Polarimetric SAR Ship Detection Using Context Aggregation Network Enhanced by Local and Edge Component Characteristics by Canbin Hu, Hongyun Chen, Xiaokun Sun, Fei Ma

“…Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. …”
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2LE-BO-DeepTrade: an integrated deep learning framework for stock price prediction by Zinnet Duygu Akşehir, Erdal Kılıç

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EMD combined with ensemble of machine learning predictors for foreign exchange rate forecasting by Duong Tuan Anh, Tran Van Xuan

“…The results indicate that the proposed EMD–LSTM model is more effective than the single LSTM. Besides, to aim at comparing deep-learning models against shallow machine learning models in combination with the EMD decomposition, the second experiment compared EMD-LSTM with the approach which combines EMD with an ensemble of k-nearest neighbors’ predictors (called EMD-KNN method) and the results of the second experiment show that EMD-LSTM cannot outperform EMD-KNN in foreign exchange rates forecasting. …”
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EMD combined with ensemble of machine learning predictors for foreign exchange rate forecasting by Duong Tuan Anh, Tran Van Xuan

“…The results indicate that the proposed EMD–LSTM model is more effective than the single LSTM. Besides, to aim at comparing deep-learning models against shallow machine learning models in combination with the EMD decomposition, the second experiment compared EMD-LSTM with the approach which combines EMD with an ensemble of k-nearest neighbors’ predictors (called EMD-KNN method) and the results of the second experiment show that EMD-LSTM cannot outperform EMD-KNN in foreign exchange rates forecasting. …”
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Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR by Wenwu LI, Qiang SHI, Dan LI, Qunyong HU, Yun TANG, Jinchao MEI

“…To reduce the non-linearity of the short-term load sequence and improve the prediction accuracy, a short-term load forecasting model based on multi-stage optimization variational mode decomposition (VMD) and particle swarm optimization optimize support vector regression (PSO-SVR) is proposed. …”
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Global, regional, and national burden of colorectal cancer associated with diet high in red meat, 1990 to 2021: an analysis for the global burden of disease study and prediction to... by Huijun Lei, Miaomiao Chen, Haoyu Qu, Zuomei He, Hui Zhong, Liang Li, Mengzhou Xie

“…Inequality slope and concentration indices quantified health disparities, while decomposition analyses identified drivers of burden changes. …”
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Multilinear Model of Heat Exchanger with Hammerstein Structure by Dragan Pršić, Novak Nedić, Vojislav Filipović, Ljubiša Dubonjić, Aleksandar Vičovac

“…The set of linear models is formed by decomposition of the nonlinear steady-state characteristic by using the modified Included Angle Dividing method. …”
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Smoothing Techniques for Improving COVID-19 Time Series Forecasting Across Countries by Uliana Zbezhkhovska, Dmytro Chumachenko

“…An ANOVA confirmed the statistically significant influence of the model type on the MAPE (<i>p</i> = 0.008), whereas the smoothing method alone showed no significant effect. …”
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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…However, directly applying single models to predict coupled cooling, heating, and electric loads under complex influencing factors often yields unsatisfactory results. …”
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Infrared Small Target Detection Based on Compound Eye Structural Feature Weighting and Regularized Tensor by Linhan Li, Xiaoyu Wang, Shijing Hao, Yang Yu, Sili Gao, Juan Yue

“…Our model is efficiently solved using the Alternating Direction Method of Multipliers (ADMM). …”
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Application of a text mining method in navigation and communication for enhancing maritime safety. by Paulina Hatłas-Sowińska, Leszek Misztal

“…This paper introduces a model for the translation of natural language into ontology and vice versa in an autonomous navigation system of a sea-going vessel. …”
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STRUCTURAL GLOBAL SENSITIVITY METHOD BASED ON PARTIAL DERIVATIVE WHOLE DOMAIN INTEGRAL by TU LongWei, LIU Jie, LIU GuangZhao, ZHANG Zheng

“…Then,local sensitivity method based on partial derivative was extended to a global sensitivity method by integrating partial derivatives of model variables in variable sapces. …”
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A data-driven hybrid framework for voltage transformer ratio error prediction: Addressing challenges in complex power systems by Jiuxi Cui, Zhenhua Li, Heping Lu, Feng Zhou, Haoyu Chen, Zhenxing Li

“…First, an energy entropy-optimized adaptive variational mode decomposition method is developed, which introduces convergence constraints for parameter selection, reducing randomness during the decomposition process and is validated by power spectrum analysis. …”
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Prospective Models of Financial Forecasting of budget Revenues by A. K. Karaev, O. V. Borisova

“…The study used such methods as measuring predictive values and the size of their errors, analyzing and comparing the results obtained using methods and models of machine and deep learning. …”
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Intelligent Malfunction Identification Method in Mechanical Manufacturing Process Based on Multisensor Data by Meng Wang

“…The mechanical data of keyboard chords are acquired by sound-sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. …”
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Hybrid models of higher education: Features and advantages by Elena Y. Kuznetsova, Olga V. Korableva

“…Within the framework of the study, the authors pay special attention to the advantages and difficulties of the transition to hybrid learning models: through the methods of scientific generalization, deduction and decomposition, both common aspects characteristic of all forms of learning using distance learning technologies and those peculiar only to the hybrid format are revealed. …”
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A Correction Method for Hardening Artifacts in CT Images Based on Integral Invariance by Yuxin LIU, Jiaotong WEI, Xiaojie ZHAO, Ping CHEN, Jinxiao PAN

“…The decomposition model for the X-ray transmission images is constructed using the maximum likelihood function of a Gaussian distribution as the objective function under the constraint of the projection integral invariance at different angles. …”
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Hybrid precoding method for mmWave massive MIMO systems based on LFM by Haoyi CHEN, Yifan DING, Ying CHENG

“…Analog-digital hybrid precoding is a key technology for millimeter wave massive MIMO systems that reduce hardware costs while balancing system performance.However,the traditional hybrid precoding scheme often needed to find a suitable codebook for precoding,and some codebooks were not easy to obtain or had deviations in actual situations.An analog-digital hybrid precoding method based on latent factor model (LFM) in machine learning without codebook was proposed for this problem.The LFM decomposition and stochastic gradient descent method were used to approximate the designed precoding matrix to the optimal full digital precoding matrix for good performance.The simulation results show that compared with the hybrid precoding design method based on orthogonal matching pursuit (OMP) algorithm,this method not only does not need a codebook,but also has better performance than the hybrid precoding algorithm based on OMP algorithm,which is closer to optimal full digital precoding method.…”
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The global burden of depression attributable to childhood sexual abuse, intimate partner violence, and bullying victimization from 1990 to 2021: an analysis based on the global bur... by Zhuo Liu, Guo Mao

“…Decomposition analysis showed that population aging and growth drove most burden increases in middle- to low-SDI regions, while changes in exposure levels explained regional rises in bullying and IPV. …”
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A load margin calculation method considering optimized reactive power support by Yuejian Wu, Xiaoming Dong, Tianguang Lu, Shunxiang Yu, Chengfu Wang, Zhengshuo Li

“…Employment of the LU decomposition method allows the CPF predictor coupling with the sensitivity calculation of voltage to reactive power changes (VQ Sensitivity), decreasing the computational burden in identifying the limit-induced bifurcation (LIB). …”
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