DTCMMA: Efficient Wind-Power Forecasting Based on Dimensional Transformation Combined with Multidimensional and Multiscale Convolutional Attention Mechanism by Wenhan Song, Enguang Zuo, Junyu Zhu, Chen Chen, Cheng Chen, Ziwei Yan, Xiaoyi Lv

“…Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. …”
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A multimodal hybrid parallel network intrusion detection model by Shuxin Shi, Dezhi Han, Mingming Cui

“…Firstly, a two-branch convolutional neural network is combined with Long Short-Term Memory (LSTM) network to extract the spatio-temporal feature information of network traffic from the original load mode of traffic, and a convolutional neural network is used to extract the feature information of traffic statistics. …”
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Predicting traffic flow with federated learning and graph neural with asynchronous computations network by Muhammad Yaqub, Shahzad Ahmad, Malik Abdul Manan, Muhammad Salman Pathan, Lan He

“…In this article, we present a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN). Our framework incorporates the principles of asynchronous graph convolutional networks with federated learning to enhance the accuracy and efficiency of real-time traffic flow prediction. …”
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Performance Comparison Between Deep Learning Models for Fault Classification in Transmission Lines Using Time Series Data by Nadeem Ahmed Tunio, Mohsin Ali Tunio, Muhammad Amir Raza, Muhammad Faheem, Ashfaque Ahmed Hashmani, Rumaisa Nadeem

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Tillage and biomass detection for estimating winter-time cropland management practices with satellite remote sensing by Maria Yli-Heikkilä, Arto Klami, Samantha Wittke, Markku Luotamo, Pinja Mero, Petri Pellikka, Janne Heiskanen, Mwaba Hiltunen, Kari Luojus, Golda Prakasam, Mikko Strahlendorff, Markus Törmä, Mi Sulkava

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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

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Multistage fall detection framework via 3D pose sequences and TCN integration by Leitao Qi, Haibo Sun

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Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

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The singing style of female roles in ethnic opera under artificial intelligence and deep neural networks by Huixia Yang

“…This work focuses on the classification of singing styles of female roles in ethnic opera and proposes an Attention-Enhanced 1D Residual Gated Convolutional and Bidirectional Recurrent Neural Network (ARGC-BRNN) model. …”
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Application of a Neural Network Model to Short-Term Water Demand Forecasting by Faten Ayyash, Matthew Hayslep, Taegon Ko, Mulenga Kalumba, Kondwani Simukonda, Raziyeh Farmani

“…This study addresses the challenges of forecasting short-term water demand and was part of the Battle for Water Demand Forecasting competition involving 10 real-world District Metered Areas in Italy. A nine-layer convolutional neural network model was proposed that considers demand from previous time steps, time of the day, weather conditions, day type, and other deterministic temporal factors to predict water demand. …”
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A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster by Tong Yang, Minan Tang, Hanting Li, Hongjie Wang, Chuntao Rao

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Rolling Bearing Fault Diagnosis Based on Recurrence Plot by Zheming Chen, Bin Xu, Zhong Zhang

“…For the prediction model, the traditional convolutional neural network is enhanced by integrating bidirectional gated recurrent unit and multi-head attention mechanism, allowing it to capture temporal features alongside the spatial features typically extracted by convolutional neural network. …”
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Speech Emotion Recognition Using Multi-Scale Global–Local Representation Learning with Feature Pyramid Network by Yuhua Wang, Jianxing Huang, Zhengdao Zhao, Haiyan Lan, Xinjia Zhang

“…To address these challenges, this paper proposes a novel multi-scale feature pyramid network. The enhanced multi-scale convolutional neural networks (MSCNNs) significantly improve the ability to extract multi-granular emotional features. …”
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Fish Body Pattern Style Transfer Based on Wavelet Transformation and Gated Attention by Hongchun Yuan, Yixuan Wang

“…To address the temporal jitter with low segmentation accuracy and the lack of high-precision transformations for specific object classes in video generation, we propose the fish body pattern sync-style network for ornamental fish videos. …”
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Pedestrian Trajectory Prediction via Window Attention and Spatial Graph Interaction Network by Xiang Gu, Chao Li, Jie Yang, Jing Wang, Qiwei Huang

“…In the spatial dimension, a hierarchical heterogeneous GCN (graph convolutional network) is constructed, combining pedestrian dynamic interaction graphs and scene semantic static graphs. …”
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RegCDNet: A RegNet-Based Framework for Remote Sensing Image Change Detection Combining Feature Enhancement and Gating Mechanism by Chuanlu Li, Xiaorong Xue, Caijia Zeng, Yifan Xu, Xingbiao Xu, Siyue Zhao

“…The model employs RegNet as the backbone network for feature extraction, using a simple and efficient strategy to fuse shallow features. …”
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VBCNet: A Hybird Network for Human Activity Recognition by Fei Ge, Zhenyang Dai, Zhimin Yang, Fei Wu, Liansheng Tan

“…Meanwhile, VBCNet employs a convolutional feed-forward network instead of the traditional feed-forward network to enhance the model’s ability to process local and multi-scale features. …”
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Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning by Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović

“…We compare feature-based classifiers applied to VDF moments, such as support vector machine and random forest (RF), with DL convolutional neural networks (CNNs) applied directly to VDFs as images in the gyrotropic velocity plane. …”
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Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…However, most of the current methods depend on data preprocessing and feature engineering, often require domain knowledge and laborious human involvement, and require additional manual adjustments when dealing with new tasks.MethodsTo reduce the model's reliance on data preprocessing, feature engineering, and traversal rules, we employed the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model. …”
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AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection by Rui Li, Xueyi Song, Yunmei Wu, Xubo Yu, Hua Huang

“…Specifically, we employ convolutional KAN, which combines the advantages of KAN and convolutional neural network (CNN) to capture the short-term temporal correlations for distinct sensor features. …”
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