MVBNSleepNet: A Multi-View Brain Network-Based Convolutional Neural Network for Neonatal Sleep Staging by Ligang Zhou, Minghui Liu, Xia Hu, Laishuan Wang, Yan Xu, Chen Chen, Wei Chen

“…<italic>Methods:</italic> We propose MVBNSleepNet, a multi-view brain network-based convolutional neural network. The framework integrates a multi-view brain network (MVBN) to characterize brain functional connectivity from linear temporal correlation, information-theoretic, and phase-dynamics perspectives, providing comprehensive spatial topological information. …”
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A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring by Shixian Dai, Shuang Han, Xinjian Bai, Zijian Kang, Yongqian Liu

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DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network by Ahmed M. Elshewey, Safia Abbas, Ahmed M. Osman, Eman Abdullah Aldakheel, Yasser Fouad

“…This paper presents a comprehensive evaluation of six deep learning models (Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and a proposed hybrid CNN-GRU model) for binary classification of network traffic into benign or attack classes. …”
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Robust Automatic Modulation Classification via a Lightweight Temporal Hybrid Neural Network by Zhao Wang, Weixiong Zhang, Zhitao Zhao, Ping Tang, Zheng Zhang

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Integrated perception-communication-logistics multi-objective oriented path planning for emergency UAVs by XU Yunpeng, XIE Yaqi, YU Ran, HOU Luyang, WANG Kailiang, XU Lianming

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TEDformer: Temporal Feature Enhanced Decomposed Transformer for Long-Term Series Forecasting by Jiayi Fan, Bingyao Wang, Dong Bian

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An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability by Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai, Xin Chen

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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features by Fazeel Zafar, Talha Ahmed Khan, Salas Akbar, Muhammad Talha Ubaid, Sameena Javaid, Kushsairy Abdul Kadir

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Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency by Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

“…While the short-time Fourier transform (STFT) generates time–frequency acoustic features (TFAF) for deep learning networks (DLNs), rectangular convolution kernels cause frequency mixing and aliasing, degrading feature extraction. …”
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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…This paper proposes a convolutional neural network‐long short‐term memory (CNN‐LSTM) network integration model based on spatio‐temporal feature fusion. …”
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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. …”
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DBANet: a dual-branch convolutional neural network with attention enhancement for motor imagery classification by Dandan Liang, Brendan Z. Allison, Ruiyu Zhao, Andrzej Cichocki, Jing Jin

“…However, recent deep learning methods fail to capture the multi-dimensional interaction of features effectively.New method This research proposes a Dual-Branch Convolutional Neural Network with Attention Enhancement (DBANet) for decoding MI. …”
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Applying 1D convolutional neural networks to advance food security in support of SDG 2 by Ghada Alturif, Alaa A. El-Bary, Alaa A. El-Bary, Alaa A. El-Bary, Radwa Ahmed Osman, Radwa Ahmed Osman

“…PurposeThe goal of this study is to predict how well five countries the US, Saudi Arabia, China, Egypt, and Sweden will do in terms of Sustainable Development Goal 2 (SDG 2), particularly the hunger index scores, between 2025 and 2030.MethodsHistorical agricultural, nutritional, and socioeconomic data from 2000 to 2022 were analyses and temporal patterns were extracted using a one-dimensional Convolutional Neural Network (1D-CNN). …”
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IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG by Jiaheng Wang, Lin Yao, Yueming Wang

“…Methods: Inspired by the concept of cross-frequency coupling and its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency Convolutional Neural Network (IFNet) to explore cross-frequency interactions for enhancing representation of MI characteristics. …”
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Fully convolutional neural networks for processing observational data from small remote solar telescopes by Piotr Jóźwik-Wabik, Adam Popowicz

“…Therefore, an insight into the actual image of the Sun with good spatial and temporal resolution is crucial. In this paper, we explore the possibility of using fully convolutional networks (FCNs) to improve the images acquired from remotely operated small solar telescopes whose resolution is limited by the size of the lens aperture and by atmospheric turbulence. …”
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Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks by Feiyan Zhou, Duanshu Fang

“…However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features of Electrocardiograms (ECGs). To address this challenge, we propose a Convolutional Neural Network (CNN) that incorporates mixed scales and hierarchical features combined with the Lead Encoder Attention (LEA) mechanism for multi-lead ECG classification. …”
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Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks by Benjamin P. Veasey, Justin Broadhead, Michael Dahle, Albert Seow, Amir A. Amini

“…<italic>Goal:</italic> We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. …”
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SADNet: sustained attention decoding in a driving task by self-attention convolutional neural network by Shuzhong Lai, Lin Yao, Yueming Wang

“…However, existing methods for decoding attention states from EEG signals face challenges such as insufficient feature extraction, inadequate representation of attention in a normal state, and weak interpretability.Methods To address these issues, we propose a sustained attention state decoding model called Sustain Attention State Decoding Neural Network (SADNet). By combining depthwise separable convolution and self-attention mechanisms, the model applies different attention to signals in the temporal and spatial domains, extracting effective local and global channel features for attention state recognition.Results In within-subject and cross-subject experiments on publicly available datasets, SADNet achieves state-of-the-art performance with an average F1-Score of 0.8894 and 0.6156 respectively, and an average AUC of 0.9545 and 0.7024, outperforming existing models in comparative experiments. …”
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Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks by Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen

“…Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. …”
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Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold by Li Yang, Qiong Yao, Xiang Xu

“…Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition accuracy. …”
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