Zeitschriftenartikel zum Thema „Random Forest predictive model“
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Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos und 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.
Der volle Inhalt der QuelleWang, Fangyi, Yongchao Wang, Xiaokang Ji und 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.
Der volle Inhalt der QuelleKor, 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.
Der volle Inhalt der QuelleRigatti, Steven J. „Random Forest“. Journal of Insurance Medicine 47, Nr. 1 (01.01.2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.
Der volle Inhalt der QuelleWei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang und Jing-Yuan Wang. „Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†“. Frontiers of Nursing 8, Nr. 3 (01.09.2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
Der volle Inhalt der QuelleDiaz, Pablo, Juan C. Salas, Aldo Cipriano und Felipe Núñez. „Random forest model predictive control for paste thickening“. Minerals Engineering 163 (März 2021): 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.
Der volle Inhalt der QuelleMao, Yiwen, und Asgeir Sorteberg. „Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest“. Weather and Forecasting 35, Nr. 6 (Dezember 2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.
Der volle Inhalt der QuelleBashir Suleiman, Aminu, Stephen Luka und 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.
Der volle Inhalt der QuelleJeong, Hoyeon, Youngjune Kim und 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.
Der volle Inhalt der QuelleEmir, Senol, Hasan Dincer, Umit Hacioglu und 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.
Der volle Inhalt der QuelleNie, Ying, und 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.
Der volle Inhalt der QuelleRen, 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.
Der volle Inhalt der QuelleMathew, 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.
Der volle Inhalt der QuelleWang, Zijie, Yufang Bi, Gang Lu, Xu Zhang, Xiangyang Xu, Yilin Ning, Xuhua Du und Anke Wang. „Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach“. Forests 15, Nr. 2 (07.02.2024): 318. http://dx.doi.org/10.3390/f15020318.
Der volle Inhalt der QuelleZhou, Shu-Ping, Su-Ding Fei, Hui-Hui Han, Jing-Jing Li, Shuang Yang und 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.
Der volle Inhalt der QuelleROHAJAWATI, Siti, Hutanti SETYODEWI, Ferryansyah Muji Agustian TRESNANTO, Debora MARIANTHI und 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.
Der volle Inhalt der QuelleYu, 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.
Der volle Inhalt der QuelleYan, Miaomiao, und Yindong Shen. „Traffic Accident Severity Prediction Based on Random Forest“. Sustainability 14, Nr. 3 (02.02.2022): 1729. http://dx.doi.org/10.3390/su14031729.
Der volle Inhalt der QuelleLiu, Qian, Wanyin Qi, Yanping Wu, Yingjun Zhou und 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.
Der volle Inhalt der QuelleWang, Hao, He Zhang, Jia Zhao, Xinyi Liu, Xinyue Feng und 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.
Der volle Inhalt der QuelleYadav, Pradeep, Chandra Prakash Bhargava, Deepak Gupta, Jyoti Kumari, Archana Acharya und 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.
Der volle Inhalt der QuelleNie, Shunqi, Honghua Chen, Xinxin Sun und 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.
Der volle Inhalt der QuelleHujare, Pravin, Praveen Rathod, Dinesh Kamble, Amit Jomde und 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.
Der volle Inhalt der QuelleDivya Chilukuri, Akhila Tejaswini. K, Prathyusha. K und 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.
Der volle Inhalt der QuelleBozorgmehr, Arezoo, Anika Thielmann und 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 (04.05.2021): e0250842. http://dx.doi.org/10.1371/journal.pone.0250842.
Der volle Inhalt der QuelleLee, Seung-hyeong, und 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.
Der volle Inhalt der QuelleGroll, Andreas, Cristophe Ley, Gunther Schauberger und 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.
Der volle Inhalt der QuelleMao, 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.
Der volle Inhalt der QuelleZhou, Jing, Yuzhen Li und 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.
Der volle Inhalt der QuelleShao, Yakui, Zhongke Feng, Meng Cao, Wenbiao Wang, Linhao Sun, Xuanhan Yang, Tiantian Ma et al. „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.
Der volle Inhalt der QuelleChao Gao. „Balancing Interpretability and Performance: Optimizing Random Forest Algorithm Based on Point-to-Point Federated Learning“. Journal of Electrical Systems 20, Nr. 7s (04.05.2024): 2389–400. http://dx.doi.org/10.52783/jes.3990.
Der volle Inhalt der QuelleTruong, Tran Xuan, Viet-Ha Nhu, Doan Thi Nam Phuong, Le Thanh Nghi, Nguyen Nhu Hung, Pham Viet Hoa und 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 (08.07.2023): 3458. http://dx.doi.org/10.3390/rs15143458.
Der volle Inhalt der QuelleLee, Soo-Kyoung, Juh Hyun Shin, Jinhyun Ahn, Ji Yeon Lee und 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.
Der volle Inhalt der QuelleMalhi, Ramandeep Kaur M., Akash Anand, Prashant K. Srivastava, G. Sandhya Kiran, George P. Petropoulos und 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 (02.09.2020): 530. http://dx.doi.org/10.3390/ijgi9090530.
Der volle Inhalt der QuelleSaladi, Sarojini Devi, und 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.
Der volle Inhalt der QuelleLe, Ngoc-Bich, Thi-Thu-Hien Pham, Sy-Hoang Nguyen, Nhat-Minh Nguyen und Tan-Nhu Nguyen. „AI-powered Predictive Model for Stroke and Diabetes Diagnostic“. International Journal of Intelligent Systems and Applications 16, Nr. 1 (08.02.2024): 24–40. http://dx.doi.org/10.5815/ijisa.2024.01.03.
Der volle Inhalt der QuelleRuyssinck, Joeri, Joachim van der Herten, Rein Houthooft, Femke Ongenae, Ivo Couckuyt, Bram Gadeyne, Kirsten Colpaert, Johan Decruyenaere, Filip De Turck und 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.
Der volle Inhalt der QuelleAlfraihat, Ausilah, Amer F. Samdani und 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.
Der volle Inhalt der QuelleLei, 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.
Der volle Inhalt der QuelleQi, 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.
Der volle Inhalt der QuelleBayramli, Ilkin, Victor Castro, Yuval Barak-Corren, Emily M. Madsen, Matthew K. Nock, Jordan W. Smoller und Ben Y. Reis. „Temporally informed random forests for suicide risk prediction“. Journal of the American Medical Informatics Association 29, Nr. 1 (02.11.2021): 62–71. http://dx.doi.org/10.1093/jamia/ocab225.
Der volle Inhalt der QuelleBayramli, Ilkin, Victor Castro, Yuval Barak-Corren, Emily M. Madsen, Matthew K. Nock, Jordan W. Smoller und Ben Y. Reis. „Temporally informed random forests for suicide risk prediction“. Journal of the American Medical Informatics Association 29, Nr. 1 (02.11.2021): 62–71. http://dx.doi.org/10.1093/jamia/ocab225.
Der volle Inhalt der QuelleWang, Shihao, und 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.
Der volle Inhalt der QuelleNikolopoulos, Efthymios I., Elisa Destro, Md Abul Ehsan Bhuiyan, Marco Borga und 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 (04.09.2018): 2331–43. http://dx.doi.org/10.5194/nhess-18-2331-2018.
Der volle Inhalt der QuelleGold, Ochim, und 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.
Der volle Inhalt der QuelleGuo, Shengnan, und Jianqiu Xu. „CPRQ: Cost Prediction for Range Queries in Moving Object Databases“. ISPRS International Journal of Geo-Information 10, Nr. 7 (08.07.2021): 468. http://dx.doi.org/10.3390/ijgi10070468.
Der volle Inhalt der QuelleFernández-Carrillo, Ángel, Antonio Franco-Nieto, María Julia Yagüe-Ballester und 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.
Der volle Inhalt der QuelleERSHOV, EVGENY V., OLGA V. YUDINA, LYUDMILA N. VINOGRADOVA und 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.
Der volle Inhalt der QuelleHuan, Juan, Bo Chen, Xian Gen Xu, Hui Li, Ming Bao Li und 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.
Der volle Inhalt der QuelleQu, Chaoran, Xiufen Yang, Weisi Peng, Xiujuan Wang und Weixiang Luo. „THE PREDICTIVE EFFECT OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PRESSURE INJURIES: A NETWORK META-ANALYSES“. Innovation in Aging 7, Supplement_1 (01.12.2023): 1178. http://dx.doi.org/10.1093/geroni/igad104.3776.
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