Image Experience Prediction for Historic Districts Using a CNN-Transformer Fusion Model by Weijia Wang, Youping Teng, Lu Yan, Longwei Wu, Yinying Yang, Zijian Luo

“…A deep learning-based sentiment analysis system was developed, utilising a convolution neural network (CNN) and transformer models to assess emotional tendencies and temporal states within images. …”
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End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems by Chi Xu, Xinyi Du, Lin Li, Xinchun Li, Haibin Yu

“…Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. …”
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Wind Turbine Fault Diagnosis with Imbalanced SCADA Data Using Generative Adversarial Networks by Hong Wang, Taikun Li, Mingyang Xie, Wenfang Tian, Wei Han

“…Specifically, the long short-term memory network that can handle time series data well is used in the generator network to learn the temporal correlations from SCADA data and thus generate samples with temporal dependencies. …”
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A Hybrid Method Combining Variational Mode Decomposition and Deep Neural Networks for Predicting PM2.5 Concentration in China by Senlin Li, Bo Tang, Xiaowu Deng

“…The deep neural structures used include recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs). …”
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An action decoding framework combined with deep neural network for predicting the semantics of human actions in videos from evoked brain activities by Yuanyuan Zhang, Manli Tian, Baolin Liu

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Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis by Ramesh Kumar Behara, Akshay Kumar Saha

“…The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. …”
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AI-driven video summarization for optimizing content retrieval and management through deep learning techniques by Deepali Vora, Payal Kadam, Dadaso D Mohite, Nilesh Kumar, Nimit Kumar, Pratheeik Radhakrishnan, Shalmali Bhagwat

“…To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. …”
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Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning by Seong-Yoon Kim, Hyun-Min Lee, Chae-Young Lim, Hyun-Woo Kim

“…In this work, we extract key features such as spectrograms, Mel-spectrograms, and MFCCs from raw acoustic data and use them as input for training a convolutional neural network. The proposed model is based on a custom ResNet architecture that incorporates Bottleneck Residual Blocks to improve training stability and computational efficiency. …”
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Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern by Hanjie Zhang, Andrea Nandorine Ban, Peter Kotanko

“…We built one-dimensional convolutional neural networks (1D-CNN), a state-of-the-art deep learning method, for SaO2 pattern classification and randomly assigned SaO2 time series segments to either a training (80%) or a test (20%) set. …”
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Fall detection method based on spatio-temporal coordinate attention for high-resolution networks by Xiaorui Zhang, Qijian Xie, Wei Sun, Ting Wang

“…The method employs 3D convolutions to extract spatio-temporal features and utilizes gradual down-sampling to generate a multi-resolution sub-network, thus realizing multi-scale fusion and perception enhancement of details. …”
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Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions by Shiyang Chen, Yang Liu, Qun Zhang, Zhouhang Shao, Zewei Wang

“…This article presents MDST‐GNN, a multi‐distance spatial‐temporal graph neural network for blockchain anomaly detection. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. …”
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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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Zebrafish identification with deep CNN and ViT architectures using a rolling training window by Jason Puchalla, Aaron Serianni, Bo Deng

“…We demonstrate a rolling window training technique suitable for use with open-source convolutional neural networks (CNN) and vision transformers (ViT) that shows promise in robustly identifying individual maturing zebrafish in groups over several weeks. …”
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A novel temporal classification prototype network for few-shot bearing fault detection by Yanfei Liu, Ziang Du, Hao Zheng, Qian Zhang, Cheng Chen, Nana Wu

“…Subsequently, discrete data sample points are transformed into points within the feature space via our Enhanced Temporal Convolutional Network(ETCN). In our investigation, we utilize the features of the support set as anchors within the feature space and employ similarity measures as the basis for classification, thus developing a more effective comparative learning classifier known as the ContractSim Classifier (CSC). …”
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Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage by Yang-Cheng Shih, Sathesh Tamilarasan, Chin-Sheng Chen, Omid Ali Zargar, Yean-Der Kuan

“…Addressing the critical need for enhanced power usage effectiveness in data centers (DCs), this study pioneers an improved convolutional long short-term memory with deep neural network (CLDNN) model, enriched with attention mechanisms for precise DC performance prediction. …”
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A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction by Shengyou Wang, Chunfu Shao, Yajiao Zhai, Song Xue, Yan Zheng

“…The impacts of upstream and downstream road sections are considered on the spatial relationship by using a Convolutional Neural Network (CNN). A Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to account for the temporal characteristics. …”
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A combined model for short-term traffic flow prediction based on variational modal decomposition and deep learning by Chuanxiang Ren, Fangfang Fu, Changchang Yin, Li Lu, Lin Cheng

“…Therefore, a combined prediction model, VMD-GAT-MGTCN, based on variational modal decomposition (VMD), graph attention network (GAT), and multi-gated attention time convolutional network (MGTCN) is proposed to enhance short-term traffic flow prediction accuracy. …”
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Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism by Yahong Ma, Zhentao Huang, Yuyao Yang, Shanwen Zhang, Qi Dong, Rongrong Wang, Liangliang Hu

“…DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network’s performance. …”
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CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems by Yafei Li, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, Zelin Wang

“…In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). …”
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