Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. …”
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Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

“…However, at the present stage, the dam prediction model based on machine learning mostly adopts the means of data preprocessing, using optimization algorithm, and using the model's characteristics to stack multiple models, lacking in in-depth consideration of the physical mechanism of dam deformation. …”
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Porosity prediction of tight reservoir rock using well logging data and machine learning by Yawen He, Hongjun Zhang, Zhiyu Wu, Hongbo Zhang, Xin Zhang, Xiaojing Zhuo, Xiaoli Song, Sha Dai, Wei Dang

“…These models are further optimized with the particle swarm optimization (PSO) algorithm to enhance their predictive accuracy. …”
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LEO satellite constellation design with multi-QoS guarantee for non-terrestrial network by Ruyan WANG, Xianyi YE, Peng HE, Yaping CUI, Dapeng WU, Fedotov Alexander

“…LEO satellite constellation (LSC) offers seamless and fast connectivity for non-terrestrial network (NTN).However, the lack of QoS guarantees for users significantly impacts the performance of the NTN system.Considering multi-QoS metrics, a capacity and downlink budget model for LSC was established, and subsequently, the establishment of inter-satellite links was framed as a link budget issue for the QoS guarantee.The non-dominated sorting genetic algorithm (INSGA-II) with improved crossover and mutation operators was proposed to optimize LSC, aiming to maximize coverage and system capacity while minimizing constellation costs.The numerical results demonstrate that the designed LSC exhibits comparable or superior performance to Telesat and Kepler, and its scale is only 64% of the Kepler system.…”
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Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…From the perspective of economics, a profit function was constructed and a nonlinear optimization algorithm was designed to investigate the Nash equilibrium and the socially optimal behavior of the secondary user packets, then a pricing policy of licensed spectrum for secondary users was formulated. …”
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Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…From the perspective of economics, a profit function was constructed and a nonlinear optimization algorithm was designed to investigate the Nash equilibrium and the socially optimal behavior of the secondary user packets, then a pricing policy of licensed spectrum for secondary users was formulated. …”
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Parameterization of user functions in digital signal processing for obtaining angular superresolution by A. A. Shchukin, A. E. Pavlov

“…Objectives. One of the most important tasks in the development of goniometric systems is improving resolution in terms of angular coordinates. …”
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RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model by Xi Chen, Xinlong Cheng

“…Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.…”
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A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction by Bo Li, Yuanqiang Lian

“…Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. …”
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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis by Lei You, Wenjie Fan, Zongwen Li, Ying Liang, Miao Fang, Jin Wang

“…Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. …”
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Distributed Coordinated Dispatch Model for Multi-area Interconnected Integrated Energy Systems Based on Sequential Cone Programming by Yujie REN, Yuhan HUANG, Zhenbo WEI

“…The solution of the distributed algorithm based on ATC is close to the global optimal solution of the distributed algorithm. …”
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Bolt Anchorage Quality Levels Classification Method Based on HO‐VMD‐CNN‐BiLSTM by Fan Kesong, Zhang Can, Liu Shaowei, Feng Mengyin, Yan Ao, Fu Mengxiong, He Deyin, Nie Zhibin

“…The model incorporates the Logistic‐Tent chaotic mapping optimization algorithm, which enhances global search capability, improves feature extraction, and increases detection efficiency and accuracy. …”
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Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. by Lin Jun, Zhou Chenliang

“…It can not only meet the demand of users and realize the optimal allocation of resources, but also improve the safety, economy and reliability of power supply, it has become a major trend in the future development of electric power industry. …”
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Establishment and Application of Passenger Flow Safety Management Evaluation Model with Entropy Weight and TOPSIS for Metro Stations by Heng YU

“…The results demonstrate that this model provides an accurate assessment of metro station passenger safety management and offers decision-makers clear directions for improvement.…”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

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A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…The physical and mechanical properties of the soil–structure interface under the freeze–thaw condition are complex, making empirical shear strength models poorly applicable. This study employs integrated machine learning algorithms to model the shear behavior of frozen–thawed silty clay and the structure interface. …”
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Research on primary frequency regulation and VSC-HVDC frequency synchronization control coordination method for large-scale hydropower DC export regional grid by Hongchun Shu, Huaibin Gao, Zongxue Shao, Yongzan Cui, Wei Zhao

“…An evolutionary algorithm is used to solve the optimal parameters hierarchically progressively. …”
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Evaluation Model of Low-Carbon Circular Economy Coupling Development in Forest Area Based on Radial Basis Neural Network by Chang Liu

“…In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. …”
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning by Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…Furthermore, we observe that the number of base learners significantly influences model performance, with an optimal range of 5–7 learners. …”
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Modeling pine forest growing stock volume in subtropical regions of China using airborne Lidar data by Zige Lan, Xiandie Jiang, Guiying Li, Yagang Lu, Hongwen Yao, Dengsheng Lu

“…More research is needed to quantitatively examine different contribution of sample sizes, modeling algorithms, variables from different sources, and stratification factors on modeling results, so that we can design an optimal procedure for GSV modeling using airborne Lidar data.…”
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