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Showing 4 open source projects for "forest"
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apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to... -
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ApkFor 1.0
ApkFor helps to transfer forest growth and yield models using Android
ApkFor is a generic Open-Source Code for compiling an application for Android (2.2) to help to transfer growth and yield models of different forest species easily changing the code. ApkFor was developed by Fernando PÉREZ-RODRÍGUEZ (fernando.perez@fora.es) and Maria MENÉNDEZ-MIGUÉLEZ. The project and Code are Beta and could be improved and re-used by anyone. In this case cite font as follows: "PÉREZ-RODRÍGUEZ F., MENÉNDEZ-MIGUÉLEZ M. 2017. ApkFor: Project and Code. https://sourceforge.net/projects/apkfor/" This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 613762 -
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mesoFON
mesoFON is an individual-based mangrove forest model, a KiWi extension
mesoFON is an individual-based mangrove forest model. It is a Java implementation of the KiWi model done with RePast Simphony. It contains several new features: (1) You can add up to 10 species via the GUI. However, in principle, the number of species to be included programmatically is unlimited. (2) You can use a variety of growth functions via typing them in as a string on the GUI. (3) The model is transparent, sinc it comes with the Eclipse workbench of RePast. -
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...New Features Include: -All the Features of the 3.7.3 Weka Package -Multi-Threaded ensemble learning -An enhancement on the popular RandomForest Learner based on "Dynamic Integration with Random Forests" by Tsymbal et al. 2006 and "Improving Random Forests" by Robnik-Sikonja 2004. -More enhancements to the voting mechanisms in Random Forest -Possibility to output Feature Weights according to the original Breiman Paper 2001
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