Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences by Zhongcong Ding, Xuehui An

“…The proposed model integrates features of the convolutional neural network and long short-term memory and is trained to extract features and compute an estimate. …”
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

“…We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network. …”
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Research on geomagnetic indoor high-precision positioning algorithm based on generative model by Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI

“…Aiming at the current bottleneck of constructing a fine geomagnetic fingerprint library that required a lot of labor costs, two generative models called the conditional variational autoencoder and the conditional confrontational generative network were proposed, which could collect a small number of data samples for a given location, and generate pseudo-label fingerprints.At the same time, in order to solve the problem of low positioning accuracy of single-point geomagnetic fingerprints, a geomagnetic sequence positioning algorithm based on attention mechanism of convolutional neural network-gated recurrent unit was designed, which could effectively use the spatial and temporal characteristics of fingerprints to achieve precise positioning.In addition, a real-time, portable mobile terminal data collection and positioning system was also designed and built.The actual test shows that the proposed model can effectively construct the available geomagnetic fingerprint database, and the average error of the proposed algorithm can reach 0.16 m.…”
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Radio frequency fingerprint data augmentation for indoor localization based on diffusion model by Haojun AI, Weike ZENG, Jingjie TAO, Jinying XU, Hanxiao CHANG

“…The radio frequency fingerprint indoor localization method ensures the accuracy by collecting a sufficient amount of fingerprints in the offline state to build a dense fingerprint database.A data augmentation method called FPDiffusion was proposed based on diffusion model to reduce the cost of fingerprint acquisition.Firstly, a temporal graph representation of the fingerprint sequence was constructed, the forward process of the diffusion model was accomplished by adding Gaussian noise, and a U-Net was utilized for the reverse process.The loss function of the network was designed according to the characteristics of radio frequency fingerprints.Finally, the computational process for generating dense fingerprints based on sparse fingerprints was presented.Experimental results demonstrate that FPDiffusion achieves 76% and 28% localization error reduction on K-nearest neighbor (KNN) and convolutional neural network (CNN) respectively, and significantly improves localization accuracy on KNN compared to Gaussian process regression (GPR) and GPR-GAN when only a small amount of labeled fingerprints is available.…”
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A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms by Bo Wang, Shu Wang, Zheng Wang, Yingying Zheng, Xin Li

“…Finally, a hybrid deep learning model, the convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) network-attention mechanism (AM), is constructed. …”
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Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation by Huimei Duan, Chenggang Guo, Yuan Ou

“…The coordinate attention gate in conjunction with the convolutional gate enable the network to model feature information spatially, temporally, and internally across channels. …”
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Deterministic reservoir computing for chaotic time series prediction by Johannes Viehweg, Constanze Poll, Patrick Mäder

“…Building upon Next Generation Reservoir Computing and the Temporal Convolution Derived Reservoir Computing, we propose a deterministic alternative to the higher-dimensional mapping therein, TCRC-LM and TCRC-CM, utilizing the parameterized but deterministic Logistic mapping and Chebyshev maps. …”
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Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG by Mingjie Yan, Zhe Chen, Jianmin Li, Jinhua Li, Lizhi Pan

“…To decode these signals, a convolutional neural network (CNN) incorporating the global attention mechanism was established, named global attention convolutional neural network (GACNN). …”
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Deep Learning Based DDoS Attack Detection by Xu Ziyi

“…In order to improve detection accuracy and generalization, this research suggests a deep learning-based detection model that combines the Long Short-Term Memory (LSTM) network architecture with Convolutional Neural Networks (CNN). …”
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Detecting command injection attacks in web applications based on novel deep learning methods by Xinyu Wang, Jiqiang Zhai, Hailu Yang

“…The model utilizes dual CNN convolutional channels for comprehensive feature extraction and employs a BiLSTM network for bidirectional recognition of temporal features. …”
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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Existing trajectory prediction studies predominantly employ recurrent neural network (RNN) and Transformer-based methods. However, the former often encounter challenges such as gradient vanishing or exploding, and the latter tend to focus on global temporal dependencies, making it difficult to capture local irregular trajectory features in coastal maritime areas. …”
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Development and evaluation of deep learning models for cardiotocography interpretation by Nicole Chiou, Nichole Young-Lin, Christopher Kelly, Julie Cattiau, Tiya Tiyasirichokchai, Abdoulaye Diack, Sanmi Koyejo, Katherine Heller, Mercy Asiedu

“…Using a published convolutional neural network (CNN), we predict fetal compromise from CTG recordings, incorporating pre-processing and hyperparameter tuning. …”
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Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application by Heesang Eom, Younghun Kim, Jongho Paik

“…To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units for temporal sequence modeling. …”
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Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models by Muhammad Waqas, Usa Wannasingha Humphries

“…The research evaluates the performance of artificial intelligence (AI)-driven downscaling techniques (Dynamic Neural Network with Memory (DyNN-Mem) and Hybrid Long Short-Term Memory Convolutional Neural Network (LSTM-CNN)) for scaling down CMIP6 Global Climate Models (GCMs) daily precipitation outputs across Thailand. …”
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Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data by He Bing, Xu Zhifeng, Xu Yangjie, Hu Jinxing, Ma Zhanwu

“…Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. …”
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Group Abnormal Behaviour Detection Algorithm Based on Global Optical Flow by Yu Hao, Ying Liu, Jiulun Fan, Zhijie Xu

“…Following, in order to realize the detection of large displacement moving target and solve the problem that the traditional optical flow algorithm is only suitable for the detection of displacement moving target, a video abnormal behaviour detection algorithm based on the double-flow convolutional neural network is proposed. The network uses two network branches to learn spatial dimension information and temporal dimension information, respectively, and uses short- and long-time neural network to model the dependency relationship between long-time video frames, so as to obtain the final behaviour classification results. …”
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A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar by Zisheng Li, Ken Chen, Yaoqin Xie

“…To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. …”
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Mifu-ER: Modality Quality Index-Based Incremental Fusion for Emotion Recognition by Sun-Hee Kim

“…Mifu-ER integrates three aspects: i) application of the sliding window method to ensure fast processing speed and a sufficient training dataset, ii) incremental fusion of physiological data by finding the MQI between modalities on the segmented dataset, and iii) emotion recognition by applying the fused multimodal dataset to the temporal convolution network (TCN) learning model. …”
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Research on Change Information Retrieval Method for Energy Infrastructure Based on Optical Remote Sensing Images by Zhibao Wang, Ying Yuan, Man Zhao, Dan Zhou, Lu Bai, Jinhua Tao, Anna Jurek-Loughrey

“…To address this, we propose a novel framework that integrates change detection with image retrieval, focusing specifically on the change information in bi-temporal remote sensing images. Our model, SCanNet-CDH (Semantic Change Network and Convolutional Deep Hashing), integrates convolutional neural networks with Transformer architecture, utilizing multi-scale features from ResNet-34 and enhancing them with the CSWin Transformer for improved global feature modeling. …”
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Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN by Haijun Liu, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu, Yalan Li

“…The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. …”
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