Search Results - "Random forest algorithms"

Impact of Attachment on Emotional Experience and Expression: A Physiological and Acoustic Investigation.

Authors: Hu, Han Gu, Wentao

Source: Journal of Speech, Language & Hearing Research. Jul2026, Vol. 69 Issue 7, p3064-3084. 21p.

Subject Terms: *Fear, *Emotion regulation, *Attachment behavior, *Emotions, *College students, Random forest algorithms, Sadness, Grammar, Research funding, Anger, Questionnaires, Multivariate analysis, Descriptive statistics, Physiological aspects of speech, Happiness, Analysis of variance, Statistics, Conceptual structures, Motion pictures, Semantics, Data analysis software, Sensitivity & specificity (Statistics)

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.

AI-Enabled Talent Acquisition and Its Impact on Organizational Performance.

Authors: Thamaraiselvi, P. D. E., Kaviya D'Souza, Anthony Philip et al.

Source: Advances in Consumer Research. Jun2026, Vol. 3 Issue 6, p420-427. 8p.

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.

U2S-HS: Unsupervised-to-Supervised Haze Stratification for Adaptive Dehazing with Multi-Profile EDCP.

Authors: Hafidh, Fathul Shidik, Guruh Fajar Syukur, Abdul et al.

Source: IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2834-2851. 18p.

Subjects: Random forest algorithms, K-means clustering, Machine learning, Supervised learning, Remote sensing

Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets.

Authors: Marković, Ivana Ljajić, Adela Stanković, Jelena Z. et al.

Source: Computer Science & Information Systems. Jun2026, Vol. 23 Issue 3, p969-1000. 32p.

Subjects: Environmental, social, & governance factors, Random forest algorithms, Machine learning, Knowledge transfer, Emerging markets, Sustainable investing