TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification by Nelly Elsayed, Constantinos L. Zekios, Navid Asadizanjani, Zag ElSayed

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Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction by Yutao Luo, Aining Sun, Jiawei Hong

“…In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. …”
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A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade by Junjia Zhang, Zhuorui Li, Enzhi Wang, Bin Yu, Jiangping Li, Jun Ma

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Electromyography-Based Gesture Recognition With Explainable AI (XAI): Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics by Jungpil Shin, Abu Saleh Musa Miah, Sota Konnai, Shu Hoshitaka, Pankoo Kim

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An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle by Zhi Zhou, Xueling Wu, Bo Peng

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Multichannel Attention-Based TCN-GRU Network for Remaining Useful Life Prediction of Aero-Engines by Jiabao Zou, Ping Lin

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Deep-CABPred: Deep learning model for predicting functional chlorophyll a-b binding proteins in trait-based plant ecology using hybrid embedding with semi-normalized temporal convolutional networks by Farman Ali, Raed Alsini, Tamim Alkhalifah, Fahad Alturise, Wajdi Alghamdi, Majdi Khalid

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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…This paper introduces a novel hybrid framework, CNN-WNN-MMHA, that combines Convolutional Neural Networks (CNN), Wavelet Neural Networks (WNN), and a Masked Multi-Head Attention (MMHA) mechanism. …”
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Power Load Data Completion Method Based on Integrated Graph Convolutional Variational Transformer by YAN Li, HU Hailin, SHI Lei, WU Qinzheng, LÜ Tianguang, XU Yingdong, ZHANG Wenbin, WANG Gaozhou

“…The IGCVT model aggregates an improved graph convolutional network （GCN） and Transformer model using the variational auto-encoder （VAE） architecture. …”
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Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India by S Karkuzhali, Thendral Puyalnithi, R Nirmalan

“…This study focuses on advancing air quality prediction in India through the application of cutting-edge deep learning techniques, specifically the Stacked Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architecture. Through meticulous preprocessing - encompassing missing value handling, normalization, and temporal sequencing - the dataset is prepared for the Stacked Bi-LSTM and CNN hybrid model. …”
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A Reinforced, Event-Driven, and Attention-Based Convolution Spiking Neural Network for Multivariate Time Series Prediction by Ying Li, Xikang Guan, Wenwei Yue, Yongsheng Huang, Bin Zhang, Peibo Duan

“…Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. …”
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MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit by Chenxi Wang, Huizhen Zhang, Shuilin Yao, Wenlong Yu, Ming Ye

“…To fully capture the spatiotemporal correlations, we propose a deep learning model based on graph convolutional neural networks called MDGCN. Firstly, we identify the heterogeneity of stations under two spaces by the Multi-graph convolutional layer. …”
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Phase Wise Classification of Abnormal Gait in Children With Cerebral Palsy Using Hybrid Neural TEMPODE Deep Learning Techniques by Yelle Kavya, S.Sofana Reka

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Weakly Supervised Semantic Segmentation of Mangrove Ecosystem Using Sentinel-1 SAR and Deep Convolutional Neural Networks by Arsalan Ghorbanian, Ardalan Ghorbanian, Seyed Ali Ahmadi, Ali Mohammadzadeh, Amin Naboureh

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sEMG-Based Gesture Recognition Using Sigimg-GADF-MTF and Multi-Stream Convolutional Neural Network by Ming Zhang, Leyi Qu, Weibiao Wu, Gujing Han, Wenqiang Zhu

“…To comprehensively leverage the temporal, static, and dynamic information features of multi-channel surface electromyography (sEMG) signals for gesture recognition, considering the sensitive temporal characteristics of sEMG signals to action amplitude and muscle recruitment patterns, an sEMG-based gesture recognition algorithm is innovatively proposed using Sigimg-GADF-MTF and multi-stream convolutional neural network (MSCNN) by introducing the Sigimg, GADF, and MTF data processing methods and combining them with a multi-stream fusion strategy. …”
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Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network by Haofeng ZHENG, Guohua YANG, Wenjun KANG, Zhiyuan LIU, Shitao LIU, Hong WU, Honghao ZHANG

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A Network Traffic Anomaly Classification Model Based on Self-Attention Mechanism and Convolutional Gated Recurrent Unit by Yulian Li, Yang Su

“…Additionally, this paper introduces a Self-Attention based Convolutional Gated Recurrent Unit (SA_CGRU) model. By combining self-attention mechanisms, Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU), the model captures key features and models temporal dependencies. …”
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Multi-Domain Feature Incorporation of Lightweight Convolutional Neural Networks and Handcrafted Features for Lung and Colon Cancer Diagnosis by Omneya Attallah

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Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network by Yutong Wei, Fudan Zhao, Fengwen Zhao, Shiqiang Zheng, Chaofeng Ye, Liangyu Liu

“…This paper examines the distribution of the human brain visually evoked magnetic field experimentally and then presents an SSVEF measurement system based on an OPM. A three-block temporal convolutional neural network (3B-TCN) is developed to classify brain magnetic signals. …”
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Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network by Bocheng Wang, Guorong Chen, Lu Rong, Yuchuan Liu, Anning Yu, Xiaohui He, Tingting Wen, Yixuan Zhang, Biaobiao Hu

“…Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. …”
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