Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long‐Term Observations at ARM Eastern North Atlantic Site by Jingjing Tian, Jennifer Comstock, Andrew Geiss, Peng Wu, Israel Silber, Damao Zhang, Parvathi Kooloth, Ya‐ Chien Feng

“…Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U‐Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground‐based radar measurements. …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan Sulaiman, Zuriani Mustaffa

“…This paper presents an innovative approach using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model optimized by the Barnacles Mating Optimizer (BMO). …”
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EEG-Based Multi-Level Mental State Classification Using Partial Directed Coherence and Graph Convolutional Networks: Impact of Binaural Beats on Stress Mitigation by Yara Badr, Fares Al-Shargie, M. N. Afzal Khan, Nour Faris Ali, Usman Tariq, Fadwa Almughairbi, Fabio Babiloni, Hasan Al-Nashash

“…Utilizing EEG signals, graph convolutional neural networks (GCNs), and binaural beats stimulation (BBs), the research investigates stress detection and reduction in two population sample groups with distinct baselines (group 1: low daily baseline, and group 2: stressed daily baseline). …”
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Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

“…This paper introduces a new method for detecting breast cancer using a one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM). …”
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A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network by Kok Poh Wai, Chai Hoon Koo, Yuk Feng Huang, Woon Chan Chong, Ahmed El-Shafie, Mohsen Sherif, Ali Najah Ahmed

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Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

“…Advanced deep learning architectures such as convolutional neural networks (CNNs), deep CNNs (DCNNs), and recurrent neural networks (RNNs) are utilized to identify critical spatial and temporal patterns in the data. …”
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Passive forensic based on spatio-temporal localization of video object removal tampering by Linqiang CHEN, Quanxin YANG, Lifeng YUAN, Ye YAO, Zhen ZHANG, Guohua WU

“…To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.…”
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A Global Deep Learning Perspective on Australia-Wide Monthly Precipitation Prediction by Luyi Shen, Guoqi Qian, Antoinette Tordesillas

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Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models by Yuzhou Lin, Ramaswamy Palaniappan, Philippe De Wilde, Ling Li

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Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning by Yinghan Wu, Gang Mei, Kaixuan Shao, Nengxiong Xu, Jianbing Peng

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Incorporated flexible load forecasting based on non-intrusive load monitoring: a TCN-based meta learning approach by Yun Zhang, Quanyan Shu, Feng Ding, Feng Liu, Shuiming Jiang, Wenlong Wu

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Unpaired Spatio-Temporal Fusion for Remote Sensing Images via Deformable Global-Local Feature Alignment by Xinlan Ding, Huihui Song, Xu Zhang

“…To solve the above problems, we propose the deformable global-local feature alignment network (DGFANet) for unpaired spatio-temporal fusion, which combines convolutional neural network and transformer to enhance texture and semantic details through global-local alignment. …”
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DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes by Wei Zhang, Zeqi Xu, Ruochen Yu, Mingfeng Jiang, Qi Dai

“…This DualGCN-GE method has proposed dual-view graph convolution network(GCN) to integrate temporal and topological features underlying whole-blood expression data, thus detecting PD-PACE subtypes. …”
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Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model by Shaofan Liu, Tianbao Xie, Yanxin Li, Siyu Liu

“…To address these challenges, this paper proposes a deep learning-based fault detection model that integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory (CNN-LSTM) network. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…This work presents Air Quality Network (AirQuaNet), a novel convolutional neural network (CNN) designed to predict air quality levels accurately. …”
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Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition by Xin Guo, Young Kim, Xueli Ning, Se Dong Min

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Athlete movement control training based on dynamic time warping algorithm by Luo Dai, Yueqiang Zhao

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Stock index forecasting based on TBA fusion model by HAN Di, GUO Wei, LIAO Kai, SUN Chuanyi, WANG Bocheng, LIN Kunling

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Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises by Shaohui Zhang, Qiuying Han, Peng Wang, Junjie Li

“…Moreover, they lack the capacity to assess motion quality based on diverse temporal characteristics. To address these challenges, we propose a Frame Topology Fusion Hierarchical Graph Convolution Network (FTF-HGCN). …”
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Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction by Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang

Subjects: “…Keywords: Traffic demand prediction, Similarity-based Principle, Spatio-temporal Graph Convolutional Neural Network model; activity-based geographic information; prediction of bicycle sharing ridership.…”
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