Suchergebnisse - "Random forest algorithms"

Man versus Machine Learning Revisited.

Autoren: Zhang, Yingguang Zhu, Yandi Linnainmaa, Juhani T

Quelle: Review of Financial Studies. Dec2025, Vol. 38 Issue 12, p3768-3790. 23p.

Schlagwörter: *FINANCIAL analysts, *FORECASTING, *RATE of return on stocks, RANDOM forest algorithms, MACHINE learning

Predicting the length-of-stay of pediatric patients using machine learning algorithms.

Autoren: Boff Medeiros, Natália Fogliatto, Flávio Sanson Karla Rocha, Miriam et al.

Quelle: International Journal of Production Research. Jan2025, Vol. 63 Issue 2, p483-496. 14p.

Schlagwörter: RANDOM forest algorithms, MACHINE learning, LENGTH of stay in hospitals, CHILDREN'S injuries, FOREST productivity, CHILD patients

Forecasting scope creep in Egyptian construction projects: an evaluation using Artificial Neural Network (ANN) and Random Forest models.

Autoren: ElAlem, Mahmoud Ahdy Mahdi, Ibrahim Mahmoud Mohamadien, Hassan A. et al.

Quelle: International Journal of Construction Management. Dec2025, Vol. 25 Issue 16, p2082-2101. 20p.

Schlagwörter: *ARTIFICIAL neural networks, *COST overruns, *PROJECT management, RANDOM forest algorithms, EGYPTIAN history, MACHINE learning, CONSTRUCTION projects

Geografische Kategorien: EGYPT

A comparative analysis of ensemble learning models for predicting lapses in investment policies.

Autoren: Matlala, Lerato Bokaba, Tebogo Ndayizigamiye, Patrick et al.

Quelle: Journal of Management Analytics. Oct2025, p1-30. 30p. 13 Illustrations.

Schlagwörter: *INVESTMENT policy, RANDOM forest algorithms, MACHINE learning, ENSEMBLE learning, BOOSTING algorithms

Prediction drug release profile from chitosan nanoparticles: integration of experimental data and machine learning models.

Autoren: Rastegari, Ali Faghihi, Homa Mobinikhaledi, Mahta

Quelle: Drug Development & Industrial Pharmacy. Dec2025, Vol. 51 Issue 12, p1819-1827. 9p.

Schlagwörter: MACHINE learning, RANDOM forest algorithms, PHARMACEUTICAL chemistry, CONTROLLED release drugs, BOOSTING algorithms, NANOPARTICLES

Forecasting market returns using machine learning: evidence from Random Forest models.

Autoren: Zare, Mohammad¹ (AUTHOR) m.zare@alzahra.ac.ir

Quelle: Applied Economics Letters. Oct2025, p1-5. 5p. 2 Illustrations.

Schlagwörter: *FORECASTING, *ECONOMIC forecasting, *STANDARD & Poor's 500 Index, *RATE of return on stocks, RANDOM forest algorithms, MACHINE learning, PREDICTION models, NONLINEAR functions

MR-RF: A robust data mining method for multiresponse optimization using a random forest.

Autoren: Koo, Dong-Hyun Lee, Dong-Hee

Quelle: Quality Engineering. 2025, Vol. 37 Issue 4, p612-630. 19p.

Schlagwörter: *DATA mining, *MATHEMATICAL optimization, *ELECTRONIC data processing, *EMPIRICAL research, RANDOM forest algorithms, MODEL validation, SENSITIVITY analysis

Research on Electricity Anomaly Monitoring Based on Electricity Consumption Data Collection and Classification Modeling Methods.

Autoren: Xie, Ruibiao Zhou, Dongxu Yang, Yuyao et al.

Quelle: International Journal of High Speed Electronics & Systems. Dec2025, Vol. 34 Issue 4, p1-14. 14p.

Schlagwörter: *ELECTRIC power distribution grids, RANDOM forest algorithms, ELECTRIC power consumption, CLASSIFICATION algorithms, ENTROPY (Information theory)

Augmentation of PM1.0 measurements based on machine learning model and environmental factors.

Autoren: Hwang, Hyemin Kim, Chang Hyeok Park, Jong-Sung et al.

Quelle: Journal of Environmental Sciences (Elsevier). Oct2025, Vol. 156, p91-101. 11p.

Schlagwörter: MACHINE learning, RANDOM forest algorithms, PARTICULATE matter, CITIES & towns, FACTOR analysis

A comprehensive study on the interplay between dataset characteristics and oversampling methods.

Autoren: Yang, Yue Fang, Tangtangfang Hu, Jinyang et al.

Quelle: Journal of the Operational Research Society. Oct2025, Vol. 76 Issue 10, p1981-2002. 22p.

Schlagwörter: MACHINE learning, RANDOM forest algorithms, RECEIVER operating characteristic curves, SKEWNESS (Probability theory), DATA augmentation

Random forests regression for soft interval data.

Autoren: Gaona-Partida, Paul Yeh, Chih-Ching Sun, Yan et al.

Quelle: Communications in Statistics: Simulation & Computation. 2025, Vol. 54 Issue 11, p4821-4840. 20p.

Schlagwörter: *REGRESSION analysis, RANDOM forest algorithms, NONLINEAR statistical models, INTERVAL analysis, MINIMUM variance estimation

Prediction of the End of a Romantic Relationship in Peruvian Youth and Adults: A Machine Learning Approach.

Autoren: Ventura-León, José Lino-Cruz, Cristopher Sánchez-Villena, Andy Rick et al.

Quelle: Journal of General Psychology. Oct-Dec2025, Vol. 152 Issue 4, p577-598. 22p.

Schlagwörter: *INTERPERSONAL relations, MACHINE learning, RANDOM forest algorithms, ROMANTIC love, LOGISTIC regression analysis

Adaptive trust evaluation model based on container security analysis in the Docker platform.

Autoren: Li, Tao Zhang, Yanyi

Quelle: International Journal of Information Security. Oct2025, Vol. 24 Issue 5, p1-14. 14p.

Schlagwörter: *RISK assessment, TRUST, RANDOM forest algorithms, INTERNET security, K-means clustering

Machining performance evaluation in turning of hardened steel using machine learning.

Autoren: Ambhore, Nitin Naranje, Vishal Shelke, Sneha

Quelle: Materials & Manufacturing Processes. 2025, Vol. 40 Issue 14, p1935-1942. 8p.

Schlagwörter: MACHINE learning, RANDOM forest algorithms, MACHINE performance, SURFACE finishing, SURFACE roughness

The effect of green, supply chain factors in predicting China's stock price crash risk: evidence from random forest model.

Autoren: Xu, Xin Ye, Tao Gao, Jieying et al.

Quelle: Environment, Development & Sustainability. Oct2025, Vol. 27 Issue 10, p23591-23614. 24p.

Schlagwörter: *LIQUIDITY (Economics), *FINANCIAL crises, RANDOM forest algorithms, GREEN behavior, MACHINE learning, ECONOMIC conditions in China

Geografische Kategorien: CHINA

Leveraging machine learning techniques for early detection of Parkinson's disease.

Autoren: Singh, Robin Gulati, Seema Mohan, Aadershi et al.

Quelle: AIP Conference Proceedings. 2025, Vol. 3357 Issue 1, p1-11. 11p.

Schlagwörter: *RANDOM forest algorithms, *PARKINSON'S disease, *NEUROLOGICAL disorders, *DECISION trees, *MEDICAL care, *EARLY diagnosis, *MACHINE learning, *SUPPORT vector machines

Study of machine learning algorithms for precise detection of volatile organic compound.

Autoren: Chowdhury, Paramita Dutta, Mahendra Roy, Sunipa et al.

Quelle: AIP Conference Proceedings. 2025, Vol. 3394 Issue 1, p1-13. 13p.

Schlagwörter: *RANDOM forest algorithms, *VOLATILE organic compounds, *MACHINE learning, *ACETONE, *THIN film devices, *CLASSIFICATION, *ZINC oxide thin films

Disease prediction and doctor recommendation using machine learning.

Autoren: Karuna, Gotlur Praveen, Boda Dasari, Phanni Sai et al.

Quelle: AIP Conference Proceedings. 2025, Vol. 3361 Issue 1, p1-6. 6p.

Schlagwörter: *MACHINE learning, *RANDOM forest algorithms, *MEDICAL referrals, *MEDICAL records, *DECISION trees, *NAIVE Bayes classification, *PROGNOSIS, *MEDICAL care

Advancing sign language recognition with CNN and random forest for real-time accessibility.

Autoren: Siri, Dharmapuri Nagavarshith, Siddoju Krishnamachary, Pathuri et al.

Quelle: AIP Conference Proceedings. 2025, Vol. 3361 Issue 1, p1-9. 9p.

Schlagwörter: *CONVOLUTIONAL neural networks, *RANDOM forest algorithms, *DEAF people, *COMMUNICATION barriers, *DEEP learning, *ASSISTIVE technology, *SIGN language

Landslide susceptibility assessment based on random forest machine learning algorithm using rare training points, Case study Bandung district, West Java Province, Indonesia.

Autoren: Nugroho, Hary Sari, Dewi Kania

Quelle: AIP Conference Proceedings. 2025, Vol. 3351 Issue 1, p1-10. 10p.

Schlagwörter: *LANDSLIDES, *RANDOM forest algorithms, *PROVINCES, *LANDSLIDE hazard analysis, *MACHINE learning, *SAMPLING methods, *HAZARD mitigation

Geografische Kategorien: BANDUNG (Indonesia), JAWA Barat (Indonesia), INDONESIA