A Hybrid Approach Integrating Decomposition Ensemble Forecasting With Optimal Combination Selection for Air Passenger Demand Forecasting by Yi-Chung Hu, Li-Chin Shih, Yu-Jing Chiu

“…The optimal combination selection from individual decomposition ensemble models was then used to construct combined models to strengthen the accuracy of decomposition ensemble forecasting. …”
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A theoretical study on the decomposition of TKX-50 with different vacancy defect concentrations under shock wave loading by Jun-qing Yang, Zhi-wei Guo, Xiao-he Wang, Ga-zi Hao, Yu-bing Hu, Xiao-jun Feng, Rui Guo, Wei Jiang

“…Afterward, the decomposition processes of these most stable models under shock waves at a speed of 10 km s−1 were examined in detail. …”
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Tensor Network Methods for Hyperparameter Optimization and Compression of Convolutional Neural Networks by A. Naumov, A. Melnikov, M. Perelshtein, Ar. Melnikov, V. Abronin, F. Oksanichenko

“…TetraOpt consistently demonstrated superior performance, effectively exploring the global optimization space and identifying configurations with higher accuracies. For model compression, we introduce a novel iterative method that combines CP, SVD, and Tucker tensor decompositions. …”
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Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer by Xiang Liu, Qingyu Liu, Shuai Feng, Yangyang Ge, Haoran Chen, Chunling Chen

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Economic Model Predictive Control for Wastewater Treatment Processes Based on Global Maximum Error POD-TPWL by Zhiyu Wang, Jing Zeng, Jinfeng Liu

“…The TPWL method constructs a reduced-order model framework, while GMEC iteratively refines the linearization point selection process. …”
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Research on sinusoidal load identification method under structural natural frequency excitation based on LSTM-CNN by HE Wenbo, SUN Hanyu, XIE Jiang, ZHANG Xiaoqiang

“…Addressing the challenge of low identification accuracy in traditional load identification methods based on the truncated singular value decomposition（TSVD）method，especially when the external load frequency approaches or reaches the natural frequency of the structure，the LSTM-CNN load identification model is proposed in this paper. …”
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A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. …”
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Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning by Yang GAO, Jun-li CHEN, Guang-li YANG

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SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing by Chao He, Chaofan Wang, Junwen Peng, Wenhui Jiang, Jing Liu

“…By utilizing Singular Value Decomposition (SVD) to decompose Long Short-Term Memory (LSTM) layer weights into two smaller matrices and adding a custom layer to the standard LSTM structure, the SVD-LSTM method reduces the dimensionality of weights in the input and intermediate layers, reducing computational complexity and accelerating model training. …”
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Two-stage stochastic capacitated Lot-Sizing problem by Lot-Size adaptation approach by Arsalan Rahmani, Meysam Hosseini, Amir Sahami

“…The computational results indicate that the proposed method is capable of efficiently solving the model.…”
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Matrix-qubit algorithm for semantic analysis of probabilistic data by Ilya A. Surov

“…The paper presents a method for semantic data analysis by means of complex-valued matrix decomposition. …”
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Forecasting the daily evaporation by coupling the ensemble deep learning models with meta-heuristic algorithms and data pre-processing in dryland by Tonglin Fu, Dong Wang, Jing Jin

“…However, developing highly accurate and universal data- driven models using time-series analysis methods to achieve precise evaporation estimation remains a challenging. …”
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Derivation and Numerical Assessment of a Stochastic Large–Scale Hydrostatic Primitive Equations Model by Francesco L. Tucciarone, Long Li, Etienne Mémin, Pranav Chandramouli

“…Derived from conservation principles via a stochastic Reynolds transport theorem, this approach decomposes velocity into a smooth–in–time large–scale component and a random–in–time field representing unresolved scales effects. To model the velocity noise term, we develop two data–driven methods based on Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) and extend this to hybrid approaches combining model– and data–driven constraints. …”
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Multivariate decomposition of shift toward public facilities for inpatient care in rural India: evidence from National Sample Survey by Sandeep Sharma, E Lokesh Kumar, Atul Kotwal

“…The study employed multivariate decomposition analysis based on the existing behavioral model of access to health facilities.ResultsThe public facility utilization for inpatient care in rural areas increased from 41.6% to 45.3% between 2014 and 2017–2018. …”
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Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images by Qiye Cheng, Jingyi Zhang, Mengting Hu, Shigeng Wang, Yijun Liu, Jianying Li, Wei Wei

“…Conclusions: Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential.…”
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Forecasting Significant Wave Height Intervals Along China’s Coast Based on Hybrid Modal Decomposition and CNN-BiLSTM by Kairong Xie, Tong Zhang

“…This study proposes a deep learning method based on buoy datasets collected from four research locations in China’s offshore waters over three years (2021–2023, 3-hourly). …”
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Lateral vibration estimation of an elevator rope by sequential modal decomposition of elevator rope displacement observed near the traction sheave by Daisuke TAINAKA, Nanako MIURA, Keito SUMIDA, Arata MASUDA

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Improvement of Network Traffic Prediction in Beyond 5G Network using Sparse Decomposition and BiLSTM Neural Network by Rihab Abdullah Jaber Al Hamadani, Mahdi Mosleh, Ali Hashim Abbas Al-Sallami, Rasool Sadeghi

“…This study proposes an effective deep learning-based traffic prediction technique using BiLSTM (Bidirectional Long Short-Term Memory). The proposed method begins with preprocessing using K-SVD (K-means Singular Value Decomposition) to reduce dimensionality and enhance data representation. …”
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Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…The novel model can depict data decomposition and feature extraction from both cyber and physical domains. …”
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Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition by Zeinab Hassani, Davud Mohammadpur, Hossein Safari

“…To address class imbalance, resampling methods are applied. LSTM and DLSTM models are trained on sequences of peak fluxes and waiting times from irregular time series, while LSTM and DLSTM, integrated with an ensemble approach, are applied to sliding windows of regularized time series with a 3 hr interval. …”
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