Decomposition of Fuzzy Soft Sets with Finite Value Spaces by Feng Feng, Hamido Fujita, Young Bae Jun, Madad Khan

“…The notion of fuzzy soft sets is a hybrid soft computing model that integrates both gradualness and parameterization methods in harmony to deal with uncertainty. …”
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Research on mapping method of logical carrying network across multiple domains by Min ZHANG, Chun-ming WU, Bin WANG, Ming JIANG

“…In order to solve the mapping problem of logical carrying network across multiple domains,a hierarchical linear program model was presented,which aims to minimize the cost of mapping.Based on this model,a mapping algorithm for logical carrying network across multiple domains was designed using primal decomposition and subgradient optimization technique.The correctness of this method was proved by theoretical analysis,and the validity of the proposed method was assessed by a collection of numerical simulation experiments through the performance of convergence and runtime,as well as mapping request acceptance ratio and revenue in a dynamical network environment.…”
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The Study of Large-Scale Fading Using a Wavelet Transform Method by Guizhen Lu, Zhi Cao, Xingning Jia, Jingjing Liang

“…A wavelet method is proposed to evaluate the modeling of the first- and second-order statistics of large-scale fading from the signal strength measurement. …”
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Estimation Method of Carbon Emissions in the Embodied Phase of Low Carbon Building by Hongwei Liu, Jun Li, Yafei Sun, Yanshu Wang, Haichun Zhao

“…Emission factor calculation method is used to establish carbon emission model for building materials. …”
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Improving agricultural commodity allocation and market regulation: a novel hybrid model based on dual decomposition and enhanced BiLSTM for price prediction by Lihua Zhang, Fushun Wang, Fushun Wang, Kejian Wang, Kejian Wang, Zhenxue He, Zhenxue He, Chen Chen, Chen Chen, Jiahao Liu, Jiahao Liu, Chao Wang, Chao Wang, Zhe Wang

“…This innovative approach first performs seasonal decomposition of the original data using the STL method, then applies the VMD method for double decomposition of the residual components, reconstructs the data based on sample entropy, and finally predicts agricultural commodity market prices using the BiLSTM network model optimized by the PSO algorithm. …”
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EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis by Zhi-Yang Zhao, Chang-Ling Huang, Tong-Min Wang, Shi-Hao Zhou, Lu Pei, Wen-Hui Jia, Wei-Hua Jia

“…Furthermore, this paper compares the performance of EM-DeepSD with that of existing benchmarked methods to demonstrate its superiority. Based on the EM-DeepSD framework, we developed the EM-DeepSSA model and compared it with two benchmarked methods across different cfDNA sequencing datasets. …”
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Combination of the Improved Diffraction Nonlocal Boundary Condition and Three-Dimensional Wide-Angle Parabolic Equation Decomposition Model for Predicting Radio Wave Propagation by Ruidong Wang, Guizhen Lu, Rongshu Zhang, Weizhang Xu

“…Numeric computation and measurement results demonstrate the computational accuracy and speed of the WA-3DPE decomposition model with the improved diffraction nonlocal BC.…”
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Improved Tikhonov regularization method for load identification and structural response reconstruction by GAO Kele, YIN Hong, PENG Zhenrui

“…Secondly, the truncated randomized singular value decomposition method was used to calculate the approximate transfer matrix at the locations of the measurement points, while the total least squares method (TLSM) and the traditional Tikhonov regularization method were combined to identify the load, and then the unknown response was reconstructed by the transfer matrix at the locations. …”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…A hybrid deep learning model is developed for AQI prediction, incorporating two-stage decomposition and hyperparameter optimization. …”
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GIS partial discharge fault diagnosis method based on SGMD-LSTM by Zhang Yun, Zhang Chao, Zhang Shiyong, Ma Pengchi, Yang Guang, Ding Hao

“…To accurately diagnose partial discharge faults in Gas Insulated Switchgear (GIS), a fault diagnosis method based on Symplectic Geometric Mode Decomposition (SGMD) and improved Long Short Term Memory (LSTM) is proposed. …”
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Numerical simulation of an effective transform mechanism with convergence analysis of the fractional diffusion-wave equations by Nazek A. Obeidat, Mahmoud S. Rawashdeh, Malak Q. Al Erjani

“…In the current study, we solve two very important mathematical models, such as the time fractional-order space-fractional telegraph and diffusion-wave equations using a reliable technique called the Adomian decomposition natural method (ADNM), which combines Adomian decomposition and natural transform. …”
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Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

“…This study aims to more accurately predict the displacement changes of landslides with nonlinear volatility development.The empirical mode decomposition is first employed to process the time series of monitoring surface displacement of a landslide,and then the irregularly changing displacement series is converted into modal components with regular changes,which generates displacement components at different frequencies.Each component is predicted separately so that the mutual influence of errors can be avoided.The comprehensive prediction of the changing trend of displacement series is based on the prediction of the changing trends of all components.The improved threshold autoregressive model able to well describe non-stationary harmonics is used to predict the landslide displacement components.Finally,the modal superposition yields the final predicted displacement.In this way,a combined prediction model based on empirical mode decomposition and threshold autoregressive model is established,and its prediction accuracy is verified with Baishuihe landslide data.Compared with a BP neural network model and a long short-term memory network model,the proposed model has a high prediction accuracy,which provides a new method for the prediction of landslide displacement.…”
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Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine by Penghai Zhang, Yang Li, Xin Dong, Tianhong Yang, Honglei Liu

“…To address the limitations of traditional early warning methods in open-pit slope displacement monitoring—particularly their neglect of spatiotemporal correlations and their difficulty in analyzing multi-scale non-stationary sequences—this study proposes an early warning framework that integrates spatiotemporal clustering with multi-scale decomposition. …”
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Using Event Logs for Local Correction of Process Models by Alexey A. Mitsyuk, Irina A. Lomazova, Wil M.P. van der Aalst

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Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion by Jingcan Wang, Yiping Yuan, Fangqi Shen, Caifeng Chen

“…To address this issue, this study introduces a high-performance fault diagnosis approach for mining motors operating under strong background noise by integrating parameter-optimized feature mode decomposition (WOA-FMD) with the RepLKNet-BiGRU-Attention dual-channel model. …”
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Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data by M. Velagar, C. Keller, C. Keller, J. N. Kutz

“…<p>We introduce the optimized dynamic mode decomposition (DMD) algorithm for constructing an adaptive and computationally efficient reduced-order model and forecasting tool for global atmospheric chemistry dynamics. …”
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Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation by G. Kalpana, N. Deepa, D. Dhinakaran

“…In this paper, we propose a new solution that integrates with AIPT (Advanced Image Preprocessing Techniques) and CASDN (Context-Aware Spatial Decomposition Network) to overcome these problems. The preprocessing pipeline apply bunch of methods including Adaptive Thresholding, Hierarchical Contrast Normalization, Contextual Feature Augmentation, Multi-Scale Region Enhancement, and Dynamic Histogram Equalization for image quality. …”
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A Novel Optimized Hybrid VMD-PCA-XGBoost Model for Forecasting Precipitation: Exemplified by the Beijing-Tianjin-Hebei Study Region in China by Qiaoli Kong, Qian Li, Qi Bai, Xiaolong Mi, Joseph Awange, Shi Wang, Yi Yang, Guoli Bo

“…In this study, we propose a novel hybrid model, variational mode decomposition-principal component analysis-extreme gradient boosting (VMD-PCA-XGBoost), which integrates VMD for effective signal processing, PCA for dimensionality reduction, and XGBoost for enhancing predictive accuracy. …”
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Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information by Ping Wang, Guangzhong Hu, Wenli Hu, Xiangdong Xue, Jing Tao, Huabin Wen

“…Conventional CFD simulation methods require several hours, whereas the reconstruction model proposed here can rapidly reconstruct the flow field within 1 min after training is completed, significantly reducing reconstruction time. …”
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Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods by Said Ziani, Yousef Farhaoui, Mohammed Moutaib

“…It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). …”
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