Search Results - "Random forests"
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1
Authors:
Source: Geo-spatial Information Science. 28(3):945-965
Subject Terms: Ecosystems, Forestry, Land use, Landsat, Time series analysis, Agro-ecological zones, Forest-agriculture mosaic landscape, Human-appropriated natural land cover, Intensity analysis, Land cover, Mosaic landscapes, Nigeria, Random forests, Tree cover, Wetlands, Geomatik, Geomatics
File Description: electronic
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2
Authors: et al.
Source: I2Connect Procedia Computer Science. 257:777-784
Subject Terms: Decision Trees, Random Forests, Vehicle Trajectory Prediction, Collision Risk Estimation, Vulnerable Road Users (VRUs), Intelligent Transportation Systems, Urban Traffic Safety, Human-Machine Interfaces (HMIs), Edge Computing, Real-Time Prediction, Distributed Real-Time Systems, Distribuerade realtidssystem (DRTS), Interaction Lab (ILAB), Skövde Artificial Intelligence Lab (SAIL)
File Description: electronic
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3
Authors: et al.
Source: Neural Computing & Applications. 37:597-610
Subject Terms: Class-imbalance, Melt-pool defects classification, Aerospace application, Additive Manufacturing, Polar Transformation, Random Forests
File Description: electronic
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4
Authors: et al.
Source: IEEE Access. 13:54561-54584
Subject Terms: Codes, Accuracy, Vocabulary, Java, Feature extraction, Python, Training, Reproducibility of results, Random forests, Programming, Flaky tests, non-deterministic tests, machine learning, artificial intelligence, software testing
File Description: electronic
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5
Authors: et al.
Source: IEEE Open Journal of Instrumentation and Measurement. 4
Subject Terms: Anomaly Detection, Conveyor Belt, Edge Computing, Industry 4.0, Low-power Microcontroller, Machine Learning, Tinyml, Data Handling, Data Mining, Decision Trees, Failure (mechanical), Heuristic Methods, Learning Systems, Microcontrollers, Pattern Matching, Pattern Recognition Systems, Preventive Maintenance, Random Forests, Bulk Materials, Conveyor Belts, Duty-cycle, F1 Scores, Low-power Microcontrollers, Machine-learning, Mining Operations, Belt Conveyors
File Description: electronic
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6
Authors: et al.
Source: Cluster Computing. 28(1)
Subject Terms: Code smell detection, Machine learning, Maintainability, Software quality, Computer software selection and evaluation, Random forests, Code smell, Feature data, Features selection, Machine learning techniques, Machine-learning, Numerical information, Status informations, Training sets
File Description: electronic
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7
Authors: Junsong Wang
Source: Electronic Journal of Structural Engineering, Vol 25, Iss 3 (2025)
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8
Authors:
Source: Social Indicators Research. 179:955-978
Subject Terms: connecting data-driven insights with theory-building. By incorporating multidimensionality and non-linear interactions, Subjective Well-Being (SWB) has emerged as a key measure in assessing societal progress beyond traditional economic indicators like GDP. While SWB is shaped by diverse socio-economic factors, most quantitative studies use limited variables and overlook non-linearities and interactions. We address these gaps by applying random forests to predict regional SWB averages across 388 OECD regions using 2016 data. Our model identifies 16 key predictors of regional SWB, revealing significant non-linearities and interactions among variables. Notably, the sex ratio among the elderly, a factor underexplored in existing literature, emerges as a predictor comparable in importance to average disposable income. Interestingly, regions with below-average employment and elderly sex ratios show higher SWB than average, but this trend reverses at higher levels. This study highlights the potential of machine learning to explore complex socio-economic systems, connecting data-driven insights with theory-building. By incorporating multidimensionality and non-linear interactions, our approach offers a robust framework for analyzing SWB and informing policy design, but this trend reverses at higher levels. This study highlights the potential of machine learning to explore complex socio-economic systems, Settore ECON-01/A - Economia politica, Subjective Well-Being (SWB) has emerged as a key measure in assessing societal progress beyond traditional economic indicators like GDP. While SWB is shaped by diverse socio-economic factors, most quantitative studies use limited variables and overlook non-linearities and interactions. We address these gaps by applying random forests to predict regional SWB averages across 388 OECD regions using 2016 data. Our model identifies 16 key predictors of regional SWB, revealing significant non-linearities and interactions among variables. Notably, the sex ratio among the elderly, regions with below-average employment and elderly sex ratios show higher SWB than average, our approach offers a robust framework for analyzing SWB and informing policy design, emerges as a predictor comparable in importance to average disposable income. Interestingly, a factor underexplored in existing literature
File Description: application/pdf
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9
Authors: et al.
Source: Applied Research in Quality of Life
Applied research in quality of life., Springer Science and Business Media B.V., 2025, vol. 20, iss. 3, p. 1289-1313.Subject Terms: Religiosity, Interdependent happiness, Family happiness, Happiness idealization, Random forests, family happiness, happiness idealization, interdependent happiness, random forests, religiosity, Satisfaction with Life
File Description: application/pdf
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10
Authors: et al.
Source: 2025 IEEE 5th International Conference on Software Engineering and Artificial Intelligence (SEAI). :7-10
Subject Terms: Random Forests, Machine-Learning, Support Vector Regression, Sample Covariances, Risk Assessment, Electronic Trading, Insurance, Markowitz Modern Portfolio Theory, Budget Control, Portfolio Optimization, Markowitz, Investments, Financial Markets, Covariance Matrices, Based Covariance Estimation, Risk Management, Decision Trees, Statistics, Commerce, Modern Portfolio Theories, Forestry, Robo-Advisory Applications, Costs, Benchmarking, Robo-Advisory Application, Risk Perception, Covariance Matrix, Covariance Estimation, Forecasting
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11
Authors: et al.
Source: OCEANS 2025 Brest. :1-5
Subject Terms: Random Forests, wind velocity, significant wave height, wave peak period, artificial neural network - multi layer perceptron
File Description: application/pdf
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12
Authors:
Source: The International Journal of Chronic Obstructive Pulmonary Disease. 18:1457-1473
Subject Terms: COX proportional hazards, machine learning, mHealth, random forests, random survival forests, telehealth or digital health, Disease Progression, Humans, Pulmonary Disease, Chronic Obstructive, Sweden, chronic obstructive lung disease, disease exacerbation, human
File Description: print
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13
Authors: et al.
Source: IATSS Research. 47(3):305-317
Subject Terms: Classification, Daylight chromaticity, Machine learning algorithms, Prediction, Regression, Road signs, Accident prevention, Color, Forecasting, Forestry, Learning algorithms, Learning systems, Motor transportation, Regression analysis, Roads and streets, Support vector machines, Color levels, Machine learning models, Random forests, Regression modelling, Road safety, Supervised machine learning, Neural networks
File Description: electronic
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14
Authors: et al.
Source: Electronic Journal of Structural Engineering, Vol 25, Iss 2 (2025)
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15
Authors: et al.
Source: Agricultural Economics (AGRICECON), Vol 71, Iss 4, Pp 173-184 (2025)
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16
Authors: et al.
Source: Proceedings of the 17th International Conference on Agents and Artificial Intelligence. :859-869
Subject Terms: Random Forests, FOS: Computer and information sciences, Computer Science - Machine Learning, Upside-Down Reinforcement Learning, Neural Networks, Explainable AI, Interpretability, Machine Learning (cs.LG)
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17
Authors: et al.
Source: Engineering Applications of Artificial Intelligence. 143
Subject Terms: 46 Information and Computing Sciences (for-2020), 4014 Manufacturing Engineering (for-2020), 40 Engineering (for-2020), 7 Affordable and Clean Energy (sdg), Inverse design, Photonic surfaces, Surrogate-based optimization, Femtosecond laser processing, Machine learning, Random forests, 08 Information and Computing Sciences (for), 09 Engineering (for), Artificial Intelligence & Image Processing (science-metrix), 40 Engineering (for-2020), 46 Information and computing sciences (for-2020)
File Description: application/pdf
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18
Authors: et al.
Source: BMC Medical Research Methodology, Vol 25, Iss 1, Pp 1-18 (2025)
Subject Terms: Random forests, Recurrent events, Survival analyses, Terminal events, High-dimensional data, Medicine (General), R5-920
File Description: electronic resource
Relation: https://doaj.org/toc/1471-2288
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19
Authors:
Source: Electronic Journal of Biotechnology, Vol 78, Iss , Pp 1-13 (2025)
Subject Terms: Biliary colic, Biomarkers, Exosomal miRNA, Logistic Regression (LR), Predictive models, Random Forests (RF), Biotechnology, TP248.13-248.65, Biology (General), QH301-705.5
File Description: electronic resource
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20
Authors:
Source: Journal of Applied Science and Engineering, Vol 29, Iss 3, Pp 693-706 (2025)
Subject Terms: concrete, ground granulated blast furnace slag, simulation, random forests, honey badger optimization, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Physics, QC1-999
File Description: electronic resource
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