Search Results - machine learning algorithms in forestry

Assessing the color status and daylight chromaticity of road signs through machine learning approaches

Authors: Saleh, Roxan Fleyeh, Hasan, Associate professor Alam, Moudud 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

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:du-46627
https://doi.org/10.1016/j.iatssr.2023.06.003

Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

Authors: Pablo Antúnez Christian Wehenkel Erickson Basave-Villalobos et al.

Source: Forest Science and Technology, Vol 21, Iss 1, Pp 110-122 (2025)

Subject Terms: Forest management, machine learning algorithms in forestry, predicting forest biomass, allometric modeling, Random Forest algorithm, Forestry, SD1-669.5, quantile regression in forest management

Access URL: https://doaj.org/article/704db43af7644df49b6c8b5d76a3b779

基于机器学习算法的湖南沅陵森林火灾风险预测.

Alternate Title: Machine learning algorithm-based forest fire risk prediction in Yuanling, Hunan Province.

Authors: 楚春晖 朱柯颖 王光军 et al.

Source: Journal of Central South University of Forestry & Technology. Oct2025, Vol. 45 Issue 10, p59-68. 10p.

Subject Terms: *MACHINE learning, *RANDOM forest algorithms, *FOREST fire management, *RISK assessment, *FIRE prevention

Geographic Terms: HUNAN Sheng (China)

Application of machine learning algorithms for automation of content moderation in app group messenging chats

Source: Scientific Bulletin of UNFU; Vol 34 No 7 (2024): Scientific Bulletin of UNFU; 52-59; 2519-2477; 1994-7836; 10.36930/403407

Index Terms: Telegram bot, PSO, SSA, GWO, SVM, feature selection, classification, ensemble methods, info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: https://nv.nltu.edu.ua/index.php/journal/article/view/2669
https://nv.nltu.edu.ua/index.php/journal/article/view/2669/2675
https://nv.nltu.edu.ua/index.php/journal/article/view/2669/2675

Predicting Wood Color Origin Using Random Forest and Spectroscopy: A Machine Learning Approach for Industrial and Forestry Applications.

Authors: Quinteros-Navarro, Dino Quinteros, Alfredo Muñoz, Victor et al.

Source: Ingénierie des Systèmes d'Information. Sep2025, Vol. 30 Issue 9, p2269-2284. 16p.

Subject Terms: *INDUSTRIAL applications, RANDOM forest algorithms, COLOR, CHEMICAL properties, SPECTROMETRY, PREDICTION models, MACHINE learning, WEATHER & climate change

Understanding the contribution of structural diversity to stand biomass for carbon management of mixed forests using machine learning algorithms.

Authors: He, Xiao Lei, Xiangdong Liu, Di et al.

Source: European Journal of Forest Research. Feb2025, Vol. 144 Issue 1, p13-28. 16p.

Subject Terms: *ARTIFICIAL neural networks, *FOREST biomass, *FORESTS & forestry, *BIOMASS estimation, *FOREST management

Deep learning-based forest fire detection using an improved SSD algorithm with CBAM.

Authors: Zhang, Diansheng Zhang, Yueyuan Dong, Leilei et al.

Source: PLoS ONE. 11/18/2025, Vol. 20 Issue 11, p1-15. 15p.

Subject Terms: *DEEP learning, *DETECTION algorithms, *FEATURE extraction, *DATA augmentation, *FOREST fire management, *OBJECT recognition (Computer vision)

Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms.

Authors: Jiang, Yunyang Zhang, Zixuan He, Huaijiang et al.

Source: Remote Sensing. Oct2024, Vol. 16 Issue 19, p3627. 18p.

Subject Terms: *LEAF area index, *RANDOM forest algorithms, *REMOTE-sensing images, *HYDROLOGIC cycle, *REMOTE sensing

Tunning machine learning algorithms for forestry modeling: a case study in the height-diameter relationship

Authors: Costa Filho, Sérgio Vinícius Serejo da Arce, Julio Eduardo Montaño, Razer Anthom Nizer Rojas et al.

Source: Ciência Florestal; Vol. 29 No. 4 (2019); 1501-1515
Ciência Florestal; v. 29 n. 4 (2019); 1501-1515
Ciência Florestal
Universidade Federal de Santa Maria (UFSM)
instacron:UFSM

Subject Terms: Redes neurais artificiais, Artificial intelligence, Busca em grade, Artificial neural networks, Grid search, Validação cruzada, Cross validation, Inteligência artificial

File Description: application/pdf

Access URL: https://periodicos.ufsm.br/cienciaflorestal/article/view/28392

Enhancing procedural generation of virtual landscape using machine learning

Source: Scientific Bulletin of UNFU; Vol 35 No 2 (2025): Scientific Bulletin of UNFU; 116-123; 2519-2477; 1994-7836; 10.36930/403502

Index Terms: digital elevation model, variational autoencoder, noise algorithms, height gradient, realistic terrain, info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: https://nv.nltu.edu.ua/index.php/journal/article/view/2738
https://nv.nltu.edu.ua/index.php/journal/article/view/2738/2740
https://nv.nltu.edu.ua/index.php/journal/article/view/2738/2740

Estimation of Marketing Live Weight of Lambs by Different Machine Learning Algorithms.

Authors: Karakus, F.¹ fkarakus@yyu.edu.tr

Source: Indian Journal of Animal Research. Jan2025, Vol. 59 Issue 1, p156-162. 7p.

Subjects: Artificial neural networks, Machine learning, Standard deviations, Radial basis functions, Support vector machines

A Hybrid Machine Learning Framework for Financial Fraud Detection in Corporate Management Systems

Authors: Prem Kumar Sholapurapu

Subject Terms: Computer gaming and animation, Artificial intelligence not elsewhere classified, Computer vision, Computer vision and multimedia computation not elsewhere classified, Computer aided design, Computer graphics, Artificial Intelligence & Writing, Artificial intelligence -- Industrial applications, artificial intelligence (<, artificial intelligence adoption, Artificial Intelligence (AI) 5G Emerging technologies, Artificial intelligence (AI) model, Artificial intelligence -- Data processing, Machine learning in radiotherapy, machine learning algorithms in forestry, Machine Learning & False Correlations, machine learning analysis of MD trajectories

Relation: https://figshare.com/articles/journal_contribution/A_Hybrid_Machine_Learning_Framework_for_Financial_Fraud_Detection_in_Corporate_Management_Systems/29464652

Availability: https://doi.org/10.6084/m9.figshare.29464652.v1
https://figshare.com/articles/journal_contribution/A_Hybrid_Machine_Learning_Framework_for_Financial_Fraud_Detection_in_Corporate_Management_Systems/29464652

Mapping the distribution of pine wilt disease based on selected machine learning algorithms and high-resolution Gaofen-2/7 remote sensing.

Authors: Wang, Yifan Zhou, Xiaocheng Chen, Chongcheng et al.

Source: International Journal of Digital Earth. Dec2025, Vol. 18 Issue 1, p1-23. 23p.

Subject Terms: *MACHINE learning, *REMOTE sensing, *CONIFER wilt, *IMAGE segmentation, *FOREST management

Geographic Terms: CHINA, FUJIAN Sheng (China)

Cyclone prediction for satellite cloud image using deep learning algorithms.

Authors: Patil, Jayashree Suryakant Radhika, Chukkapalli Kousar, Syeda Tahmeena et al.

Source: AIP Conference Proceedings. 2025, Vol. 3361 Issue 1, p1-8. 8p.

Subject Terms: *CYCLONE forecasting, *DEEP learning, *WEATHER forecasting, *ENVIRONMENTAL sciences, *DATA augmentation, *REMOTE-sensing images, *ARTIFICIAL neural networks

Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms.

Authors: Dmitriev, Pavel A. Dmitrieva, Anastasiya A. Kozlovsky, Boris L.

Source: AgriEngineering. Mar2025, Vol. 7 Issue 3, p90. 13p.

Subject Terms: *DEEP learning, *SPECTRAL reflectance, *PLANT classification, *PRINCIPAL components analysis, *RANDOM forest algorithms

Company/Entity: ACER Inc.

Comparative analysis of deep learning approaches for forest stand type classification: insights from the new VHRTreeSpecies benchmark dataset.

Authors: Sertel, Elif Topgul, Sule Nur

Source: International Journal of Digital Earth. Dec2025, Vol. 18 Issue 1, p1-29. 29p.

Subject Terms: *DEEP learning, *TRANSFORMER models, *DETECTION algorithms, *REMOTE-sensing images, *VEGETATION classification, *ARTIFICIAL neural networks, *PLANT species

Geographic Terms: TURKEY

Integrating UAV, Sentinel-2, and ALOS PALSAR-2 data for improving above-ground biomass estimation in Miombo woodlands using machine learning algorithms.

Authors: Melitha, Goodluck S. Kashaigili, Japhet J. Mugasha, Wilson A.

Source: International Journal of Remote Sensing. Jul2025, Vol. 46 Issue 13, p4796-4831. 36p.

Subject Terms: *MACHINE learning, *CLIMATE change mitigation, *FOREST management, *BIODIVERSITY conservation, *MULTISENSOR data fusion, *FOREST degradation

Integrating isolation forest and deep learning for reliability check of land vector patch types.

Authors: Wang, Shengli Zheng, Nanshan Zhu, Yihu et al.

Source: International Journal of Remote Sensing. May2025, Vol. 46 Issue 10, p3770-3799. 30p.

Subject Terms: *DATABASES, *VECTOR data, *FORESTS & forestry, *CONSTRAINT algorithms, *DATA quality

Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.

Authors: Zhuang, Zhong-Han Tsai, Hui-Ping Chen, Chung-I

Source: Sensors (14248220). Apr2025, Vol. 25 Issue 7, p1966. 28p.

Subject Terms: *LEAF area index, *DRONE aircraft, *PHOTOSYSTEMS, *CARBON sequestration, *TEA plantations, *PLANTATIONS, LEAF growth

应用机器学习算法预测樟子松树皮厚度.

Alternate Title: Prediction of bark thickness of Pinus sylvestris var. mongolica by machine learning algorithm.

Authors: 谌俊燃 张兹鹏 姜立春

Source: Journal of Central South University of Forestry & Technology. Feb2025, Vol. 45 Issue 2, p82-90. 9p.