Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting by Yuyang Zhang, Lei Cui, Wenqiang Yan

“…Electricity demand is driven by a diverse set of factors, including fluctuations in business cycles, interregional dynamics, and the effects of climate change. Accurately quantifying the impact of these factors remains challenging, as existing methods often fail to address the complexities inherent in these influences. …”
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Research on the Application of Reinforcement Learning in Traffic Flow Prediction by Hu Yiquan

“…Additionally, the article discusses the application of RL-based Long Short-Term Memory Networks, Graph Convolutional Networks (GCN), and Dynamic GCN in TFP. …”
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A deep neural network with attention mechanism for flow prediction of compressor blade by Guanyu Gao, Gang Wang

“…Abstract For flow-related design optimization problems, computational fluid dynamics (CFD) simulations are commonly used to predict the flow fields. …”
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A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints by Dongcan Xu, Yahao Liu, Yuan Kong

“…This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). …”
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Deep learning-based object detection for environmental monitoring using big data by Wenbo Lin, Tingting Li, Xiao Li

“…EGAN constructs a spatiotemporal graph representation that integrates physical proximity, ecological similarity, and temporal dynamics, and applies graph convolutional encoders to learn expressive spatial features. …”
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Autonomous Maneuver Decision of UCAV Air Combat Based on Double Deep Q Network Algorithm and Stochastic Game Theory by Yuan Cao, Ying-Xin Kou, Zhan-Wu Li, An Xu

“…Firstly, through the UCAV dynamics model and maneuver library, a reasonable air combat situation assessment model and advantage reward function are established, and the sample data of situation assessment indicators are constructed using the structure entropy weight method. …”
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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer by Chengdong Wang, Jianming Wang, Wenbo Gao, Lei Guo

“…This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. …”
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A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes by Jong-Min Kim

“…In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. …”
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Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions by Eui-Jin Kim, Ho-Chul Park, Seung-Woo Ham, Seung-Young Kho, Dong-Kyu Kim

“…Obtaining the trajectories of all vehicles in congested traffic is essential for analyzing traffic dynamics. To conduct an effective analysis using trajectory data, a framework is needed to efficiently and accurately extract the data. …”
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Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems by Said A. Salloum, Tarek Gaber, Mohammed Amin Almaiah, Rami Shehab, Romel Al-Ali, Theyazan H.H Aldahyani

“… In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. …”
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Improving Wind Turbine Gearbox Reliability: A Hybrid Deep Learning Approach Using Bispectrum Image Analysis by Muhammad Zacky Asy'Ari, Jun Kondoh, Benyamin Kusumoputro

“…This study explores the potential of bispectrum image analysis combined with Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (LSTM), and CNN-Bidirectional LSTM networks for enhanced fault detection and predictive maintenance for wind turbine systems. …”
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MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting by Sunlei Qian, Xiaorong Zhu

“…., periodic, topological, and transient dynamics). To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. …”
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Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model by Shanshan Wang, Jingwu Liu

“…IntroductionThis study investigates the potential of a deep learning-based Life Log Sharing Model (LLSM) to enhance adolescent physical fitness and exercise behaviors through personalized public health interventions.MethodsWe developed a hybrid Temporal–Spatial Convolutional Neural Network-Bidirectional Long Short-Term Memory (TS-CNN-BiLSTM) model. …”
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A soil organic carbon mapping method based on transfer learning without the use of exogenous data by Jingfeng Han, Mujie Wu, Yanlong Qi, Xiaoning Li, Xiao Chen, Jing Wang, Jinlong Zhu, Qingliang Li

“…Accurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. …”
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Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach by Marvin H. Cheng, Po-Lin Huang, Hao-Chuan Chu

“…To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. …”
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Complex Network Analytics for Structural–Functional Decoding of Neural Networks by Jiarui Zhang, Dongxiao Zhang, Hu Lou, Yueer Li, Taijiao Du, Yinjun Gao

“…This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. …”
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Using artificial intelligence tools to predict and alleviate poverty by Ghada Alturif, Wafaa Saleh, Alaa A. El-Bary, Radwa Ahmed Osman

“…A one-dimensional Convolutional Neural Network (1D-CNN) is used in the model to examine and extract patterns from historical socio-economic data from 2000 to 2022. …”
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Evaluation of Different Generative Models to Support the Validation of Advanced Driver Assistance Systems by Manasa Mariam Mammen, Zafer Kayatas, Dieter Bestle

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The use of artificial intelligence-based Siamese neural network in personalized guidance for sports dance teaching by Yi Xie, Yao Yan, Yuwei Li

“…First, a human skeletal graph is constructed. A graph convolutional network (GCN) is employed to extract features from the nodes (joints) and edges (bone connections) in the graph structure, capturing both spatial relationships and temporal dynamics between joints. …”
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Multi-scale time series prediction model based on deep learning and its application. by Zhifei Yang, Jia Zhang, Zeyang Li

“…To improve the accuracy of time series prediction, the paper proposes a multiscale convolutional attention long short-term memory model (MSCALSTM), which combines a multiscale convolutional neural network (MSCNN), a multiscale convolutional block attention module (MSCBAM) and LSTM. …”
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