Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection by Zhen Gao, Xiaowen Chen, Jingning Xu, Rongjie Yu, Heng Zhang, Jinqiu Yang

“…The proposed CT-Net (CLIP-Transformer Network) achieves an AUC (Area Under the Curve) of 0.892, a 36% accuracy improvement over the prevalent CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) end-to-end model, reaching state-of-the-art performance. …”
Get full text

Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation by Chenglin Yu, Lihong Shi, Yangyang Zhao

“…Our approach begins with the construction of trajectory flow graphs using graph convolutional networks (GCNs) to globally capture POI correlations across both spatial and temporal dimensions. …”
Get full text

Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review by S. M. Atoar Rahman, Md Ibrahim Khalil, Hui Zhou, Yu Guo, Ziyun Ding, Xin Gao, Dingguo Zhang

“…In this overview, we review the very new and fundamental models of GNNs and their modifications, such as graph regularized neural networks, graph convolutional neural networks, spatial-temporal graph neural networks, graph attention networks, and their variants in EEG signal analysis fields. …”
Get full text

Biomimetic Visual Information Spatiotemporal Encoding Method for In Vitro Biological Neural Networks by Xingchen Wang, Bo Lv, Fengzhen Tang, Yukai Wang, Bin Liu, Lianqing Liu

“…This method transforms high-dimensional images into a series of pulse sequences through convolution, temporal delay, alignment, and compression for BNN stimuli. …”
Get full text

Neural network-based symmetric encryption algorithm with encrypted traffic protocol identification by Jiakai Hao, Ming Jin, Yuting Li, Yuxin Yang

“…In this study, we first introduce a plaintext guessing model (SCGM model) based on symmetric encryption algorithms, leveraging the strengths of convolutional neural networks to evaluate the plaintext guessing capabilities of four symmetric encryption algorithms. …”
Get full text

Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification by Hunmin Lee, Ming Jiang, Jinhui Yang, Zhi Yang, Qi Zhao

“…To address these limitations, we introduce novel graph structures meticulously crafted to encapsulate the spatial proximity of distributed EMG sensors and the temporal adjacency of EMG signals. Harnessing these tailored graph structures, we present Graph Convolution Network (GCN)-based classification models adept at effectively extracting and aggregating key features associated with various gestures. …”
Get full text

A Framework for User Traffic Prediction and Resource Allocation in 5G Networks by Ioannis Konstantoulas, Iliana Loi, Dimosthenis Tsimas, Kyriakos Sgarbas, Apostolos Gkamas, Christos Bouras

“…To address this issue, we present a framework for both predicting user traffic and allocating users to base stations in 5G networks using neural network architectures. This framework consists of a hybrid approach utilizing a Long Short-Term Memory (LSTM) network or a Transformer architecture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. …”
Get full text

A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting by Yaojun Ding, Bingxuan Hu

“…The LRCT network effectively leverages the temporal learning advantages of Local Recurrent Networks (Local RNN) and the spatial learning strengths of Convolutional Neural Network (CNN) by designing the local feature extraction block (denoted as LRC Block), which extracts fine-grained local features from 2000-dimensional original sequences and reduces the dimensionality to 125. …”
Get full text

GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults by Proma Dutta, Kanchon Kanti Podder, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury, Amith Khandakar, Nasser Al-Emadi, Moajjem Hossain Chowdhury, M. Murugappan, Mohamed Arselene Ayari, Sakib Mahmud, S. M. Muyeen

“…This research presents GearFaultNet, a novel, lightweight 1D Convolutional Neural Network (CNN)-based network, designed to detect gearbox faults. …”
Get full text

Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection by Canbin Hu, Sida Du, Hongyun Chen, Xiaokun Sun, Kailun Liu

“…To address these challenges, we propose the change-guided difference interaction attention network (CGDIANet). This network effectively establishes interaction between dual-temporal features through difference interaction attention module (DIAM), enhancing the capability to extract change features. …”
Get full text

Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

Get full text

A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia by Xiukuan Zhao, Guozhu Li, Haiyong Xie, Lianhuan Hu, Wenjie Sun, Yi Li, Guofeng Dai, Jianfei Liu, Yu Li, Baiqi Ning, Michi Nishioka, Septi Perwitasari, Prasert Kenpankho

“…The model integrates the convolutional neural network and long short‐term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. …”
Get full text

Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang, Jinlei Zhang

“…This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to jointly predict toll station inbound flow and outbound flow. …”
Get full text

Multiscale Interaction Purification-Based Global Context Network for Industrial Process Fault Diagnosis by Yukun Huang, Jianchang Liu, Peng Xu, Lin Jiang, Xiaoyu Sun, Haotian Tang

“…The application of deep convolutional neural networks (CNNs) has gained popularity in the field of industrial process fault diagnosis. …”
Get full text

Feasibility Analysis for Predicting Indian Ocean Bigeye Tuna (<i>Thunnus obesus</i>) Fishing Grounds Based on Temporal Characteristics of FY-3 Microwave Radiation Imager Data by Yun Zhang, Jinglan Ye, Shuhu Yang, Yanling Han, Zhonghua Hong, Wanting Meng

“…For this paper, we designed a deep learning network model for radiometer TB time series feature extraction (TimeTB-FishNet) based on the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Attention mechanism. …”
Get full text

Human activity recognition algorithm based on the spatial feature for WBAN by Chi JIN, Zhijun LI, Dayang SUN, Fengye HU

“…Traditional image-based activity recognition algorithms have some problems,such as high computational cost,numerous blind spots and easy privacy leakage.To solve the problem above,the CCLA (convolution-convolutional long short-term memory-attention) activity recognition algorithm based on the acceleration and gyroscope data was proposed.The convolutional neural network was used to extract spatial features of activity data and got the hidden time series information from the convolutional long short-term memory network.Simulating human brain selecting attention mechanism,attention-encoder was constructed to extract the spatial and temporal features at a higher level.The CCLA algorithm was tested on UCI-HAPT (university of California Irvine-smartphone-based recognition of human activities and postural transitions) public data set,and realized the classification of 12 types of activity with the accuracy of 93.27%.…”
Get full text

Channel Estimation Using CNN-LSTM in RIS-NOMA Assisted 6G Network by Chi Nguyen, Tiep M. Hoang, Adnan A. Cheema

“…CNN-LSTM leverages both the benefits of convolutional neural network (CNN) as well as long-short term memory (LSTM), in which CNN can capture special features while LSTM can capture temporal features of time-series data. …”
Get full text

Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling by Can CHEN, Zinuo SU, Yuan MA, Jialin LIU, Yuqing WANG, Fei WANG

“…Then, a probabilistic forecasting model based on hierarchical graph convolutional neural networks (GCNs) is proposed to mine deep spatio-temporal correlation features between PV power stations, thereby enhancing the accuracy of ultra-short-term probabilistic forecasting of regional distributed PV power. …”
Get full text

Novel video anomaly detection method based on global-local self-attention network by Jing YANG, Chengmao WU, Liuping ZHOU

“…In order to improve the accuracy of video anomaly detection, a novel video anomaly detection method based on global-local self-attention network was proposed.Firstly, the video sequence and the corresponding RGB sequence were fused to highlight the motion change of the object.Secondly, the temporal correlation of the video sequence in the local area was captured by the expansion convolution layer, along with the self-attention network was utilized to compute the global temporal dependencies of the video sequence.Meanwhile, by deepening the basic network U-Net and combining the relevant motion and representation constraints, the network model was trained end-to-end to improve the detection accuracy and robustness of the model.Finally, experiments were carried out on the public data sets UCSD Ped2, CUHK Avenue and ShanghaiTech, as well as the test results were visually analyzed.The experimental results show that the detection accuracy AUC of the proposed method reaches 97.4%, 86.8% and 73.2% respectively, which is obviously better than that of the compared methods.…”
Get full text

Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction by Zhifei Yang, Zeyang Li, Jia Zhang

“…The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. …”
Get full text