A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network by Qinghao Zhang, Miao Ye, Xiaofang Deng

“…The simulation results obtained on a public dataset show that the proposed approach can significantly improve upon existing methods interms of robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with longshort-term memory (LSTM).…”
Get full text

Deep learning-based time series prediction for precision field crop protection by Tao He, Meijin Li, Dong Jin

“…By combining convolutional layers for spatial feature extraction, recurrent neural networks for temporal modeling, and attention mechanisms for data fusion, SADF-Net captures intricate spatial-temporal dependencies while ensuring robustness to noisy and incomplete data. …”
Get full text

STGATN: A novel spatiotemporal graph attention network for predicting pollutant concentrations at multiple stations. by Huazhen Xu, Wei Song, Lanmei Qian, Xiangxiang Mei, Guojian Zou

“…The gated temporal convolutional network is constructed to capture the long-term temporal causal relationships. …”
Get full text

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, the CMS-HCAL Collaboration

“…In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. …”
Get full text

A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…It efficiently and intelligently detects and manages security data in digital library network. The method combines the structures of temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to extract spatial and temporal features from digital library network security data. …”
Get full text

Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

“…Spatial and temporal features have been extracted sequentially in deep learning-based models using convolutional neural networks (CNN) followed by recurrent neural networks (RNN) which may not only be weak at the detection of the separate spatial-temporal feature representations but also the semantic tendencies in speech. …”
Get full text

An attention based hybrid approach using CNN and BiLSTM for improved skin lesion classification by Ayesha Shaik, Shivanya Shomir Dutta, Ishaan Milind Sawant, Shreyas Kumar, Ananthakrishnan Balasundaram, Kanjar De

“…This work explores the integration of advanced Convolutional Neural Networks (CNNs) with Bidirectional Long Short Term Memory (BiLSTM) enhanced by spatial, channel, and temporal attention mechanisms to improve the classification of skin lesions. …”
Get full text

Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data by Bas Peters, Eldad Haber, Keegan Lensink

“…The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentation. …”
Get full text

Spatiotemporal fusion knowledge tracking model based on spatiotemporal graph and fourier graph neural network by Yinquan Liu, Weidong Ji, Guohui Zhou

“…Current state-of-the-art Graph Neural Network (GNN)-based methods typically require spatial networks (e.g., Graph Convolutional Network) to capture static spatial dependencies between knowledge points and temporal networks (e.g., Long Short-Time Memory) to model local temporal dependencies in the learning sequence. …”
Get full text

Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…However, baseline Convolutional Neural Network (CNN) commonly faced obstacles to fully capture the temporal dependencies present in sequential medical imaging data, limiting their diagnostic performance. …”
Get full text

A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction by Wei Chen, Zongping Li, Can Liu, Yi Ai

“…Firstly, we use automatic fare collection (AFC) system data to evaluate the congestion delays of stations. Then, we use a convolutional long short-term memory (Conv-LSTM) network to extract spatial and temporal characteristics to solve the short-term prediction problem of the subway congestion delay in the network structure. …”
Get full text

Key method of digitization of power distribution panel with artificial intelligence identification for power communication network by LIU Lin, YANG Zhimin, HUANG Qiang, YANG Jingwei, CHEN Yitong

“…Finally, a text recognition model targeting the labels of subordinate branches in the distribution panel was constructed on the basis of the convolutional recurrent neural network-connectionist temporal classification (CRNN-CTC). …”
Get full text

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. …”
Get full text

Bangla Speech Emotion Recognition and Cross-Lingual Study Using Deep CNN and BLSTM Networks by Sadia Sultana, M. Zafar Iqbal, M. Reza Selim, Md. Mijanur Rashid, M. Shahidur Rahman

“…The system combines a deep convolutional neural network (DCNN) and a bidirectional long-short term memory (BLSTM) network with a time-distributed flatten (TDF) layer. …”
Get full text

MFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images by Honghui Jiang, Tingting Luo, Hu Peng, Guozheng Zhang

“…Rotated object detection in remote sensing images presents a highly challenging task due to the extensive fields of view and complex backgrounds. While Convolutional Neural Networks (CNNs) and Transformer networks have made progress in this area, there is still a lack of research on extracting and fusing features for small targets in complex backgrounds. …”
Get full text

Multimodal Fusion Multi-Task Learning Network Based on Federated Averaging for SDB Severity Diagnosis by Songlu Lin, Renzheng Tang, Yuzhe Wang, Zhihong Wang

Get full text

Investigation into the Prediction of Ship Heave Motion in Complex Sea Conditions Utilizing Hybrid Neural Networks by Yuchen Liu, Xide Cheng, Kunyu Han, Zhechun Liu, Baiwei Feng

“…Consequently, this paper proposes a hybrid neural network method that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory Networks (BiLSTMs), and an Attention Mechanism to predict the heaving motion of ships in moderate to complex sea conditions. …”
Get full text

BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. …”
Get full text

Damage Identification of Conduit Rack in Offshore Platform Structures Based on a Novel Composite Neural Network by Jiaqiang Yan, Yuanchao Qiu, Renhe Shao, Ziqiao Ling, Ruixiang Zhang

“…First, the temporal convolutional network (TCN) breaks through the localisation of traditional convolutional neural networks in modelling the temporal dimension by efficiently extracting the long-term time since of the structural vibration response through an expansive causal convolution mechanism. …”
Get full text

Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…Our architecture employs a Convolutional Neural Network (CNN) with Inception-V3 as the foundational model for initial feature extraction. …”
Get full text