A term co-occurrence network graph uncovers semantic links, such as “shame” and “depression,” revealing discourse patterns. …”
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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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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. …”
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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. …”
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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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Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops by My Abdelmajid Kassem

“…This review presents an integrated overview of AI/ML applications in QTL mapping and seed trait prediction, highlighting key methodologies such as LASSO regression, Random Forest, Gradient Boosting, ElasticNet, and deep learning techniques including convolutional neural networks (CNNs) and graph neural networks (GNNs). …”
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mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals by Jiake Tian, Yi Zou, Jiale Lai

“…The first stage employs a dual-branch network with depthwise separable convolutions and self-attention to extract multi-scale spatiotemporal features from dual-view radar inputs. …”
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Construction of a traffic flow prediction model based on neural ordinary differential equations and Spatiotemporal adaptive networks by Li Ma, Yunshun Wang, Xiaoshi Lv, Lijun Guo

“…Experiments are conducted on four traffic flow and two traffic speed datasets, showing that compared to traditional time series models, the proposed model’s prediction accuracy indicators have relatively improved by 45.09%, 39.14%, and 0.47% on average; compared to recurrent neural network (RNN) series models, the improvements are 18.91%, 15.77%, and 0.18% on average; compared to graph convolution series models, the improvements are 21.31%, 16.65%, and 0.21% on average; and compared to Transformer series models, the improvements are 6.57%, 6.23%, and 0.05% on average. …”
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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

“…In this study, we introduce a multimodal deep neural network model for DTA prediction, referred to as MDNN-DTA. 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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Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model complex spatial-temporal dependencies, especially in dynamic or subtle actions. …”
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Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics by Su-jun Fang, Su-jun Fang, Zhao-di Yin, Qi Cai, Li-fan Li, Peng-fei Zheng, Li-zhen Chen, Li-zhen Chen

“…Recent advancements in artificial intelligence (AI), especially deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), offer powerful new tools for analysis. …”
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Automated violence monitoring system for real-time fistfight detection using deep learning-based temporal action localization by Baolong Qi, Baoyuan Wu, Bailing Sun

“…The proposed framework leverages both Context-Aware Encoded Transformer (CAET) for modeling interactions between individuals and their environment and Spatial–Temporal Graph Convolutional Networks (ST-GCN) for capturing intra-person and inter-person dynamics from skeletal data. …”
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Research on intelligent classification of coastal land cover by integrating remote sensing images and deep learning by Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Our approach incorporates multi-scale spatial analysis and graph-based models to capture spatial dependencies and contextual features across various coastal environments. …”
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Machine learning for classifying affective valence from fMRI: a systematic review and meta-analysis by Charith Chitraranjan, Ruwan Dayananda, Dakshitha Suriyaaratchie, Nuwan Abeynayake, Svetlana Shinkareva

“…However, we suggest that future studies also explore deep learning architectures such as convolutional and graph neural networks, which have not yet been applied to classify valence from fMRI data.…”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. …”
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