Artykuły w czasopismach na temat „XGBOOST MODEL”
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Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang i Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation". JMIR Medical Informatics 10, nr 6 (16.06.2022): e36958. http://dx.doi.org/10.2196/36958.
Pełny tekst źródłaOUKHOUYA, HASSAN, HAMZA KADIRI, KHALID EL HIMDI i RABY GUERBAZ. "Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models". Statistics, Optimization & Information Computing 12, nr 1 (3.11.2023): 200–209. http://dx.doi.org/10.19139/soic-2310-5070-1822.
Pełny tekst źródłaGu, Kai, Jianqi Wang, Hong Qian i Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm". Mathematical Problems in Engineering 2021 (26.04.2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.
Pełny tekst źródłaLiu, Jialin, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu i Ke Li. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model". PLOS ONE 16, nr 2 (4.02.2021): e0246306. http://dx.doi.org/10.1371/journal.pone.0246306.
Pełny tekst źródłaJi, Shouwen, Xiaojing Wang, Wenpeng Zhao i Dong Guo. "An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise". Mathematical Problems in Engineering 2019 (16.09.2019): 1–15. http://dx.doi.org/10.1155/2019/8503252.
Pełny tekst źródłaZhu, Yiming. "Stock Price Prediction based on LSTM and XGBoost Combination Model". Transactions on Computer Science and Intelligent Systems Research 1 (12.10.2023): 94–109. http://dx.doi.org/10.62051/z6dere47.
Pełny tekst źródłaXiong, Shuai, Zhixiang Liu, Chendi Min, Ying Shi, Shuangxia Zhang i Weijun Liu. "Compressive Strength Prediction of Cemented Backfill Containing Phosphate Tailings Using Extreme Gradient Boosting Optimized by Whale Optimization Algorithm". Materials 16, nr 1 (28.12.2022): 308. http://dx.doi.org/10.3390/ma16010308.
Pełny tekst źródłaWang, Yu, Li Guo, Yanrui Zhang i Xinyue Ma. "Research on CSI 300 Stock Index Price Prediction Based On EMD-XGBoost". Frontiers in Computing and Intelligent Systems 3, nr 1 (17.03.2023): 72–77. http://dx.doi.org/10.54097/fcis.v3i1.6027.
Pełny tekst źródłaHarriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati i Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction". Jambura Journal of Informatics 5, nr 1 (27.04.2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.
Pełny tekst źródłaSiringoringo, Rimbun, Resianta Perangin-angin i Jamaluddin Jamaluddin. "MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR". METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 5, nr 2 (31.10.2021): 97–103. http://dx.doi.org/10.46880/jmika.vol5no2.pp97-103.
Pełny tekst źródłaGu, Zhongyuan, Miaocong Cao, Chunguang Wang, Na Yu i Hongyu Qing. "Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model". Sustainability 14, nr 16 (22.08.2022): 10421. http://dx.doi.org/10.3390/su141610421.
Pełny tekst źródłaLee, Jong-Hyun, i In-Soo Lee. "Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost". Batteries 9, nr 11 (5.11.2023): 544. http://dx.doi.org/10.3390/batteries9110544.
Pełny tekst źródłaZhang, Kun. "Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors". Journal of Physics: Conference Series 2666, nr 1 (1.12.2023): 012006. http://dx.doi.org/10.1088/1742-6596/2666/1/012006.
Pełny tekst źródłaXiaobing Lin, Xiaobing Lin, Zhe Wu Xiaobing Lin, Jianfa Chen Zhe Wu, Lianfen Huang Jianfa Chen i Zhiyuan Shi Lianfen Huang. "A Credit Scoring Model Based on Integrated Mixed Sampling and Ensemble Feature Selection: RBR_XGB". 網際網路技術學刊 23, nr 5 (wrzesień 2022): 1061–68. http://dx.doi.org/10.53106/160792642022092305014.
Pełny tekst źródłaHe, Wenwen, Hongli Le i Pengcheng Du. "Stroke Prediction Model Based on XGBoost Algorithm". International Journal of Applied Sciences & Development 1 (13.12.2022): 7–10. http://dx.doi.org/10.37394/232029.2022.1.2.
Pełny tekst źródłaGuo, RuYan, MinFang Peng, ZhenQi Cao i RunFu Zhou. "Transformer graded fault diagnosis based on neighborhood rough set and XGBoost". E3S Web of Conferences 243 (2021): 01002. http://dx.doi.org/10.1051/e3sconf/202124301002.
Pełny tekst źródłaOgunleye, Adeola, i Qing-Guo Wang. "XGBoost Model for Chronic Kidney Disease Diagnosis". IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, nr 6 (1.11.2020): 2131–40. http://dx.doi.org/10.1109/tcbb.2019.2911071.
Pełny tekst źródłaYin, Yilan, Yanguang Sun, Feng Zhao i Jinxiang Chen. "Improved XGBoost model based on genetic algorithm". International Journal of Computer Applications in Technology 62, nr 3 (2020): 240. http://dx.doi.org/10.1504/ijcat.2020.10028423.
Pełny tekst źródłaChen, Jinxiang, Feng Zhao, Yanguang Sun i Yilan Yin. "Improved XGBoost model based on genetic algorithm". International Journal of Computer Applications in Technology 62, nr 3 (2020): 240. http://dx.doi.org/10.1504/ijcat.2020.106571.
Pełny tekst źródłaZhao, Haolei, Yixian Wang, Xian Li, Panpan Guo i Hang Lin. "Prediction of Maximum Tunnel Uplift Caused by Overlying Excavation Using XGBoost Algorithm with Bayesian Optimization". Applied Sciences 13, nr 17 (28.08.2023): 9726. http://dx.doi.org/10.3390/app13179726.
Pełny tekst źródłaXu, Bing, Youcheng Tan, Weibang Sun, Tianxing Ma, Hengyu Liu i Daguo Wang. "Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model". Sustainability 15, nr 6 (15.03.2023): 5201. http://dx.doi.org/10.3390/su15065201.
Pełny tekst źródłaFeng, Dachun, Bing Zhou, Shahbaz Gul Hassan, Longqin Xu, Tonglai Liu, Liang Cao, Shuangyin Liu i Jianjun Guo. "A Hybrid Model for Temperature Prediction in a Sheep House". Animals 12, nr 20 (17.10.2022): 2806. http://dx.doi.org/10.3390/ani12202806.
Pełny tekst źródłaZheng, Jiayan, Tianchen Yao, Jianhong Yue, Minghui Wang i Shuangchen Xia. "Compressive Strength Prediction of BFRC Based on a Novel Hybrid Machine Learning Model". Buildings 13, nr 8 (29.07.2023): 1934. http://dx.doi.org/10.3390/buildings13081934.
Pełny tekst źródłaLin, Nan, Jiawei Fu, Ranzhe Jiang, Genjun Li i Qian Yang. "Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm". Remote Sensing 15, nr 15 (28.07.2023): 3764. http://dx.doi.org/10.3390/rs15153764.
Pełny tekst źródłaWu, Kehe, Yanyu Chai, Xiaoliang Zhang i Xun Zhao. "Research on Power Price Forecasting Based on PSO-XGBoost". Electronics 11, nr 22 (16.11.2022): 3763. http://dx.doi.org/10.3390/electronics11223763.
Pełny tekst źródłaYuan, Jianming. "Predicting Death Risk of COVID-19 Patients Leveraging Machine Learning Algorithm". Applied and Computational Engineering 8, nr 1 (1.08.2023): 186–90. http://dx.doi.org/10.54254/2755-2721/8/20230122.
Pełny tekst źródłaHa, Jinbing, i Ziyi Zhou. "Subway Energy Consumption Prediction based on XGBoost Model". Highlights in Science, Engineering and Technology 70 (15.11.2023): 548–52. http://dx.doi.org/10.54097/hset.v70i.13958.
Pełny tekst źródłaWan, Zhi, Yading Xu i Branko Šavija. "On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance". Materials 14, nr 4 (3.02.2021): 713. http://dx.doi.org/10.3390/ma14040713.
Pełny tekst źródłaUbaidillah, Rahmad, Muliadi Muliadi, Dodon Turianto Nugrahadi, M. Reza Faisal i Rudy Herteno. "Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search". JURNAL MEDIA INFORMATIKA BUDIDARMA 6, nr 3 (25.07.2022): 1723. http://dx.doi.org/10.30865/mib.v6i3.4146.
Pełny tekst źródłaLiu, Yuan, Wenyi Du, Yi Guo, Zhiqiang Tian i Wei Shen. "Identification of high-risk factors for recurrence of colon cancer following complete mesocolic excision: An 8-year retrospective study". PLOS ONE 18, nr 8 (11.08.2023): e0289621. http://dx.doi.org/10.1371/journal.pone.0289621.
Pełny tekst źródłaZhu, Mini, Gang Wang, Chaoping Li, Hongjun Wang i Bin Zhang. "Artificial Intelligence Classification Model for Modern Chinese Poetry in Educatio". Sustainability 15, nr 6 (16.03.2023): 5265. http://dx.doi.org/10.3390/su15065265.
Pełny tekst źródłaLi, Dan, Delan Zhu, Tao Tao i Jiwei Qu. "Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models". Processes 12, nr 1 (22.12.2023): 39. http://dx.doi.org/10.3390/pr12010039.
Pełny tekst źródłaSong, Weihua, Xiaowei Han i Jifei Qi. "Prediction of Gas Emission in the Working Face Based on LASSO-WOA-XGBoost". Atmosphere 14, nr 11 (30.10.2023): 1628. http://dx.doi.org/10.3390/atmos14111628.
Pełny tekst źródłaWang, Jiayi, i Shaohua Zhou. "CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures". Micromachines 14, nr 9 (27.08.2023): 1673. http://dx.doi.org/10.3390/mi14091673.
Pełny tekst źródłaM.I., Omogbhemhe, i Momodu I.B.A. "Model for Predicting Bank Loan Default using XGBoost". International Journal of Computer Applications 183, nr 32 (16.10.2021): 1–4. http://dx.doi.org/10.5120/ijca2021921705.
Pełny tekst źródłaZhang, Huimin, Renshuang Ding, Qi Zhang, Mingxing Fang, Guanghua Zhang i Naiwen Yu. "An ARDS Severity Recognition Model based on XGBoost". Journal of Physics: Conference Series 2138, nr 1 (1.12.2021): 012009. http://dx.doi.org/10.1088/1742-6596/2138/1/012009.
Pełny tekst źródłaKang, Yunxiang, Minsheng Tan, Ding Lin i Zhiguo Zhao. "Intrusion Detection Model Based on Autoencoder and XGBoost". Journal of Physics: Conference Series 2171, nr 1 (1.01.2022): 012053. http://dx.doi.org/10.1088/1742-6596/2171/1/012053.
Pełny tekst źródłaJiang, Hui, Zheng He, Gang Ye i Huyin Zhang. "Network Intrusion Detection Based on PSO-Xgboost Model". IEEE Access 8 (2020): 58392–401. http://dx.doi.org/10.1109/access.2020.2982418.
Pełny tekst źródłaAlim, Mirxat, Guo-Hua Ye, Peng Guan, De-Sheng Huang, Bao-Sen Zhou i Wei Wu. "Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study". BMJ Open 10, nr 12 (grudzień 2020): e039676. http://dx.doi.org/10.1136/bmjopen-2020-039676.
Pełny tekst źródłaLi, Xiangcheng, Jialong Wang, Zhirui Geng, Yang Jin i Jiawei Xu. "Short-term Wind Power Prediction Method Based on Genetic Algorithm Optimized XGBoost Regression Model". Journal of Physics: Conference Series 2527, nr 1 (1.06.2023): 012061. http://dx.doi.org/10.1088/1742-6596/2527/1/012061.
Pełny tekst źródłaTang, Jinjun, Lanlan Zheng, Chunyang Han, Fang Liu i Jianming Cai. "Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model". Journal of Advanced Transportation 2020 (9.06.2020): 1–12. http://dx.doi.org/10.1155/2020/6401082.
Pełny tekst źródłaNoorunnahar, Mst, Arman Hossain Chowdhury i Farhana Arefeen Mila. "A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh". PLOS ONE 18, nr 3 (27.03.2023): e0283452. http://dx.doi.org/10.1371/journal.pone.0283452.
Pełny tekst źródłaJiang, Jinyang, Zhi Liu, Pengbo Wang i Fan Yang. "Improved Crow Search Algorithm and XGBoost for Transformer Fault Diagnosis". Journal of Physics: Conference Series 2666, nr 1 (1.12.2023): 012040. http://dx.doi.org/10.1088/1742-6596/2666/1/012040.
Pełny tekst źródłaWang, Jun, Wei Rong, Zhuo Zhang i Dong Mei. "Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China". Wireless Communications and Mobile Computing 2022 (19.03.2022): 1–14. http://dx.doi.org/10.1155/2022/8005493.
Pełny tekst źródłaLe, Le Thi, Hoang Nguyen, Jian Zhou, Jie Dou i Hossein Moayedi. "Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost". Applied Sciences 9, nr 13 (4.07.2019): 2714. http://dx.doi.org/10.3390/app9132714.
Pełny tekst źródłaLuo, Xiong, Lijia Xu, Peng Huang, Yuchao Wang, Jiang Liu, Yan Hu, Peng Wang i Zhiliang Kang. "Nondestructive Testing Model of Tea Polyphenols Based on Hyperspectral Technology Combined with Chemometric Methods". Agriculture 11, nr 7 (16.07.2021): 673. http://dx.doi.org/10.3390/agriculture11070673.
Pełny tekst źródłaAdmassu, Tsehay. "Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection". Proceedings of Engineering and Technology Innovation 23 (1.01.2023): 48–59. http://dx.doi.org/10.46604/peti.2023.10101.
Pełny tekst źródłaYang, Tian. "Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost Models". Highlights in Science, Engineering and Technology 49 (21.05.2023): 244–49. http://dx.doi.org/10.54097/hset.v49i.8513.
Pełny tekst źródłaMeng, Yunxiang, Qihong Duan, Kai Jiao i Jiang Xue. "A screened predictive model for esophageal squamous cell carcinoma based on salivary flora data". Mathematical Biosciences and Engineering 20, nr 10 (2023): 18368–85. http://dx.doi.org/10.3934/mbe.2023816.
Pełny tekst źródłaZong, Jing, Xin Xiong, Jianhua Zhou, Ying Ji, Diao Zhou i Qi Zhang. "FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition". Sensors 23, nr 12 (17.06.2023): 5680. http://dx.doi.org/10.3390/s23125680.
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