A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…To identify the irregularities in the cardiac data pattern, a gated recurrent unit (GRU) classifier and a one-dimensional convolutional neural network (1D-CNN) are introduced. …”
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Long Term Trajectory Prediction of Hypersonic Aircraft Based on GRU-KAN by Su Yu, Zhang Longzhengteng, Zhao Guohong, Guo Zhengyu, Zhang Ke

“…Subsequently, the trajectory data is segmented and preprocessed using a sliding window to obtain the trajectory dataset. …”
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The Concept of Quantum Teleportation for Remote Control of a Car-like Mobile Robot by Joslin Numbi, Nadjet Zioui, Mohamed Tadjine

“…Before implementing this protocol, we calculate the quantum teleportation circuit, and we use quantum gates in matrix form to simulate the data teleportation process. …”
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DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction by Guohua Huang, Guohua Huang, Runjuan Xiao, Runjuan Xiao, Chunying Peng, Jinyun Jiang, Weihong Chen

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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

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Thermal Neutron Induced Capture and Fission Gamma-Ray Emission Measurements and Simulation Methods by Danon Yaron, Keparutis Katelyn, Parker Ian, Barry Devin

“…The experimental results were compared to simulations using a modified version of MCNP6.2 that can read capture gamma-ray cascades from a file, transport them through the detector geometry and output event-by-event energy deposition in each detector similarly to the processed experimental data. The Monte Carlo code, DICEBOX, was used to generate the capture gamma-ray cascades using ENSDF evaluated gamma-ray energies and intensities and RIPL-3 model parameters. …”
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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

“…The model offers a high level of interpretability, which is crucial for medical professionals to understand and trust the decision-making process of the system. The developed hybrid model outperforms various other state-of-the-art techniques like ANN, CNN, CNN-Bi-LSTM-GRU-AM (Convolutional Neural Network-Bidirectional Long Short-Term Memory-Gated Recurrent Unit-Attention Mechanism), and CNN-GRU (Convolutional Neural Network- Gated Recurrent Unit) in terms of accuracy, feature extraction and computational time. …”
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Viral particle prediction in wastewater treatment plants using nonlinear lifelong learning models by Jianxu Chen, Ibrahima N’Doye, Yevhen Myshkevych, Fahad Aljehani, Mohammad Khalil Monjed, Taous-Meriem Laleg-Kirati, Pei-Ying Hong

“…Abstract Predicting new unseen data using only wastewater process inputs remains an open challenge. …”
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Downscaling of Soil Moisture Map using Sentinel Radar Satellite Images and Distribution Analysis in the West of Iran by Seyed Hossein Mirmosavi, kohzad Raispour, Muhammad Kamangar

“…Simulated global system moisture data have been available since 1954, but the spatial resolution of this data is low. …”
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The Long-Term Surface Deformation Monitoring and Prediction of Hutubi Gas Storage Reservoir in Xinjiang Based on InSAR and the GWO-VMD-GRU Model by Wang Huang, Wei Liao, Jie Li, Xuejun Qiao, Sulitan Yusan, Abudutayier Yasen, Xinlu Li, Shijie Zhang

“…The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. …”
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FTLNet: federated deep learning model for multi-horizon wind power forecasting by Majad Mansoor, Gong Tao, Adeel Feroz Mirza, Balal Yousaf, Muhammad Irfan, Wei Chen

“…Our method integrates the sophisticated capabilities of Gated Residual Networks with a strategic process of feature selection, tailored for the unique demands of secure and efficient power forecasting. …”
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Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms by S. Jayanthi, Swathi Sowmya Bavirthi, P. Murali, K. Vijaya Kumar, Hend Khalid Alkahtani, Mohamad Khairi Ishak, Samih M. Mostafa

“…Furthermore, the feature selection process uses the RIME optimization algorithm (ROA) model to select the most relevant features from the data. …”
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Application of Machine Tool Thermal Error Compensation in Digital Twin-based System by MA Chi, LI Minging, LIU Jialan, HE Jialong, HUA Chunlei, WANG Liang

“…A novel prediction model utilizing a strong-convergence chimp optimization algorithm combined with a minimal gated unit-attention mechanism is proposed to characterize the dependence of the current thermal error on the historical thermal informant data, forming strong-convergence chimp optimization algorithm-minimal gated unit-attention ( SC-ChOA-MGU-A) model,ensuring data integrity. …”
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Multimodal Metaverse Healthcare: A Collaborative Representation and Adaptive Fusion Approach for Generative Artificial-Intelligence-Driven Diagnosis by Jianhui Lv, Adam Slowik, Shalli Rani, Byung-Gyu Kim, Chien-Ming Chen, Saru Kumari, Keqin Li, Xiaohong Lyu, Huamao Jiang

“…The framework’s adaptive fusion approach, utilizing attention mechanisms and gated neural networks, demonstrates robust performance across varying noise levels and data quality conditions. …”
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Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning by Yanli Zhao, Zongduo Liu, Junjie Pang

“…Only structural information with minimal sensitive content is transmitted to the central server, whereas sensitive node attributes are preserved and processed locally to enhance privacy protection. Furthermore, a cross-gated feature fusion mechanism is introduced to enhance the expressive interaction between features and to generate graph-level embeddings for anomaly classification. …”
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A Multi-Scale Fusion Convolutional Network for Time-Series Silicon Prediction in Blast Furnaces by Qiancheng Hao, Wenjing Liu, Wenze Gao, Xianpeng Wang

“…Validation using real blast furnace data demonstrates that MSF-CNN outperforms recurrent neural network models such as Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). …”
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Enhanced Rolling Bearing Fault Diagnosis Using Multimodal Deep Learning and Singular Spectrum Analysis by Yunhang Wang, Hongwei Wang, Ruoyang Bai, Yuxin Shi, Xicong Chen, Qingang Xu

“…First, key features are extracted from the multimodal signal set using singular spectrum analysis (SSA), and these features are transformed into a composite dataset that combines short-time Fourier transform (STFT) images and time series data. Based on this, a recursive gated convolutional neural network (RGCNN) is designed to process the STFT image data, while a 1D convolutional neural network (1DCNN) is specifically optimized for training with time series data. …”
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A Study of Tool Wear Prediction Based on Digital Twins by LIU Minghao, MAO Xinhui, XIA Wei, YUE Caixu, LIU Xianli

“…Through virtual-real interaction and data fusion mechanisms, it provides an engineering solution for the dynamic perception of tool wear during the milling process.…”
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Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact by Dildar Hussain, Naseem Abbas, Jawad Khan

“…The fusion of PET and CT technologies has revolutionized medical imaging, offering unprecedented insights into both anatomical structure and functional processes. The analysis delves into key technological innovations, including advancements in image reconstruction, data-driven gating, and time-of-flight capabilities, highlighting their impact on enhancing diagnostic accuracy and clinical outcomes. …”
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An Optimized Weighted-Voting-Based Ensemble Learning Approach for Fake News Classification by Muhammad Shahzaib Toor, Hooria Shahbaz, Muddasar Yasin, Armughan Ali, Norma Latif Fitriyani, Changgyun Kim, Muhammad Syafrudin

“…Cross-validation was employed to enhance the model’s trustworthiness, validating its capacity to generalize effectively to novel data.…”
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