Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
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A Robust Multi-Scale Depthwise Separable With Dual-Reservoir Bi-LSTM Model for Gait Phase Recognition Across Complex Terrains by Jing Tang, Zequan Jiang, Chen Yao, Minghu Wu

“…The model employs a multi-scale depthwise separable convolution layer to capture both local and global gait features, while a dual-reservoir BiLSTM network effectively models temporal dependencies for precise phase transitions. …”
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Foot Pressure-Based Abnormal Gait Recognition With Multi-Scale Cross-Attention Fusion by Menghao Yuan, Yan Wang, Xiaohu Zhou, Meijiang Gui, Aihui Wang, Chen Wang, Guotao Li, Hongnian Yu, Lin Meng, Zengguang Hou

“…MSCAF-Gait incorporates multi-scale convolutional modules with channel and spatial attention mechanisms to effectively capture features across temporal, channel, and spatial dimensions. …”
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Kinematic Integration Network With Enhanced Temporal Intelligence and Quality-Driven Attention for Precise Joint Angle Prediction in Exoskeleton-Based Gait Analysis by Lyes Saad Saoud, Irfan Hussain

“…The core innovation of KINETIQA lies in its Kinematic Integration Network (KIN), an architecture that seamlessly integrates advanced techniques, including Transformers, temporal convolutions, and multi-headed attention mechanisms. …”
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

“…This study introduces a multisensor data fusion approach, integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery with advanced machine learning techniques, specifically a convolutional neural network (CNN) based classification model. …”
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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…MDBM learns shared representations out of heterogeneous data sources, MVAE captures the inherent distribution of multi-modalities, while the mechanism of attention in fusion networks is done to stress important features. Finally, temporal context is modeled using long short-term memory and transformer networks, temporal convolutional networks and transformer networks with temporal encoding. …”
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Soil moisture retrieval and spatiotemporal variation analysis based on deep learning by Zihan Zhang, Jinjie Wang, Jianli Ding, Jinming Zhang, Liya Shi, Wen Ma

“…Nine deep learning models, including three basic architectures (Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Transformer) and six hybrid structures (CNN-LSTM, LSTM-CNN, CNN-with-LSTM, CNN-Transformer, GAN-LSTM, Transformer-LSTM), were systematically compared to evaluate the impact of neural network structure on model performance. …”
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Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding by Yelan Wu, Pugang Cao, Meng Xu, Yue Zhang, Xiaoqin Lian, Chongchong Yu

“…To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. …”
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Enhancing Arabic handwritten word recognition: a CNN-BiLSTM-CTC architecture with attention mechanism and adaptive augmentation by Bounour Imane, Ammour Alae, Khaissidi Ghizlane, Mostafa Mrabti

“…This work introduces an enhanced Arabic handwritten word recognition architecture that integrates the attention mechanism (AM) into an end-to-end framework combining convolutional neural networks (CNN), Bidirectional long short-term memory (BiLSTM), and connectionist temporal classification (CTC), while utilizing word beam search (WBS) for decoding. …”
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A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction by Muhammad Farooq Siddique, Faisal Saleem, Muhammad Umar, Cheol Hong Kim, Jong-Myon Kim

“…The model combines time-frequency domain analysis using CWT with a classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and a 1D convolutional residual network (1D conv ResNet). This architecture effectively captures both spatial and temporal dependencies, enhances noise resilience, and extracts discriminative features from nonstationary and nonlinear vibration signals. …”
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Research on the Application of Deep Learning Algorithm in the Damage Detection of Steel Structures by Qingyun Ge, Caimei Li, Fulian Yang

“…This study introduces a novel Convolutional Long Short-Term Memory (ConvLSTM) network for steel structure damage detection, aimed at enhancing the accuracy and reliability of structural health monitoring systems. …”
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An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model by Rajesh Kannan Megalingam, Kariparambil Sudheesh Sankardas, Sakthiprasad Kuttankulangara Manoharan

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Real-Time Human Action Recognition With Dynamical Frame Processing via Modified ConvLSTM and BERT by Raden Hadapiningsyah Kusumoseniarto, Zhi-Yuan Lin, Shun-Feng Su, Pei-Jun Lee

“…In our proposed architecture, we replace global average pooling (GAP) with Bidirectional Encoder Representations from Transformers (BERT) to address the limitations of temporal processing in a two-dimensional convolutional neural network (2D-CNN). …”
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Action Recognition with 3D Residual Attention and Cross Entropy by Yuhao Ouyang, Xiangqian Li

“…Additionally, the integration of Fast Fourier Convolution (FFC) enhances the network’s capability to effectively capture temporal and spatial features. …”
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Spatiotemporal Deformation Prediction Model for Retaining Structures Integrating ConvGRU and Cross-Attention Mechanism by Yanyong Gao, Zhaoyun Xiao, Zhiqun Gong, Shanjing Huang, Haojie Zhu

“…With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. …”
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Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation by Abdul Karim, Semin Ryu, In cheol Jeong

“…An ensemble learning framework was developed by integrating Random Forest, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to classify spectrogram images generated from percussion and palpation signals. …”
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Lightweight Fire Detection in Tunnel Environments by Shakhnoza Muksimova, Sabina Umirzakova, Dilnoza Abduxalikovna Babaraximova, Young Im Cho

“…This study proposes a lightweight hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, offering an efficient and robust solution for real-time tunnel fire detection. …”
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Spatio-Temporal Collaborative Perception-Enabled Fault Feature Graph Construction and Topology Mining for Variable Operating Conditions Diagnosis by Jiaxin Zhao, Xing Wu, Chang Liu, Feifei He

“…Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. …”
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GCT-GF: A generative CNN-transformer for multi-modal multi-temporal gap-filling of surface water probability by Yanjiao Song, Linyi Li, Yun Chen, Junjie Li, Zhe Wang, Zhen Zhang, Xi Wang, Wen Zhang, Lingkui Meng

“…A Generative CNN-Transformer (GCT) for Gap-Filling (GF) of surface water probability, GCT-GF, was then proposed to integrate the strengths of convolutional neural networks (CNNs) and transformers to reconstruct gapless water probability images from multi-modal and multi-temporal data. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

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