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

“…The data feature extraction ability of CNNs, the temporal analysis capabilities of BiLSTMs, and the dynamic adjustment function of Attention on feature weights were comprehensively utilized to predict a ship’s heave motion. …”
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AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients by Qiuyue Li, Kewei Li, Cong Fu, Yiyuan Zhang, Huan Yu, Chen Chen, Wei Chen

“…Traditional single-event detection methods often overlook the dynamic interactions between SDB and LM, failing to capture their temporal overlap and differences in duration. …”
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Automated Models for Predicting Software Defects in Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) Parallel Programs Using Deep Learning by Amani Saad Althiban, Hajar M. Alharbi, Lama A. Al Khuzayem, Fathy Elbouraey Eassa

“…Using a balanced dataset of 1,500 C++ files, three neural architectures—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model—were evaluated. …”
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RainHCNet: Hybrid High-Low Frequency and Cross-Scale Network for Precipitation Nowcasting by Lei Wang, Zheng Wang, Wenjun Hu, Cong Bai

“…Recent advancements in deep learning have led to the development of radar echo extrapolation methods. However, most convolutional neural network-based methods focus primarily on high-frequency information, neglecting essential low-frequency cues necessary for forecasting high-intensity rainfall. …”
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A Study on the Lightweight and Fast Response GRU Techniques for Indoor Continuous Motion Recognition Based on Wi-Fi CSI by Kyongseok Jang, Chao Sun, Junhao Zhou, Yongbin Seo, Youngok Kim, Seyeong Choi

“…In this study, we propose and investigate a novel lightweight and fast-response gated recurrent unit (LFR-GRU) model that can distinguish continuous dynamic and static postures over time of a person without electronic devices in an indoor environment by utilizing the propagation characteristics of Wi-Fi signals. …”
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Photon–photon chemical thermodynamics of frequency conversion processes in highly multimode systems by Huizhong Ren, Georgios G. Pyrialakos, Qi Zhong, Fan O. Wu, Mercedeh Khajavikhan, Demetrios N. Christodoulides

“…Abstract Frequency generation in highly multimode nonlinear optical systems is inherently a complex process, giving rise to an exceedingly convoluted landscape of evolution dynamics. While predicting and controlling the global conversion efficiencies in such nonlinear environments has long been considered impossible, here, we formally address this challenge even in scenarios involving a very large number of spatial modes. …”
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Joint Adaptive Resolution Selection and Conditional Early Exiting for Efficient Video Recognition on Edge Devices by Qingli Wang, Chengwu Yu, Shan Chen, Weiwei Fang, Naixue Xiong

“…Deep learning has shown its remarkable performance in video analytics, by applying 2D or 3D Convolutional Neural Networks (CNNs) across multiple video frames. …”
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Tidal Volume Monitoring via Surface Motions of the Upper Body—A Pilot Study of an Artificial Intelligence Approach by Bernhard Laufer, Tamer Abdulbaki Alshirbaji, Paul David Docherty, Nour Aldeen Jalal, Sabine Krueger-Ziolek, Knut Moeller

“…Subsequently, linear regression and a tailored convolutional neural network (CNN) were used to determine tidal volumes from an optimal set of motion parameters. …”
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YOLOv8-SDC: An Improved YOLOv8n-Seg-Based Method for Grafting Feature Detection and Segmentation in Melon Rootstock Seedlings by Lixia Li, Kejian Gong, Zhihao Wang, Tingna Pan, Kai Jiang

“…Specifically, the SAConv module dynamically adjusts the receptive field of convolutional kernels to enhance the model’s capability in extracting seedling shape features. …”
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Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado, João Manuel R. S. Tavares

“…In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. …”
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MBGPIN: Multi-Branch Generative Prior Integration Network for Super-Resolution Satellite Imagery by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Furkat Bolikulov, Shakhnoza Muksimova, Young-Im Cho

“…Traditional interpolation methods often fail to recover fine details, while deep-learning-based approaches, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly advanced super-resolution performance. …”
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ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction by Aneta Kolackova, Viet Anh Phan, Jan Jerabek, Sergey Andreev, Jiri Hosek

“…Traditional static energy management approaches frequently waste resources and lead to increased costs, highlighting the need for more dynamic methods that adapt to changing network conditions. …”
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Deep learning-based InSAR time-series deformation prediction in coal mine areas by Chuanzeng Shu, Zhiguo Meng, Ying Yang, Yongzhi Wang, Shanjun Liu, Xiaoping Zhang, Yuanzhi Zhang

“…This framework integrates a Transformer-encoder module, a Bi-LSTM-decoder module, and an innovative convolutional attention feature extraction module. It can effectively capture both global and key temporal features and dynamically model the interactions among multimodal data. …”
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A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM by Umesh Kumar Lilhore, Sarita Simaiya, Monish Khan, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi

“…We propose a novel model that incorporates class-aware attention weights, which dynamically modulate the focus of attention on input features according to their importance for a specific heart disease class. …”
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Identifying native grasslands and key phenological stages using time series Sentinel-2 data and deep learning models by Yihan Pu, Amy Nixon, Beatriz Prieto, Xulin Guo

“…However, the current amount, distribution, and dynamic changes of native grassland remain uncertain, partly due to the difficulty of separating native grassland with other land cover types, especially tame grassland in landcover classification products. …”
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AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images by Sen Wang, Huiping Jiang, Jixiang Yang, Xuan Ma, Jiamin Chen

“…This enables the convolutional kernels to effectively adapt to varying scales of ground targets, capturing more details while expanding the receptive field. …”
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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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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…By merging domain-specific handcrafted features with dynamic deep learning representations, the model not only enhances predictive accuracy but also provides meaningful biological insights. …”
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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…Finally, the disordered feature extraction (DFE) module was designed to specifically identify disordered regions of plant proteins and extract dynamic features to further enhance the accuracy of plant PepPI prediction. …”
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An In-Depth Study of Personalized Anesthesia Management Models in Gastrointestinal Endoscopy Based on Multimodal Deep Learning by Hanqi Shi, Hongyu Wang, Xibing Ding, Zheng Dang

“…The model is capable of dynamically adjusting its parameters based on the specific needs of each individual patient, utilizing real-time physiological data to predict vital signs and anesthesia states with a 10-second lead time. …”
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