Individual Contribution-Based Spatial-Temporal Attention on Skeleton Sequences for Human Interaction Recognition by Xing Liu, Bo Gao

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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

“…This study presents an innovative approach to hourly demand forecasting for bike-sharing systems using a multi-attribute, edge-weighted, Gated Graph Convolutional Network (GGCN). It addresses the challenge of imbalanced bike borrowing and returning demands across stations, aiming to enhance station utilization rates. …”
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Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill by Rongrong Peng, Changfen Gong, Shuai Zhao

“…First, convolutional neural networks (CNNs) were adopted for the deeper features of multi-sensor signals. …”
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Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks by Gleb Melshin, Anthony DiMaggio, Nadia Zeramdini, Michael MacKinley, Lena Palaniyappan, Alban Voppel

“…Modified ResNet-18 convolutional neural networks (CNNs) performed three classification tasks; (1) schizophrenia-spectrum vs healthy controls, within 179 clinically-rated patients, (2) individuals with more severe vs less severe negative symptom burden, and (3) clinically obvious vs subtle blunted affect. …”
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Coastal salt marsh vegetation classification using hybrid convolutional neural networks and spectral index time series images by Bolu Sun, Dong Zhang, Zhengqing Lai

“…In this study, we propose a novel classification method based on hybrid convolutional neural networks (CNNs) to monitor the coastal saltmarshes of Yancheng City, Jiangsu Province. …”
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A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex. by Jacob R Pennington, Stephen V David

“…Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. …”
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Music audio emotion regression using the fusion of convolutional neural networks and bidirectional long short-term memory models by Yi Qiu, Yu Lin, Yun Lin

“…This research presents an innovative model that combines convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) networks to analyze and predict the emotional impact of musical audio. …”
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Classification of power quality disturbances in microgrids using a multi-level global convolutional neural network and SDTransformer approach. by Junzhuo Jiang, Hao Wu, Changhua Zhong, Hong Song

“…To enhance the accuracy of identifying power quality disturbances in microgrids, this paper introduces a Multi-level Global Convolutional Neural Network combined with a Simplified double-layer Transformer model (MGCNN-SDTransformer). …”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee

“…By combining the spatial feature extraction capability of Graph Convolutional Networks (GCNs) with the latent temporal feature modeling of Variational Autoencoders (VAEs), our method can effectively detect abnormal signs in the data, particularly in the lead-up to system failures. …”
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CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting by Zhenkai Qin, Zhenkai Qin, Zhenkai Qin, Baozhong Wei, Baozhong Wei, Yujia Zhai, Ziqian Lin, Xiaochuan Yu, Xiaochuan Yu, Jingxuan Jiang

“…The CMDMamba model employs a dual-layer Mamba structure that effectively captures price fluctuations at both the micro- and macrolevels in financial markets and integrates an innovative Dual Convolutional Feedforward Network (DconvFFN) module. …”
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Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks by Dakun Lai, Xinyue Zhang, Kefei Ma, Zichu Chen, Wenjing Chen, Heng Zhang, Han Yuan, Lei Ding

“…A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time–frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. …”
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Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study by Shuailei Zhang, Kai Keng Ang, Dezhi Zheng, Qianxin Hui, Xinlei Chen, Yang Li, Ning Tang, Effie Chew, Rosary Yuting Lim, Cuntai Guan

“…To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. …”
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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

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Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network by Tianliang Xu, Zhiquan Zhou, Chenxu Wang, Yingchun Li, Tian Rong

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IEDSFAN: information enhancement and dynamic-static fusion attention network for traffic flow forecasting by Lianfei Yu, Ziling Wang, Wenxi Yang, Zhijian Qu, Chongguang Ren

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An incremental learning framework for pipeline weld crack damage identification and leakage rate prediction by Jing Huang, Zhifen Zhang, Yanlong Yu, Yongjie Li, Shuai Zhang, Rui Qin, Ji Xing, Wei Cheng, Guangrui Wen, Xuefeng Chen

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Recognizing Digital Ink Chinese Characters Written by International Students Using a Residual Network with 1-Dimensional Dilated Convolution by Huafen Xu, Xiwen Zhang

“…Additionally, residual connections facilitate the training of deep one-dimensional convolutional neural networks. Moreover, the paper proposes a more expressive ten-dimensional feature representation that includes spatial, temporal, and writing direction information for each sampling point, thereby improving classification accuracy. …”
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A comparative analysis of deep learning models for accurate spatio-temporal soil moisture prediction by Litao Zhu, Wen Dai, Jiru Huang, Zicong Luo

“…This study fine-tunes and evaluates state-of-the-art deep learning models for spatio-temporal SM prediction in the North China Plain, including Convolutional Long Short-Term Memory (ConvLSTM), Memory in Memory (MIM), Predictive Recurrent Neural Network (PredRNN), and Cubic Recurrent Neural Network (CubicRNN). …”
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A Hybrid Convolutional–Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson’s Disease Detection by Chayut Bunterngchit, Laith H. Baniata, Hayder Albayati, Mohammad H. Baniata, Khalid Alharbi, Fanar Hamad Alshammari, Sangwoo Kang

“…To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. …”
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A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features by Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo, Paolo Gamba

“…Deep learning (DL), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. …”
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