MB-MSTFNet: A Multi-Band Spatio-Temporal Attention Network for EEG Sensor-Based Emotion Recognition by Cheng Fang, Sitong Liu, Bing Gao

“…A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. …”
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Multimodal Fall Detection Using Spatial–Temporal Attention and Bi-LSTM-Based Feature Fusion by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Najmul Hassan, Koki Hirooka, Yoichi Tomioka

“…The system uses a Graph-based Spatial-Temporal Convolutional and Attention Neural Network (GSTCAN) to capture spatial and temporal relationships from skeleton and motion data information in stream-1, while a Bi-LSTM with Channel Attention (CA) processes sensor data in stream-2, extracting both spatial and temporal features. …”
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A Data-Driven Multi-Step Flood Inundation Forecast System by Felix Schmid, Jorge Leandro

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Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions by Zhao Dengfeng, Tian Chaoyang, Fu Zhijun, Zhong Yudong, Hou Junjian, He Wenbin

“…The first and second convolutional layers of a convolutional neural network (CNN) are used to simultaneously extract the spatio-temporal features from the bearing vibration signal and fuse them to obtain multi-scale spatiotemporal features. …”
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Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities by Hyung-Ju Ahn, Dae-Hyeok Lee, Ji-Hoon Jeong, Seong-Whan Lee

“…We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi–head attention over the spatial, spectral, and temporal domains. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. …”
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Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism by Zhixin Jin, Xudong Hu, Hongli Wang, Shengyu Guan, Kaiman Liu, Zhiwen Fang, Hongwei Wang, Xuesong Wang, Lijie Wang, Qun Zhang

“…This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. …”
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A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity by Guangwen Peng, Yingbing Liu, Cong Xiao, Wenying Du, Changjiang Xiao

“…To address these challenges, we propose a Dynamic Adaptive Graph Convolutional Recurrent Network (DAGCRN) for global SSS prediction. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

“…We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. …”
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IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks by Suma Christal Mary Sundararajan, Yamini Bhavani Shankar, Sinthia Panneer Selvam, Nalini Manogaran, Koteeswaran Seerangan, Deepa Natesan, Shitharth Selvarajan

“…The proposed model integrates GRU networks with a convolutional autoencoder to improve water quality prediction by capturing trends and managing temporal dependencies. …”
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Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm by Pei Shi, Mingjie Tang, Quan Wang, Xiaofei Ma

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Advancing BCI with a transformer-based model for motor imagery classification by Wangdan Liao, Hongyun Liu, Weidong Wang

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DAGAN-Based Gait Features Augmentation for Ankle Instability Detection by Xin Liu, Yao Zhang, Yuwei Jiao, Bin Zheng, Qinwei Guo, Yuanyuan Yu, Aziguli Wulamu

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Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records by Mamadou Dia, Ghazaleh Khodabandelou, Syed Muhammad Anwar, Alice Othmani

“…Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. …”
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Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain by Pratixita Bhattacharjee, Piotr Augustyniak

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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

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A spatio-temporal fusion-based approach for multi-dimensional classification of Parkinson’s disease progression using multi-modal dataset by Vinay Kukreja, Vandana Ahuja, Modafar Ati, Hariharan Shanmugasundaram, Murugaperumal Krishnamoorthy, Rishabh Sharma, Abhishek Bhattacherjee

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Enhanced Artificial Rabbit Optimization with attention-based deep learning for leukemia cancer classification by Sabita Rani Behera, Bibudhendu Pati, Devee Sivaprasad Dulam

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Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Koki Hirooka, Md. Al Mehedi Hasan, Yoichi Tomioka, Yong Seok Hwang

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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…The performance of the partial least squares regression, random forest, and convolutional neural network–long short-term memory (CNN-LSTM) models was evaluated using a 10-fold cross-validation approach. …”
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