Evaluación del Daño y Restauración de los Árboles Después de un Huracán by Edward F. Gilman, Mary L. Duryea, Eliana Kampf, Traci Jo Partin, Astrid Delgado, Carol J. Lehtola

“…Duryea, Eliana Kampf, Traci Jo Partin, Astrid Delgado, and Carol Lehtola is the Spanish language version of ENH-1036, "Assessing Damage and Restoring Trees after a Hurricane", funded by the Florida Division of Forestry and the USDA Forest Service, Southern Region as part of the Urban Forest Hurricane Recovery Program. …”
Get full text

Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning by Ferdian Maulana Akbar, Robby Hermansyah, Sofian Lusa, Dana Indra Sensuse, Nadya Safitri, Damayanti Elisabeth

“…Results showed that the Random Forest model, after hyperparameter tuning, had the best performance with an F1 score of 0.765. …”
Get full text

Socio Economic Assessment of Urban Forestry Respondents’ income in Okiti Pupa, Ondo State, Nigeria by OI Faleyimu, M Akinyemi

“…©JASEM Keywords: Income, urban forest, education, age …”
Get full text

Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa by Tirualem Zeleke Yehuala, Sara Beyene Mengesha, Nebebe Demis Baykemagn

“…Python software was used to process the data, and machine learning techniques such as Random Forest, Decision Tree, Logistic Regression, Extreme Gradient Boosting, and Gaussian were applied. …”
Get full text

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

“…When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). …”
Get full text

Native versus non-native Prosopis woody species: Which fertilize the soil better? by Moslehi Maryam, Ahmadi Farzad, Matinizadeh Mohammad, Sadeghi Seyed Mousa, Izadi Masoumeh, Faunae Nafiseh, Alizadeh Tahereh, Shackleton Ross T.

Get full text

Guide to Fertilization for Pine Straw Production on Coastal Plain Sites by Anna Osiecka, Patrick J. Minogue, E. David Dickens

“…David Dickens, and published by the School of Forest Resources and Conservation, December 2015. …”
Get full text

Macromycete fungal diversity in the Andean Amazonian rainforest of the Sacta Valley, Cochabamba, Bolivia by Mario Coca Morante, Alejandro Coca-Salazar, Olga Herrera Fernández, Casimiro Mendoza Bautista

“…The inventory was taken in the permanently monitored plots established within the primary forest preserved by the San Simón University at the Valley of Sacta Fund. …”
Get full text

Diversity and Conservation Status of Large Mammals in Ghamot National Park, Azad Jammu and Kashmir, Pakistan by Muhammad Jahangeer, Siddique Muhammad, Muhammad Shakeel, Mir Muhammad Saleem, Ali Usman, Hussain Abid

“… We assessed the richness, diversity, composition of the large mammal community in Ghamot National Park (GNP), Neelum valley Azad Jammu and Kashmir, Pakistan, and how these characteristics differed between four habitat types: forest, riparian zone, scrubland, and wetland, as well as between seasons. …”
Get full text

The Spatio-temporal Evolution of NDVI in the Three Gorges Reservoir Area Driven by the Synergistic Effect of two Factors over the Past 21 Years by QI Haimei, ZHENG Peilong, WANG Yunqi, WANG Jiani, LI Cheng, ZHANG Xiaoming

Get full text

Mechanical behavior of additive manufactured wood-based composites for construction by Milinda Yapa Hamillage, Robert Carne, Armando G McDonald, Michael Maughan, Ahmed A Ibrahim, Daniel J Robertson

Medicinal Potential of Pine Trees: A Brief Review Focusing on Three Species by Qinsong Li, Qiyu Li, Anping Wang, Wenxuan Quan

Get full text

A New Twist in Managing Cogongrass by Rick Williams

“…Published by the UF School of Forest Resources and Conservation, May 2008. …”
Get full text

Enhancing Natural Resource Programs with Field Trips by Julie Athman, Martha C. Monroe

“… This document is FOR 105, one of a series of the School of Forest Resources and Conservation, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. …”
Get full text

Managing Cattle on Timberlands: Forage Management by Chris Demers, Rob Clausen

“… This document is SS-FOR-20, one of a series of the School of Forest Resources and Conservation, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. …”
Get full text

Wildland-Urban Interface Case Study: Mediating for Change in Martin County, Florida by Lauren McDonell, Martha C. Monroe

“…Published by the UF School of Forest Resources and Conservation, October 2009.   …”
Get full text

Social Marketing in the Wildland-Urban Interface by Martha C. Monroe

“…Published by the UF School of Forest Resources and Conservation, July 2008. FOR193/FR254: Social Marketing in the Wildland-Urban Interface (ufl.edu) …”
Get full text

Controlling Hardwoods in Longleaf Pine Restoration by Patrick J. Minogue, Kimberly Bohn, Rick Williams

“…Published by the UF School of Forest Resources and Conservation, August 2007. …”
Get full text

Enhancing Natural Resource Programs: Designing Effective Brochures by Martha C. Monroe, Ludovica Weaver

“…Published by the UF School of Forest Resources and Conservation, April 2007.   …”
Get full text

An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Twum Frimpong, Abdul-Salaam Gaddafi

“…Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. …”
Get full text