Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates by Lin Meng, Deyu Wang, Jun Ma, Yu Shi, Hongbo Zhao, Yanlin Wang, Qingqing Shi, Xiaodong Zhu, Dong Ming

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Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset by Houmem Slimi, Imen Cherif, Sabeur Abid, Mounir Sayadi

“…This study proposes a hybrid convolutional neural network-spiking neural network (CNN-SNN) architecture to classify AD stages using structural MRI (sMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). …”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…Traffic prediction is the key for Intelligent Transport Systems (ITS) to achieve traffic control and traffic guidance, and the key challenge is that traffic flow has complex spatial-temporal dependence and nonlinear dynamics. Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. …”
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Hybrid lightweight temporal-frequency analysis network for multi-channel speech enhancement by Yinghan Cao, Shiyun Xu, Wenjie Zhang, Mingjiang Wang, Yun Lu

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MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting by Sunlei Qian, Xiaorong Zhu

“…To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. …”
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PoseRL-Net: human pose analysis for motion training guided by robot vision by Bin Liu, Hui Wang

“…PoseRL-Net integrates multiple components, including a Spatial-Temporal Graph Convolutional Network (STGCN), attention mechanism, Gated Recurrent Unit (GRU) module, pose refinement, and symmetry constraints. …”
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DTCNet: finger flexion decoding with three-dimensional ECoG data by Fufeng Wang, Zihe Luo, Wei Lv, XiaoLin Zhu

“…The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…To address the issues of low prediction accuracy and weak generalization ability in carbonate reservoir porosity prediction, a porosity prediction model (CNN-BiLSTM-Transformer) combining a convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and Transformer network is proposed. …”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

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State of health prediction of lithium-ion batteries in charging chambers based on multi-modal deep learning by ZHAO Yinghua, CHEN Anbi, ZHANG Zengyu, LI Wenzhong, HAN Yu

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An air target intention data extension and recognition model based on deep learning by Bo Cao, Qinghua Xing, Longyue Li, Weijie Lin

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ABA-IDS: Attention-Based Autoencoder for Intrusion Detection in Assistive Mobility Robotic Network by Daniel Ayo Oladele, Elisha Didam Markus

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A text classification model for dynamic fusion of global and local features by ZHENG Wenjun, ZHANG Shunxiang

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Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism by Yuchen Yang, Chunsong Han, Guangtao Ran, Tengyu Ma, Juntao Pan

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Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN by Tie CHEN, Zhifan ZHANG, Xianshan LI, Yifu CHEN, Hongxin LI

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A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

“…A convolutional neural network combined with long short-term memory (CNN-LSTM) phase compensation method (PCM) is proposed and demonstrated, where CNN is employed to extract spatial features, and LSTM is used to capture temporal features and realize the long-term predictions of residual phase fluctuations. …”
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Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms by Tuxiang Lin, Rongliang Lin

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Multi-branch feature learning based speech emotion recognition using SCAR-NET by Keji Mao, Yuxiang Wang, Ligang Ren, Jinhong Zhang, Jiefan Qiu, Guanglin Dai

“…In this paper, we propose SCAR-NET, an improved convolutional neural network, to extract emotional features from speech signals and implement classification. …”
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Human activity recognition algorithms for manual material handling activities by Andreas Sochopoulos, Tommaso Poliero, Jamil Ahmad, Darwin G. Caldwell, Christian Di Natali

“…The analyzed classifiers are feedforward neural networks, 1-D convolutional neural networks, and recurrent neural networks, standard architectures in time series classification but offer different classification capabilities and computational complexity. …”
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