Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model by Seunghee Lee, Geonchul Shin, Jeongseok Hwang, Yunjeong Hwang, Hyunwoo Jang, Ju Han Park, Sunmi Han, Kyeongmin Ryu, Jong-Yeup Kim

“…In this study, we developed a predictive model for the early detection of sepsis by leveraging advanced machine learning techniques, specifically the Gated Recurrent Unit (GRU-D) and Multitask Gaussian Process-Temporal Convolutional Network (MGP-TCN) models. This newly developed model demonstrated improved performance compared to existing results, with an area under the precision-recall curve of 0.965 (0.710) from 0.689 (0.432) and an area under the receiver operating characteristic curve of 0.994 (0.924) from 0.915 (0.828). …”
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Multisensor Fusion and Deep Learning Approaches for Semantic Segmentation of Glacial Lakes: A Comparative Study for Coastal Hydrology Applications by Lingling Xue, Asad Khan, Muhammad Haseeb, Mourad Aqnouy, Dawood Ahmad, Refka Ghodhbani, Dmitry E. Kucher, Olga D. Kucher

“…This study evaluates three deep learning architectures, U-Net, simple convolutional neural network (CNN), and atrous spatial pyramid pooling SegNet (ASPP SegNet), for binary semantic segmentation of glacial lakes using multisensor optical satellite imagery (Sentinel-2). …”
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Research on rock burst prediction based on an integrated model by Junming Zhang, Qiyuan Xia, Hai Wu, Sailei Wei, Zhen Hu, Bing Du, Yuejing Yang, Huaixing Xiong

“…The model integrates the local feature extraction capability of convolutional neural networks (CNN), the temporal modeling advantages of the modified long short-term memory network (MoLSTM), and the enhanced feature recognition capability of the attention mechanism. …”
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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. …”
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Analyzing the learning behavior patterns of business english learners using deep learning technology by Xiaohui Zeng

“…First, it applies a hybrid deep learning approach, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model both static and temporal aspects of learning behaviors. …”
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Research Progress and Technology Outlook of Deep Learning in Seepage Field Prediction During Oil and Gas Field Development by Tong Wu, Qingjie Liu, Yueyue Wang, Ying Xu, Jiale Shi, Yu Yao, Qiang Chen, Jianxun Liang, Shu Tang

“…This paper systematically reviews the development history of seepage field prediction methods and focuses on the typical models and application paths of Deep Learning in this field, including FeedForward Neural networks, Convolutional Neural Networks, temporal networks, Graphical Neural Networks, and Physical Information Neural Networks (PINNs). …”
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AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms by Chuanyan Wu, Bentao Lin, Jialin Zhang, Rui Gao, Rui Song, Zhi-Ping Liu

“…Spatial characteristics of proteins are obtained from the protein-protein interaction (PPI) network by employing Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). …”
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Estimating Gait Speed in the Real World With a Head-Worn Inertial Sensor by Paolo Tasca, Francesca Salis, Samanta Rosati, Gabriella Balestra, Claudia Mazza, Andrea Cereatti

“…This study aimed at validating a two-steps machine learning method to estimate initial contacts and stride-by-stride speed in real-world gait using a single inertial sensor attached to the temporal region. A convolutional network is used to detect strides. …”
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Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting by Xiao Li, Xiao Chen, Rui Fu, Xiao Hu, Mintong Chen, Kun Niu

“…The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Methods The framework comprises six interconnected components: (1) handcrafted feature extraction encoding biochemical and physicochemical attributes; (2) an embedding layer for dense sequence representation; (3) a Convolutional Neural Network (CNN) branch that captures local patterns from handcrafted features; (4) a Long Short-Term Memory branch for learning temporal dependencies in sequence data; (5) a scaled dot-product attention-based fusion module that integrates complementary information from both branches; and (6) a Multi-Layer Perceptron for final classification. …”
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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings by Chuanxiang Ren, Chunxu Chai, Changchang Yin, Haowei Ji, Xuezhen Cheng, Ge Gao, Heng Zhang

“…In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. …”
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A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition by David Hollinger, Ryan S. Pollard, Mark C. Schall, Howard Chen, Michael Zabala

“…K-nearest neighbors (KNN), bidirectional long short-term memory (BiLSTM), and temporal convolutional network (TCN) models were employed for action-level classification, and a random forest model trained on action-specific IMU data was used for joint-level prediction. …”
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Attention-Module-Guided Time-Lapse Leakage Plume Imaging Driven by LeakInv-CUNet GPR Inversion Framework by Honghua Wang, Shan Wang, Fei Zhou, Yi Lei, Bin Zhang

“…This paper develops LeakInv-CUNet, a novel attention-guided GPR inversion framework, to enable refined imaging of leakage plumes and their temporal-spatial evolution. To enhance network training, extensive GPR datasets are generated by augmenting simulated data and experimentally measured data, accounting for variations in injection orientation, plume dynamics, and subsurface media properties. …”
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Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature by Bosen Peng, Jiancheng Mu, Feng Xu, Wanyue Guo, Chuhuan Sun, Wei Fan

“…Key research hot spots are identified by keywords such as “deep learning,” “machine learning,” “convolutional neural network,” ”diabetic retinopathy,“ and ”ophthalmology.…”
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CoastVisionNet: transformer with integrated spatial-channel attention for coastal land cover classification by Li Yang, Liu Yijun, Wenhao Deng

“…While traditional convolutional neural networks and fixed-resolution transformer models have made notable strides, they often struggle to generalize across varying topographies and spectral distributions. …”
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Enhancing vector control: AI-based identification and counting of Aedes albopictus (Diptera: Culicidae) mosquito eggs by Minghao Wang, Yibin Zhou, Shenjun Yao, Jianping Wu, Minhui Zhu, Linjuan Dong, Dunjia Wang

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A Dual-Camera Eye-Tracking Platform for Rapid Real-Time Diagnosis of Acute Delirium: A Pilot Study by Ahmed Al-Hindawi, Marcela Vizcaychipi, Yiannis Demiris

“…We divided the collected data into training and validation cohorts based on the data originating center. We trained two Temporal Convolutional Network (TCN) models that can classify delirium using a pre-existing manual scoring system (Confusion Assessment Method in ICU (CAM-ICU)) as the training target. …”
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Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos by Chunhua Pan, Boting Qu, Rui Miao, Xin Wang

“…Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. …”
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Experimental Study on Heat Transfer Performance of FKS-TPMS Heat Sink Designs and Time Series Prediction by Mahsa Hajialibabaei, Mohamad Ziad Saghir

“…To further enhance the experimental process, a deep learning model based on a Temporal Convolutional Network (TCN) was developed to predict steady-state surface temperatures using early-stage time-series data, to reduce test time and enable efficient validation.…”
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Radar-Based Hand Gesture Recognition With Feature Fusion Using Robust CNN-LSTM and Attention Architecture by Irshad Khan, Young-Woo Kwon

“…This article introduces a novel deep learning approach for hand gesture recognition, leveraging convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms. …”
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