Long sequence time-series forecasting method based on multi-scale segmentation by HE Shenglin, LONG Chen, ZHENG Jing, WANG Shuang, WEN Zhenkun, WU Huisi, NI Dong, HE Xiaorong, WU Xueqing

“…Experimental results on the real-world power transformer dataset, encompassing variables like electricity transformer temperature, electricity consumption load, and weather demonstrate that the proposed Transformer model based on the multi-scale segmentation approach outperforms traditional benchmark models such as Transformer, Informer, gated recurrent unit, temporal convolutional network and long short term memory in terms of mean absolute error (MAE) and mean squared error (MSE). …”
Get full text

Personalized region of interest recommendation through adaptive fusion of multi-dimensional user preferences by Qing Tang, Shenghua Xu, Zhuolu Wang, Yong Wang, Shichuan Liu, Haiqin Hua

“…Next, the social preferences are determined by assessing the influence of social connections and the impact of social networks on the likelihood of a user visiting a region, using a convolutional neural network. …”
Get full text

CABAD: A video dataset for benchmarking child aggression recognition by Shehzad Ali, Md Tanvir Islam, Ik Hyun Lee, Mohammad Hijji, Khan Muhammad

“…Leveraging CABAD, we propose CABA_Net, a multi-stage deep-learning framework integrating MobileViT for spatial feature extraction, Temporal Convolutional Networks (TCN) for sequential modeling, and an Attention LSTM for refined temporal attention on behavioral patterns. …”
Get full text

Analysis of Passenger Flow Characteristics and Origin–Destination Passenger Flow Prediction in Urban Rail Transit Based on Deep Learning by Zhongwei Hou, Jin Han, Guang Yang

“…To simultaneously consider the spatiotemporal characteristics of passenger flow distribution and achieve high precision estimation of origin and destination (OD) passenger flow quickly, a predictive model based on a temporal convolutional network and a long short-term memory network (TCN–LSTM) combined with an attention mechanism was established to process passenger flow data in urban rail transit. …”
Get full text

Land Surface Temperature Super-Resolution With a Scale-Invariance-Free Neural Approach: Application to MODIS by Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurelie Michel, Julien Michel, Xavier Briottet, Lucas Drumetz

“…The main contribution of this work is the introduction of a Scale-Invariance-Free approach for training neural network (NN) models, and the implementation of two NN models, called Scale-Invariance-Free Convolutional Neural Network for Super-Resolution (SIF-CNN-SR) for the super-resolution of MODIS LST products. …”
Get full text

ECO++: Adaptive deep feature fusion target tracking method in complex scene by Yuhan Liu, He Yan, Qilie Liu, Wei Zhang, Junbin Huang

“…In this paper, we propose a new target tracking method, namely ECO++, using deep feature adaptive fusion in a complex scene, in the following two aspects: First, we constructed a new temporal convolution mode and used it to replace the underlying convolution layer in Conformer network to obtain an improved Conformer network. …”
Get full text

From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

“…The ensuing renormalizing generative models (RGM) can be regarded as discrete homologs of deep convolutional neural networks or continuous state-space models in generalized coordinates of motion. …”
Get full text

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data by Shenyi Ding, Zhijie Wang, Jue Zhang, Fang Han, Xiaochun Gu, Guangxiao Song

“…Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. …”
Get full text

SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT by Kamran Sattar Awaisi, Qiang Ye, Srinivas Sampalli

“…Our experimental results indicate that SET consistently outperforms baseline methods, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and Multilayer Perceptron (MLP), as well as the proposed comparative variant of SET, Multi-Encoder Transformer (MET), in terms of accuracy, precision, recall, and F1-score across different fault intensities. …”
Get full text

Traffic flow prediction based on spatiotemporal encoder-decoder model. by Yuanming Ding, Wei Zhao, Lin Song, Chen Jiang, Yunrui Tao

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
Get full text

Lightweight and efficient skeleton-based sports activity recognition with ASTM-Net. by Bin Wu, Mei Xue, Ying Jia, Ning Zhang, GuoJin Zhao, XiuPing Wang, Chunlei Zhang

“…To address these challenges, we propose ASTM‑Net, an Activity‑aware SpatioTemporal Multi‑branch graph convolutional network comprising two novel modules. …”
Get full text

Security application of intrusion detection model based on deep learning in english online education by Xue Li, Yugui Zhang

“…Therefore, this paper proposes a multi scale convolutional neural network based on multi head attention mechanism and hierarchical long short term memory network (MCNN-MHA-HLSTM). …”
Get full text

Depth prediction of urban waterlogging based on BiTCN-GRU modeling. by Quan Wang, Mingjie Tang, Pei Shi

“…This model integrates Bidirectional Temporal Convolutional Networks (BiTCN) and Gated Recurrent Units (GRU) to enhance prediction performance. …”
Get full text

A Deep Reinforcement Learning Approach for Portfolio Management in Non-Short-Selling Market by Ruidan Su, Chun Chi, Shikui Tu, Lei Xu

“…Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. Temporal interrelation is also captured by a temporal convolutional network based on new factors designed with price and volume data. …”
Get full text

Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…This design allows the model to prioritize features significantly contributing to action recognition, enhancing its overall recognition capabilities. A temporal convolutional network processes the extracted action features. …”
Get full text

Enhancing Speaker Recognition with CRET Model: a fusion of CONV2D, RESNET and ECAPA-TDNN by Pinyan Li, Lap Man Hoi, Yapeng Wang, Xu Yang, Sio Kei Im

“…Although the Emphasized Channel Attention, Propagation, and Aggregation in Time Delay Neural Network (ECAPA-TDNN) model can obtain temporal context information through dilated convolution to some extent, this model falls short in acquiring fully comprehensive speech features. …”
Get full text

Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
Get full text

EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…For EEG signals, spatiotemporal features are extracted using dual-scale temporal convolution and depthwise separable convolution, and a hybrid attention module is introduced to enhance the network's sensitivity to salient neural patterns. …”
Get full text

The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM by Fan Hou, Yue Zhang, Xinli Fu, Lele Jiao, Wen Zheng

“…Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). …”
Get full text

Cross-Scenario Subdomain Adaptive Displacement Anomaly Detection in Dams by Yu Wang, Guohua Liu

“…To overcome the challenges of limited data, domain distribution differences, and the need for retraining in unsupervised learning methods for cross-scenario anomaly detection in dams, this study introduces a novel approach; the Temporal Displacement Subdomain Adaptation Network (TDSAN) combines temporal convolutional networks with subdomain adaption. …”
Get full text