Search Results - "Random forest algorithms"

Teachers' Knowledge, Attitudes, and Behaviors Regarding Hearing Loss and Inclusive Education.

Authors: Sendesen, İrem Kamışlı, Gurbet İpek Şahin Sönmez, Gamze

Source: Language, Speech & Hearing Services in Schools. Jul2026, Vol. 57 Issue 3, p916-925. 10p.

Subject Terms: *Psychology of teachers, *Data analysis, *Attitudes toward disabilities, *Mainstreaming in special education, *College teacher attitudes, *Research methodology, *Machine learning, *Factor analysis, Random forest algorithms, Cross-sectional method, Self-efficacy, Hearing aids, Rehabilitation, Questionnaires, Multiple regression analysis, Private sector, Multivariate analysis, Judgment sampling, Mann Whitney U Test, Descriptive statistics, Professions, Social skills, Statistics, Social support, Data analysis software, Hard of hearing people, Professional competence, Nonparametric statistics

Geographic Terms: Türkiye

Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.

Authors: Gearhardt, Ashley N. Hutelin, Zach Nartey, Emmanuel et al.

Source: American Journal of Public Health. Jul2026, Vol. 116 Issue 7, p950-959. 10p.

Machine learning approaches for electricity price prediction: A case study on the day - ahead market.

Authors: Niste, Daniela F. Pavel, Sorin Gh. Beleiu, Horia G. et al.

Source: EPJ Web of Conferences. 6/11/2026, Vol. 372, p1-7. 7p.

Machine Learning-Based Prediction of Thermal Conductivity in Bio-Composite Materials.

Authors: Er-rradi, Hafida Idchabani, Rachida Bouchefra, Imane

Source: EPJ Web of Conferences. 5/30/2026, Vol. 371, p1-8. 8p.

Land-use/cover change and future prediction by integrating the ML techniques of random forest and CA-Markov chain model of the Ganges alluvial tract of Eastern India.

Authors: Roy, Kailash Chandra Soren, David Durjoy Lal Biswas, Brototi

Source: Environment, Development & Sustainability. Jul2026, Vol. 28 Issue 7, p15635-15662. 28p.

Subject Terms: *Land use, *Random forest algorithms, *Alluvial plains, *Land use planning, *Cellular automata, *Watersheds, *Ecological impact

Geographic Terms: India

Continental-scale prediction of hydrologic signatures and processes.

Authors: Araki, Ryoko Holt, Anne Hammond, John C. et al.

Source: Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 11, p3647-3673. 27p.

Subject Terms: *Watersheds, *Random forest algorithms, *Hydrologic models, *Soils, *Environmental engineering, *Water management, *Continents, *Hydrologic cycle

Geographic Terms: United States

Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests.

Authors: El Ouahabi, Taha-Abderrahman Bourgin, François Perrin, Charles et al.

Source: Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 11, p3549-3574. 26p.

Subject Terms: *Random forest algorithms, *Streamflow, *Machine learning, *Hydrological forecasting, *Watersheds, *Hydrologic models

基于 KPCA-PSO-RF 矿井突水水源判别模型.

Alternate Title: Discrimination of mine water inrush source based on KPCA-PSO-RF model.

Authors: 刘伟韬 卢润时 杜衍辉

Source: Coal Science & Technology (0253-2336). May2026, Vol. 54 Issue 5, p378-392. 15p.

Subject Terms: *Mine water, *Random forest algorithms, *Optimization algorithms, *Particle swarm optimization, *Machine learning, *Composition of water, *Dimensional reduction algorithms

Research on identification, separation and mechanism of soft and hard gangue from raw coal gangue via dual-energy X-ray based on machine learning.

Authors: Zhao, Haitao Ma, Xiaomin Fan, Yuping et al.

Source: International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 6, p1713-1734. 22p.

Subject Terms: *Machine learning, *Random forest algorithms, *X-ray absorption, *Coal mine waste, *Mineral analysis, *X-ray imaging

Simulation and Correction Study of Solar Irradiance in Guangdong Based on WRF-Solar and Random Forest.

Authors: He, Yuanhong Li, Zheng Zhou, Fang et al.

Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2077. 18p.

Subject Terms: *Random forest algorithms, *Calibration, *Energy industry forecasting, *Numerical weather forecasting, *Solar radiation

Geographic Terms: China, Guangdong Sheng (China)

Machine Learning-Based Recommendation Support for Personalized Marketing Information Systems: A Comparative Study.

Authors: Oktaviani, Intan Triana Triyono

Source: Ingénierie des Systèmes d'Information. May2026, Vol. 31 Issue 5, p1463-1472. 10p.

Multi-Timeframe Feature Engineering for Bitcoin Market Prediction: A Price-Level-Agnostic Machine Learning Approach.

Authors: Sobreiro, Pedro Martinho, Domingos Martins, Rui et al.

Source: Forecasting. Jun2026, Vol. 8 Issue 3, p40. 27p.

Predicting Financial Distress Using Machine Learning Techniques.

Authors: Sethi, Pallavi Singh, Archana Gupta, Vikas

Source: Asia-Pacific Financial Markets. Jun2026, Vol. 33 Issue 2, p857-879. 23p.

一种基于电磁地图的物理层无线入侵检测策略.

Alternate Title: A Strategy of Physical-layer Wireless Intrusion Detection Based on Electromagnetic Maps.

Authors: 张佳琪 王元杰 韩瑞昌 et al.

Source: Telecommunication Engineering. 5/28/2026, Vol. 66 Issue 5, p799-809. 11p.

基于优化随机森林算法的复合杉木精油防腐性能研究.

Alternate Title: Optimization of Preservative Performance in Compound Camphorwood Essential Oil Using a Genetic Algorithm-optimized Random Forest Model.

Authors: 肖 粲 朱正坤

Source: China Forest Products Industry. May2026, Vol. 63 Issue 5, p98-105. 8p.

Shared dockless e-scooter and metro station built environment: its nonlinear relationship using random forest modeling.

Authors: Yang, Wookjae Kim, Keuntae Kim, Junsik

Source: Transportation Planning & Technology. Jun2026, Vol. 49 Issue 4, p732-753. 22p.

Prediction of furniture exports in Türkiye using machine learning methods.

Authors: AKYÜZ, İlker ERSEN, Nadir BARDAK, Timuçin et al.

Source: Turkish Journal of Agriculture & Forestry. 2026, Vol. 50 Issue 3, p342-356. 15p.

Subjects: Machine learning, Random forest algorithms, Decision trees, Deep learning, Economic indicators, Furniture industry, Economic forecasting

Geographic Terms: Türkiye

Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.

Authors: Su, Weidi Cheng, Yaofei Wei, Li et al.

Source: Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2511. 27p.

Subjects: Random forest algorithms, Compressive strength, Machine learning, Structural engineering, Concrete-filled tubes, Prediction models, Mechanical behavior of materials

Preliminary Feasibility of a Single-Channel Nighttime Cloud Detection in Artificially Lit Regions Using Ground Light Source Observations from VIIRS/DNB Images.

Authors: Chen, Mingyu Hu, Shensen Li, Haoran et al.

Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1956. 25p.

Subjects: Light scattering, Random forest algorithms, Radar, Remote sensing, Machine learning

Improved Regional Atmospheric Weighted Mean Temperature Modeling Using a Decadal Dataset and Machine Learning Methods over China.

Authors: Hu, Zuquan Liang, Hong Zhang, Peng et al.

Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1925. 20p.

Subjects: Random forest algorithms, Machine learning, Meteorology, Meteorological databases, Atmospheric temperature measurements, Atmospheric water vapor

Geographic Terms: China