Application of Hybrid Algorithm with Large Data in Wind Farm Modeling by GUO Min, ZHAO Qiao-e, GAO Jin-cheng, ZHOU Bin-long

“…The real operation data of large-scale wind farm are analyzed and modeled by using the optimized particle swarm K-means hybrid clustering algorithm. …”
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Analytical MPC Algorithm Using Steady-State Process Model by Piotr M. Marusak

“…To improve the MPC algorithm operation, one can use a steady-state process model; this paper describes how to do this skillfully. …”
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Generative LOD algorithm based on space–time grid model by Huangchuang Zhang, Ge Li

“…Based on this, this paper proposes generative levels of detail (LOD) algorithm based on space–time grid model. Taking the space–time grid model as the space–time guiding framework, different levels of regions are divided according to the distance and viewpoint of the camera, and then the culling operation is performed, and then the appropriate level of detail and corresponding sampling method are dynamically selected for rendering to achieve lightweighting of the CIM. …”
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Grasshopper Algorithmic Modelling: Parametric Design for Product Platform Customisation by Amanda Martín-Mariscal, Juan Francisco Fernández-Rodríguez, Alberto Picardo, Estela Peralta

“…Recent advances in visual programming tools for algorithmic modelling have significantly expanded the possibilities for designing industrial products. …”
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Algorithms and probabilistic models of parameters of operation of in-plant power supply by E. I. Gracheva, O. V. Naumov, A. N. Gorlov, Z. M. Shakurova

“…The results obtained allow us to recommend the use of the developed models for effective control of circuit and mode parameters of low-voltage electrical networks to improve the quality of power supply to consumers.It is assumed that for external parameters of a random nature, the normal distribution is reliable. …”
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Study on College English Online Teaching Model in Mixed Context Based on Genetic Algorithm and Neural Network Algorithm by Xiaoxia Ma

“…The algorithm avoids subjective errors and improves the accuracy and reliability of the evaluation results, and a comprehensive evaluation model is constructed. …”
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Forensic Genetics: A Machine Learning Algorithm for Mutation Modelling by Sofia Antão Sousa, Leonor Gusmão, Marisa Faustino, António Amorim, Nádia Pinto

“…These results support that machine learning algorithms may be used to improve mutation modelling, statistical significance depending on the available data to be used as training and test sets. …”
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Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm by Yi LU, Mengying XU, Jie ZHOU

“…Aiming at the multi-constraint routing problem,a mathematical model was designed,and an improved immune clonal shuffled frog leaping algorithm (IICSFLA) was proposed,which combined immune operator with traditional SFLA.Under the constraints of bandwidth,delay,packet loss rate,delay jitter and energy cost,total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation,the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.…”
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Influence maximization algorithm of social networks based on Transformer model by YU Shuke, YAO Yao, YAN Chenxue

“…In view of this problem, a improved Transformer model based social network influence maximization algorithm was proposed. …”
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Low-complexity ATPM-VSIMM algorithm with adaptive model parameters by Hao ZENG, Wangqiang MU, Yang JIANG, Shunping YANG

“…Aiming at the problem that for maneuvering target tracking, the accuracy of tracking degraded in interacting multiple model algorithms due to the fixed model sets and the fixed transition probability matrix, a low-complexity ATPM-VSIMM algorithm was proposed, which could update the model parameters adaptively.The maneuvering situation of the target was judged according to the innovation changes of the system, and the state noise of the model sets was adjusted to realize the adaptive update of the model sets.Then, the more accurate transition probability matrix was computed through the change of the model posterior probability and the inter-model switching relationship.Therefore, the matching degree between the system motion model and the target motion trajectory was improved.Finally, the high filtering accuracy and the fast response speed of the tracking system were guaranteed.The effectiveness of the proposed algorithm was verified through three aspects that are the initial value of the model posterior probability, the initial value of the transition probability matrix, and the state noise.Simulation results demonstrate that the filtering accuracy of the ATPM-VSIMM algorithm is improved about 8% compared with the existing algorithms.…”
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Spatiotemporal data modeling and prediction algorithms in intelligent management systems by Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

“…Conclusion: The spatiotemporal data modeling and prediction algorithm proposed by the author in the intelligent management system significantly improves prediction accuracy.…”
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Artificial Intelligence Algorithm for Optimal Time Series Data Model by Kang Wang

“…Based on the characteristics, the theory and algorithm of model selection are proposed. The model selection theory and algorithm in this paper is used for empirical analysis. …”
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Optimizing Models and Data Denoising Algorithms for Power Load Forecasting by Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee, Tae-Kook Kim

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A novel model for malaria prediction based on ensemble algorithms. by Mengyang Wang, Hui Wang, Jiao Wang, Hongwei Liu, Rui Lu, Tongqing Duan, Xiaowen Gong, Siyuan Feng, Yuanyuan Liu, Zhuang Cui, Changping Li, Jun Ma

“…However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.…”
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A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms by Mohammad Ghattas, Antonio M. Mora, Suhail Odeh

“…Similarly, Random Forest models showed a slight improvement, reaching 81% with feature selection versus 80% without. …”
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Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm by Youping Wu, Zhihui Zhou

“…In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. …”
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Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms by Yuki Hanawa, Tomohiro Harada, Yukiya Miura

“…To reduce this cost, SAEAs employ surrogate models—machine learning models that approximate expensive evaluation functions. …”
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A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models by Athanasios Donas, George Galanis, Ioannis Pytharoulis, Ioannis Th. Famelis

“…This study introduces a novel enhancement to the Kalman filter algorithm by integrating it with Radial Basis Function neural networks to improve numerical weather prediction models. …”
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Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

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Advances in the Application of Intelligent Algorithms to the Optimization and Control of Hydrodynamic Noise: Improve Energy Efficiency and System Optimization by Maosen Xu, Bokai Fan, Renyong Lin, Rong Lin, Xian Wu, Shuihua Zheng, Yunqing Gu, Jiegang Mou

“…Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future research directions. …”
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