Suchergebnisse - "stacking integration algorithm"

Stacking integration algorithm Based on Regression models and Informer for Olympic Medal Standings Forecasting

Autoren: Kaidi Wang Yunqing Guo Shiyuan Chen

Quelle: Highlights in Science, Engineering and Technology. 146:260-269

Predicting Stroke Through Health Data Analysis and Stacking Integration Algorithm

Autoren: Yi Wang Xinping Shang A'ni Dong et al.

Quelle: 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE). :1-5

Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting

Autoren: Shucheng Luo Baoshi Wang Qingzhong Gao et al.

Quelle: Energy Reports. 12:2676-2689

Achievement Prediction System Based on Stacking Integration Algorithm

Autoren: Yan Chen

Quelle: 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). :74-79

Design and Implementation of State Grid Data Operation Management System Based on Stacking Integration Algorithm

Autoren: Jiajia Wang Bo Zhang Wenjian Yao et al.

Quelle: 2024 3rd International Conference for Innovation in Technology (INOCON). :1-4

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

Autoren: Zhanjun Tang Xiaobing Shi Huayu Zou et al.

Quelle: Sensors, Vol 23, Iss 13, p 6198 (2023)

Schlagwörter: fault diagnosis, WTG, stacking integration algorithm, machine learning, EDP, adaptive threshold, Chemical technology, TP1-1185

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1424-8220/23/13/6198; https://doaj.org/toc/1424-8220

Zugangs-URL: https://doaj.org/article/940c68cc10af4cacb745c17ae9238642

Research on Sinter Quality Prediction System Based on Granger Causality Analysis and Stacking Integration Algorithm

Autoren: Xin Li Xiaojie Liu Hongyang Li et al.

Quelle: Metals, Vol 13, Iss 2, p 419 (2023)

Schlagwörter: sintering quality, Granger causality analysis, stacking integration algorithm, Mining engineering. Metallurgy, TN1-997

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-4701/13/2/419; https://doaj.org/toc/2075-4701

Zugangs-URL: https://doaj.org/article/eaf646d2c34846b5930665378e7073f1

Accurate Heart Disease Prediction via Improved Stacking Integration Algorithm

Autoren: Jun-long Zhao Hua-ping Jia Wei-Xi Sun et al.

Quelle: Journal of Imaging Science and Technology. 65:030408-1

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://www.ingentaconnect.com/content/ist/jist/2021/00000065/00000003/art00010

Research on Sinter Quality Prediction System Based on Granger Causality Analysis and Stacking Integration Algorithm.

Autoren: Li, Xin Liu, Xiaojie Li, Hongyang et al.

Quelle: Metals (2075-4701); Feb2023, Vol. 13 Issue 2, p419, 17p

Schlagwörter: TRAVERTINE, RANK correlation (Statistics), PEARSON correlation (Statistics), IGNITION temperature, COAL gas, COINTEGRATION, AIR pressure, SINTER (Metallurgy), BLAST furnaces

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold.

Autoren: Tang, Zhanjun Shi, Xiaobing Zou, Huayu et al.

Quelle: Sensors (14248220); Jul2023, Vol. 23 Issue 13, p6198, 19p

Schlagwörter: BOOSTING algorithms, TURBINE generators, FAULT diagnosis, MACHINE learning, WIND turbines, STANDARD deviations, ALGORITHMS

Firma/Körperschaft: EDP-Energias de Portugal SA

Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms

Autoren: Jiachuang Wang Haoji Ma Xianhang Yan

Quelle: Mathematics, Vol 11, Iss 4, p 838 (2023)

Schlagwörter: rockburst prediction, stacking integration algorithm, voting integration algorithm, SMOTE data oversampling, fusion strategy, Mathematics, QA1-939

Relation: https://www.mdpi.com/2227-7390/11/4/838; https://doaj.org/toc/2227-7390; https://doaj.org/article/b9c4950719cd4f45823a195aa3227d9a

Verfügbarkeit: https://doi.org/10.3390/math11040838
https://doaj.org/article/b9c4950719cd4f45823a195aa3227d9a

Accurate Heart Disease Prediction via Improved Stacking Integration Algorithm.

Autoren: Hua-ping Jia Jun-long Zhao Jun-Liu et al.

Quelle: Journal of Imaging Science & Technology; May2021, Vol. 65 Issue 3, p1-9, 9p

Schlagwörter: HEART diseases, SURVIVAL rate, MACHINE learning, DISEASE incidence, FORECASTING

Vibration Test of Single Coal Gangue Particle Directly Impacting the Metal Plate and the Study of Coal Gangue Recognition Based on Vibration Signal and Stacking Integration

Autoren: Yang Yang Qingliang Zeng Guangjun Yin et al.

Quelle: IEEE Access, Vol 7, Pp 106784-106805 (2019)

Schlagwörter: Coal gangue recognition, intercepted signal, stacking integration algorithm, vibration signal, EMD, impact, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 01 natural sciences, TK1-9971, 0105 earth and related environmental sciences

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08782521.pdf
https://doaj.org/article/ae04beb04c7f419c9f3d88433e685127

Prediction of Copper Matte Grade Based on DN-GAN Stacking Algorithm: Prediction of copper matte grade based on DN-GAN stacking algorithm: Li, Gu, Gao, Ding, and Yin.

Autoren: Li, Tiangui Gu, Wenjuan Gao, Wenqi et al.

Quelle: JOM: The Journal of The Minerals, Metals & Materials Society (TMS); Jan2025, Vol. 77 Issue 1, p50-60, 11p

Schlagwörter: OPTIMIZATION algorithms, ENSEMBLE learning, GENERATIVE adversarial networks, COPPER, COPPER smelting

MOBILE USER ENGLISH LEARNING PATTERN RECOGNITION MODEL BASED ON INTEGRATED LEARNING.

Autoren: QIAN ZHANG YUNLAN REN

Quelle: Scalable Computing: Practice & Experience; Jul2024, Vol. 25 Issue 4, p2371-2384, 14p

Schlagwörter: MOBILE learning, PATTERN recognition systems, ENGLISH language, INDIVIDUALIZED instruction, FEATURE selection, MOBILE operating systems

Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms.

Autoren: Wang, Jiachuang Ma, Haoji Yan, Xianhang

Quelle: Mathematics (2227-7390); Feb2023, Vol. 11 Issue 4, p838, 29p

Schlagwörter: MACHINE learning, CLASSIFICATION algorithms, FORECASTING, PREDICTION models, CLASSIFICATION

Research and application of transformer fault diagnosis method based on SHAP-IHHO-Xgboost model.

Autoren: Liu, Cheng

Quelle: Journal of Computational Methods in Sciences & Engineering; Nov2025, Vol. 25 Issue 6, p5715-5739, 25p

Schlagwörter: FAULT diagnosis, FEATURE selection, POWER transformers, MACHINE learning, OPTIMIZATION algorithms, MATHEMATICAL optimization

Prediction of intraductal cancer microinfiltration based on the hierarchical fusion of peri-tumor imaging histology and dual view deep learning.

Autoren: Yao, Guoyan Huang, Yan Shang, Xiaojing et al.

Quelle: BMC Cancer; 10/14/2025, Vol. 25 Issue 1, p1-13, 13p

Schlagwörter: DEEP learning, DUCTAL carcinoma, PREDICTIVE validity, CLINICAL decision support systems, RADIOMICS, DATA integration, MICROMETASTASIS

Optimized Hybrid Deep Learning Framework for Short-Term Power Load Interval Forecasting via Improved Crowned Crested Porcupine Optimization and Feature Mode Decomposition.

Autoren: Luo, Shucheng Meng, Xiangbin Pang, Xinfu et al.

Quelle: Algorithms; Oct2025, Vol. 18 Issue 10, p659, 27p

Schlagwörter: LOAD forecasting (Electric power systems), DEEP learning, GAUSSIAN mixture models, FORECASTING, OPTIMIZATION algorithms, FEATURE extraction, COMPUTER performance

Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data.

Autoren: Karamollaoğlu, Hamdullah

Quelle: Turkish Journal of Electrical Power & Energy Systems; Oct2025, Vol. 5 Issue 3, p152-160, 9p

Schlagwörter: ELECTRIC power production forecasting, RENEWABLE energy sources, ENSEMBLE learning, LONG short-term memory, ENERGY security, TRANSFORMER models, FORECASTING, METADATA

Geografische Kategorien: TURKEY