Artykuły w czasopismach na temat „Random Forest predictive model”
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Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos i Johan J. Polder. "Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model". Statistics, Politics and Policy 11, nr 2 (16.12.2020): 111–38. http://dx.doi.org/10.1515/spp-2019-0010.
Pełny tekst źródłaWang, Fangyi, Yongchao Wang, Xiaokang Ji i Zhiping Wang. "Effective Macrosomia Prediction Using Random Forest Algorithm". International Journal of Environmental Research and Public Health 19, nr 6 (10.03.2022): 3245. http://dx.doi.org/10.3390/ijerph19063245.
Pełny tekst źródłaKor, Hakan. "Global solar radiation prediction model with random forest algorithm". Thermal Science 25, Spec. issue 1 (2021): 31–39. http://dx.doi.org/10.2298/tsci200608004k.
Pełny tekst źródłaRigatti, Steven J. "Random Forest". Journal of Insurance Medicine 47, nr 1 (1.01.2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.
Pełny tekst źródłaWei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang i Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†". Frontiers of Nursing 8, nr 3 (1.09.2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
Pełny tekst źródłaDiaz, Pablo, Juan C. Salas, Aldo Cipriano i Felipe Núñez. "Random forest model predictive control for paste thickening". Minerals Engineering 163 (marzec 2021): 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.
Pełny tekst źródłaMao, Yiwen, i Asgeir Sorteberg. "Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest". Weather and Forecasting 35, nr 6 (grudzień 2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.
Pełny tekst źródłaBashir Suleiman, Aminu, Stephen Luka i Muhammad Ibrahim. "CARDIOVASCULAR DISEASE PREDICTION USING RANDOM FOREST MACHINE LEARNING ALGORITHM". FUDMA JOURNAL OF SCIENCES 7, nr 6 (31.12.2023): 282–89. http://dx.doi.org/10.33003/fjs-2023-0706-2128.
Pełny tekst źródłaJeong, Hoyeon, Youngjune Kim i So Yeong Lim. "A Predictive Model for Farmland Purchase/Rent Using Random Forests". Korean Agricultural Economics Association 63, nr 3 (30.09.2022): 153–68. http://dx.doi.org/10.24997/kjae.2022.63.3.153.
Pełny tekst źródłaEmir, Senol, Hasan Dincer, Umit Hacioglu i Serhat Yuksel. "Random Regression Forest Model using Technical Analysis Variables". International Journal of Finance & Banking Studies (2147-4486) 5, nr 3 (21.07.2016): 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.
Pełny tekst źródłaNie, Ying, i Yundong Xu. "Prediction On Tiktok Like Behavior Based on Random Forest Model". Highlights in Science, Engineering and Technology 101 (20.05.2024): 292–98. http://dx.doi.org/10.54097/d6metn07.
Pełny tekst źródłaRen, Keying. "House Price Prediction Based on Machine Learning Algorithms - Taking Ames as an Example". Advances in Economics, Management and Political Sciences 85, nr 1 (28.05.2024): 181–89. http://dx.doi.org/10.54254/2754-1169/85/20240870.
Pełny tekst źródłaMathew, Dr Tina Elizabeth. "An Improvised Random Forest Model for Breast Cancer Classification". NeuroQuantology 20, nr 5 (18.05.2022): 713–22. http://dx.doi.org/10.14704/nq.2022.20.5.nq22227.
Pełny tekst źródłaWang, Zijie, Yufang Bi, Gang Lu, Xu Zhang, Xiangyang Xu, Yilin Ning, Xuhua Du i Anke Wang. "Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach". Forests 15, nr 2 (7.02.2024): 318. http://dx.doi.org/10.3390/f15020318.
Pełny tekst źródłaZhou, Shu-Ping, Su-Ding Fei, Hui-Hui Han, Jing-Jing Li, Shuang Yang i Chun-Yang Zhao. "A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment". BioMed Research International 2021 (20.02.2021): 1–13. http://dx.doi.org/10.1155/2021/6666453.
Pełny tekst źródłaROHAJAWATI, Siti, Hutanti SETYODEWI, Ferryansyah Muji Agustian TRESNANTO, Debora MARIANTHI i Maruli Tua Baja SIHOTANG. "KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL". Applied Computer Science 20, nr 1 (30.03.2024): 173–88. http://dx.doi.org/10.35784/acs-2024-11.
Pełny tekst źródłaYu, Chenghao. "Walmart Sales Forecasting using Different Models". Highlights in Science, Engineering and Technology 92 (10.04.2024): 302–7. http://dx.doi.org/10.54097/kqf76062.
Pełny tekst źródłaYan, Miaomiao, i Yindong Shen. "Traffic Accident Severity Prediction Based on Random Forest". Sustainability 14, nr 3 (2.02.2022): 1729. http://dx.doi.org/10.3390/su14031729.
Pełny tekst źródłaLiu, Qian, Wanyin Qi, Yanping Wu, Yingjun Zhou i Zhiwei Huang. "Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma". Computational and Mathematical Methods in Medicine 2022 (28.08.2022): 1–6. http://dx.doi.org/10.1155/2022/2173412.
Pełny tekst źródłaWang, Hao, He Zhang, Jia Zhao, Xinyi Liu, Xinyue Feng i Yinuo Sun. "Product order-demand prediction model based on random forest". Highlights in Business, Economics and Management 18 (15.10.2023): 383–90. http://dx.doi.org/10.54097/hbem.v18i.12735.
Pełny tekst źródłaYadav, Pradeep, Chandra Prakash Bhargava, Deepak Gupta, Jyoti Kumari, Archana Acharya i Madhukar Dubey. "Breast Cancer Disease Prediction Using Random Forest Regression and Gradient Boosting Regression". International Journal of Experimental Research and Review 38 (30.04.2024): 132–46. http://dx.doi.org/10.52756/ijerr.2024.v38.012.
Pełny tekst źródłaNie, Shunqi, Honghua Chen, Xinxin Sun i Yunce An. "Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model". Sustainability 16, nr 11 (22.05.2024): 4358. http://dx.doi.org/10.3390/su16114358.
Pełny tekst źródłaHujare, Pravin, Praveen Rathod, Dinesh Kamble, Amit Jomde i Shalini Wankhede. "Predictive analytics of disc brake deformation using machine learning". Journal of Information and Optimization Sciences 45, nr 4 (2024): 1153–63. http://dx.doi.org/10.47974/jios-1699.
Pełny tekst źródłaDivya Chilukuri, Akhila Tejaswini. K, Prathyusha. K i Anjali. N. "A review on predictive model for Autisim spectrum disorder". World Journal of Advanced Engineering Technology and Sciences 12, nr 1 (30.05.2024): 218–21. http://dx.doi.org/10.30574/wjaets.2024.12.1.0204.
Pełny tekst źródłaBozorgmehr, Arezoo, Anika Thielmann i Birgitta Weltermann. "Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model". PLOS ONE 16, nr 5 (4.05.2021): e0250842. http://dx.doi.org/10.1371/journal.pone.0250842.
Pełny tekst źródłaLee, Seung-hyeong, i Eun-Ju Baek. "Development of a predictive model for university students’ core competency index using machine learning: Focusing on D University". Korean Association For Learner-Centered Curriculum And Instruction 22, nr 11 (15.06.2022): 831–49. http://dx.doi.org/10.22251/jlcci.2022.22.11.831.
Pełny tekst źródłaGroll, Andreas, Cristophe Ley, Gunther Schauberger i Hans Van Eetvelde. "A hybrid random forest to predict soccer matches in international tournaments". Journal of Quantitative Analysis in Sports 15, nr 4 (25.10.2019): 271–87. http://dx.doi.org/10.1515/jqas-2018-0060.
Pełny tekst źródłaMao, Mohan. "A Comparative Study of Random Forest Regression for Predicting House Prices Using". Highlights in Science, Engineering and Technology 85 (13.03.2024): 969–74. http://dx.doi.org/10.54097/bdfe8032.
Pełny tekst źródłaZhou, Jing, Yuzhen Li i Xuan Guo. "Predicting psoriasis using routine laboratory tests with random forest". PLOS ONE 16, nr 10 (19.10.2021): e0258768. http://dx.doi.org/10.1371/journal.pone.0258768.
Pełny tekst źródłaShao, Yakui, Zhongke Feng, Meng Cao, Wenbiao Wang, Linhao Sun, Xuanhan Yang, Tiantian Ma i in. "An Ensemble Model for Forest Fire Occurrence Mapping in China". Forests 14, nr 4 (29.03.2023): 704. http://dx.doi.org/10.3390/f14040704.
Pełny tekst źródłaChao Gao. "Balancing Interpretability and Performance: Optimizing Random Forest Algorithm Based on Point-to-Point Federated Learning". Journal of Electrical Systems 20, nr 7s (4.05.2024): 2389–400. http://dx.doi.org/10.52783/jes.3990.
Pełny tekst źródłaTruong, Tran Xuan, Viet-Ha Nhu, Doan Thi Nam Phuong, Le Thanh Nghi, Nguyen Nhu Hung, Pham Viet Hoa i Dieu Tien Bui. "A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas". Remote Sensing 15, nr 14 (8.07.2023): 3458. http://dx.doi.org/10.3390/rs15143458.
Pełny tekst źródłaLee, Soo-Kyoung, Juh Hyun Shin, Jinhyun Ahn, Ji Yeon Lee i Dong Eun Jang. "Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods". International Journal of Environmental Research and Public Health 18, nr 6 (13.03.2021): 2954. http://dx.doi.org/10.3390/ijerph18062954.
Pełny tekst źródłaMalhi, Ramandeep Kaur M., Akash Anand, Prashant K. Srivastava, G. Sandhya Kiran, George P. Petropoulos i Christos Chalkias. "An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas". ISPRS International Journal of Geo-Information 9, nr 9 (2.09.2020): 530. http://dx.doi.org/10.3390/ijgi9090530.
Pełny tekst źródłaSaladi, Sarojini Devi, i Radhika Yarlagadda. "An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm". Revue d'Intelligence Artificielle 35, nr 1 (28.02.2021): 77–83. http://dx.doi.org/10.18280/ria.350109.
Pełny tekst źródłaLe, Ngoc-Bich, Thi-Thu-Hien Pham, Sy-Hoang Nguyen, Nhat-Minh Nguyen i Tan-Nhu Nguyen. "AI-powered Predictive Model for Stroke and Diabetes Diagnostic". International Journal of Intelligent Systems and Applications 16, nr 1 (8.02.2024): 24–40. http://dx.doi.org/10.5815/ijisa.2024.01.03.
Pełny tekst źródłaRuyssinck, Joeri, Joachim van der Herten, Rein Houthooft, Femke Ongenae, Ivo Couckuyt, Bram Gadeyne, Kirsten Colpaert, Johan Decruyenaere, Filip De Turck i Tom Dhaene. "Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit". Computational and Mathematical Methods in Medicine 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7087053.
Pełny tekst źródłaAlfraihat, Ausilah, Amer F. Samdani i Sriram Balasubramanian. "Predicting curve progression for adolescent idiopathic scoliosis using random forest model". PLOS ONE 17, nr 8 (11.08.2022): e0273002. http://dx.doi.org/10.1371/journal.pone.0273002.
Pełny tekst źródłaLei, Xiaoli. "Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest". Mathematical Problems in Engineering 2022 (21.05.2022): 1–8. http://dx.doi.org/10.1155/2022/8765166.
Pełny tekst źródłaQi, Yuxuan. "Research on Stock Price Prediction Based on LSTM Model and Random Forest". Advances in Economics, Management and Political Sciences 86, nr 1 (28.05.2024): 35–42. http://dx.doi.org/10.54254/2754-1169/86/20240938.
Pełny tekst źródłaBayramli, Ilkin, Victor Castro, Yuval Barak-Corren, Emily M. Madsen, Matthew K. Nock, Jordan W. Smoller i Ben Y. Reis. "Temporally informed random forests for suicide risk prediction". Journal of the American Medical Informatics Association 29, nr 1 (2.11.2021): 62–71. http://dx.doi.org/10.1093/jamia/ocab225.
Pełny tekst źródłaBayramli, Ilkin, Victor Castro, Yuval Barak-Corren, Emily M. Madsen, Matthew K. Nock, Jordan W. Smoller i Ben Y. Reis. "Temporally informed random forests for suicide risk prediction". Journal of the American Medical Informatics Association 29, nr 1 (2.11.2021): 62–71. http://dx.doi.org/10.1093/jamia/ocab225.
Pełny tekst źródłaWang, Shihao, i Xiangxiang Wu. "The Mechanical Performance Prediction of Steel Materials based on Random Forest". Frontiers in Computing and Intelligent Systems 6, nr 1 (27.11.2023): 1–3. http://dx.doi.org/10.54097/fcis.v6i1.01.
Pełny tekst źródłaNikolopoulos, Efthymios I., Elisa Destro, Md Abul Ehsan Bhuiyan, Marco Borga i Emmanouil N. Anagnostou. "Evaluation of predictive models for post-fire debris flow occurrence in the western United States". Natural Hazards and Earth System Sciences 18, nr 9 (4.09.2018): 2331–43. http://dx.doi.org/10.5194/nhess-18-2331-2018.
Pełny tekst źródłaGold, Ochim, i Agaji Iorshase. "Heart failure prediction framework using random forest and J48 with Adaboost algorithms". Science World Journal 18, nr 2 (20.10.2023): 165–75. http://dx.doi.org/10.4314/swj.v18i2.1.
Pełny tekst źródłaGuo, Shengnan, i Jianqiu Xu. "CPRQ: Cost Prediction for Range Queries in Moving Object Databases". ISPRS International Journal of Geo-Information 10, nr 7 (8.07.2021): 468. http://dx.doi.org/10.3390/ijgi10070468.
Pełny tekst źródłaFernández-Carrillo, Ángel, Antonio Franco-Nieto, María Julia Yagüe-Ballester i Marta Gómez-Giménez. "Predictive Model for Bark Beetle Outbreaks in European Forests". Forests 15, nr 7 (27.06.2024): 1114. http://dx.doi.org/10.3390/f15071114.
Pełny tekst źródłaERSHOV, EVGENY V., OLGA V. YUDINA, LYUDMILA N. VINOGRADOVA i NIKITA I. SHAKHANOV. "EQUIPMENT CONDITION MODELING BASED ON RANDOM FOREST AND ARIMA MACHINE LEARNING ALGORITHM STACKING". Cherepovets State University Bulletin 4, nr 97 (2020): 32–40. http://dx.doi.org/10.23859/1994-0637-2020-4-97-3.
Pełny tekst źródłaHuan, Juan, Bo Chen, Xian Gen Xu, Hui Li, Ming Bao Li i Hao Zhang. "River Dissolved Oxygen Prediction Based on Random Forest and LSTM". Applied Engineering in Agriculture 37, nr 5 (2021): 901–10. http://dx.doi.org/10.13031/aea.14496.
Pełny tekst źródłaQu, Chaoran, Xiufen Yang, Weisi Peng, Xiujuan Wang i Weixiang Luo. "THE PREDICTIVE EFFECT OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PRESSURE INJURIES: A NETWORK META-ANALYSES". Innovation in Aging 7, Supplement_1 (1.12.2023): 1178. http://dx.doi.org/10.1093/geroni/igad104.3776.
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