WideConvNet: A Novel Wide Convolutional Neural Network Applied to ERP-Based Classification of Alcoholics by Abdul Baseer Buriro, Attaullah Buriro, Aziz Altaf Khuwaja, Abdul Aziz Memon, Nabeel Siddiqui, Stephen J. Weddell, Richard D. Jones

“…This paper presents WideConvNet, a novel convolutional neural network (ConvNet or CNN) that outperforms three established end-to-end models – EEGNet, Shallow Convolutional Neural Network (ShallowConvNet), and Deep Convolutional Neural Network (DeepConvNet) – in classifying event-related potentials (ERPs) from electroencephalogram (EEG) signals. …”
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Spatio-temporal prediction of terrorist attacks based on GCN-LSTM by Yingjie Du, Ning Ding, Hongyu Lv

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Recent technological developments allow for passive acoustic monitoring of Orthoptera (grasshoppers and crickets) in research and conservation across a broad range of temporal and... by David Bennett, Henning Nissen, Marc Andre Maschke, Heinrich Reck, Tim Diekötter

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A multi-scale temporal feature fusion framework for sheep voiceprint recognition by Xipeng Wang, Delong Wang, Weijiao Dai, Cheng Zhang, Yudongchen Liang, Yong Zhou, Juan Yao, Fang Tian

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An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention by Bin Jiang, Hao Wu, Qingling Xia, Hanguang Xiao, Bo Peng, Li Wang, Yun Zhao

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Wavelet-CNN for temporal data: Enhancing long-term stock price prediction via multi-resolution wavelet decomposition and CNN-based feature extraction by Komei Hiruta, Junsuke Senoguchi

“…To address these challenges, we propose an approach that combines wavelet transformation and a convolutional neural network (CNN), both of which are specialized for long-term stock price prediction, to efficiently and automatically extract the features of stock prices at various temporal resolutions. …”
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EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization by Yonghao Song, Qingqing Zheng, Bingchuan Liu, Xiaorong Gao

“…Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding. …”
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Subdivision of river channel sand micro-scale facies with feature attention spatio-temporal network by Ruipu Zhao, Lili Zeng, Chendong Fu, Xiaoqing Zhao

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Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach by Saif Ullah, Niamat Ullah, Muhammad Farooq Siddique, Zahoor Ahmad, Jong-Myon Kim

“…In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. …”
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Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions by Meshari D. Alanazi, Amna Maraoui, Imen Werda, Ahmed Ben Atitallah, Turki M. Alanazi, Mohammed Albekairi, Anis Sahbani, Amr Yousef

“…The dual difficulties of high computing needs in 3D convolutional networks and the necessity of more precise brain tumor diagnosis are addressed by this work. …”
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Convolutional Autoencoder With Sequential and Channel Attention for Robust ECG Signal Denoising With Edge Device Implementation by Alif Wicaksana Ramadhan, Syifa Kushirayati, Salsabila Aurellia, Mgs M. Luthfi Ramadhan, Muhammad Hannan Hunafa, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Siti Nurmaini, Satria Mandala, Wisnu Jatmiko

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TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks by Adir Krayden, M. Avraham, H. Ashkar, T. Blank, S. Stolyarova, Yael Nemirovsky

“…We present a real-time drift compensation framework based on a lightweight Temporal Convolutional Neural Network (TCNN) combined with a Hadamard spectral transform. …”
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Recognition and classification techniques of marine mammal calls based on LSTM and expanded causal convolution by Wanlu Cheng, Wanlu Cheng, Hao Chen, Jiaming Jiang, Jiaming Jiang, Shuang Li, Shuang Li, Jingjing Wang, Jingjing Wang, Yanping Zhou

“…The model comprises three modules: (1) a frequency-domain feature extraction module employing dilated causal convolutions at multiple scales to capture multi-resolution spectral information from Mel spectrograms; (2) a time-domain feature extraction module that inputs Mel-frequency cepstral coefficients (MFCCs) into an LSTM enhanced with a time-attention mechanism to highlight key temporal features; and (3) a classification module leveraging transfer learning, where a pre-trained neural network is fine-tuned on real marine mammal call data to improve performance. …”
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Golden Chip-Free Hardware Trojan Detection Using Attention-Based Non-Local Convolution With Simple Recurrent Unit by Rama Devi Maddineni, Deepak Ch

“…Furthermore, temporal features are extracted using recurrent neural networks such as the simple recurrent unit. …”
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SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition by Nicole Yah Yie Ha, Lee-Yeng Ong, Meng-Chew Leow

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AAT model based channel estimation for mmWave massive MIMO systems by Shujuan YU, Rong LIU, Yun ZHANG, Na XIE, Liya HUANG

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VSRDiff: Learning Inter-Frame Temporal Coherence in Diffusion Model for Video Super-Resolution by Linlin Liu, Lele Niu, Jun Tang, Yong Ding

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Exoplanet Classification Through Vision Transformers with Temporal Image Analysis by Anupma Choudhary, Sohith Bandari, B. S. Kushvah, C. Swastik

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Federated duelling deep Q‐network based collaborative energy scheduling for a power distribution network by Yanhong Yang, Wei Pei, Tianyi Xu, Dawei Wang, Abdelbari Redouane

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Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction by Juan Chen, Rui Huang

“…In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. …”
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