A multi-syntax functional programming language that compiles to WebAssembly.

This script performs a list of common health checks to a specific domain, or the entire forest. The results are then compiled into a colour coded HTML report.

The European Forest Information SCENario Model (EFISCEN) is a large-scale forest model that projects forest resource development on regional to European scale. This page intends to support users of the model and contains a repository for supporting documentation and the compiled versions of EFISCEN.

source code to compile the LArge VEgetation SImulator (LAVESI), a program to simulates the vegetation dynamics of boreal forest tree species driven by monthly weather data series in a spatial-explicit environment.

PyNR is a virtual screening program. PyNR includes a self implementation of X-Score, Vina, AutoDock and RF-Score scoring functions. It allows you to train the Linear Regression and Random Forest models associated with each scoring functions. It also can understand multiple formats: PDBQT, PDB, MOL2 and SDF. All of this while providing a beautiful and easy to use GUI. Currently the Molecular Viewer is still under development, any contribution to the project is highly appreciated. MinGW C++ Compiler and the QT Framework are required to build the project.

A compiled version for The Alwyn H. Gentry Forest Transect Data Set

In this task, a data-set is used that comprise of different jobs posted on a job portal. The data-set was downloaded from Kaggle. It had the following basic properties: It was provided in .csv format. The data-set simulated the real life scenario of jobs posted on a job portal and comprised of Job's title, Job's description along with its category As the data was labeled so in the context of machine learning, it was a Supervised Machine learning problem i.e. I had access to the data that was already correctly labeled and I had to train a model using this historical data. The main goal was to build a model that could accurately classify new and unseen data when it was input to it i.e. to assign proper label to a job posting when its input to the model. As the nature of the data was "text" so this project also involved extensive usage of text mining techniques as well. Text in its basic form is unstructured and to develop predictive models, the data needs to be thoroughly pre-processed. So the pipeline of developing models that I followed was: Data Profiling Data Cleansing Exploratory Analysis Data Preprocessing Feature Extraction and Selection Model Development Model Evaluation When text data is pre-processed, the issue of curse of dimensionality usually appears i.e. data becomes highly multi-dimensional with lots of features ranging in thousands. Not all of those features are helpful and also it adversely affects the peformance of classifiers as well so following the best practices, I opted for best-in-class feature extraction methods and also applied feature selection techniques so as to compile only those features that will contribute in this prediction problem. For model development, I used and compared the following set of machine learning algorithms: Bernoulli Naive Bayes Multinomial Naive Bayes Random Forests Linear SVM and compared these algorithms on different metrics like accuracy, training and testing time. As per my analysis, SVM outshines all of the other models when it comes to accuracy. Random Forests accuracy score was also quite good but took considerable time during training phase. For implementation, I used Python. Specifically, I used the following libraries/modules of Python for different set of tasks: pandas, numpy sklearn nltk matplotlib To run the code, please make sure that the latest version of Python, Jupyter and aforementioned libraries are installed in your system.

The Algeria Forest Fire dataset contains information related to forest fires in Algeria. This dataset compiles data on forest fire occurrences, including their geographical locations, dates, and environmental conditions.

Compiled and cleaned datasets for forest disturbance analysis: aerial detection surveys, climate data (TerraClimate, PRISM, WorldClim), and related environmental variables.

A virtual reality (VR) experience where a user can explore a mystical forest was created. The objective was to create a relaxing experience that engages users and draws them to nature. Low poly assets were created in Google Block, a VR based creative software, and Maya. Using Unity, a software used to create video games, the assets were compiled and the environment built. This report aims to detail the process undertaken to build the experience. It details the initial concepts, the process of designing the map, building the terrain and designing and developing the creatures that reside in the forest. In the report, we elaborate on the development processes in Unity and how the project progressed from individual assets into an entire forest.

- Studied the connection between the use of moral words in political tweets and the number of retweets - Used Twitter’s API and python library Tweepy to scrape and feature tag roughly 50k tweets from politicians - Identified Moral Words using Jesse Graham and Jonathan Haidt’s Moral Foundations Dictionary - Compiled, cleaned, and vectorized data into a Pandas data frame using Genism’s Doc2Vec API - Created a predictive model for retweet class with 68% accuracy using Sklearn’s Random Forest Classifier API

A multi-syntax functional programming language that compiles to WebAssembly.

forest craft website compiled for deployment

WIP repo to compile NPS forest data for carbon storage and sequestration calculations.

updated version of python2_tools. This package includes tools such as submission compiler, SPCOMP parser, Populate Forest Unit and etc.

Template for Sons Of The Forest mods that can compile to BepInEx and MelonLoader

The plug-in is used by users in Vietnam to compile forest inventory tables.

Repository contains compiled list of relevant papers and articles related to Random Forest Hyperparameters tuning

The Forest ESV Database, supported by USAID and the US Forest Service's Compass Program, aids sustainable forest governance decisions in Bangladesh. It compiles data on ecosystem services from various sources, assisting informed decision-making by the Bangladesh Forest Department.

Web scraper built in Javascript for compiling trail names, distance, and elevation from the Forest Service website.

Deep Learning: Create neural networks to classify the type of forest cover based off cartographic variables compiled from the USFS

The Himalayan Bird Abundances and the Himalayan Forest Metadata tables collected and compiled by Dr. Alex White as part of his dissertation.

Compiling sediment core data from eight lakes in the Superior National Forest, MN to characterize concomitant changes in diatoms and sediment phosphorus

Compared the performance of KNN, SVM, and Random Forest regression on predicting the rise in sea level based on compiled time-series climatology data.

Camping Bee has compiled the most rammed mountain forest scenery for camper in 2020. You can search for your favorite campsite according to your needs.

The goal of this project is to compile in R a marker gene based (e.g. bacterial 16S rRNA gene) taxonomic classifier using random forest.

Trained Random Forest model using R programming language for two problems; regression and classification based on the solar power generation that was compiled by Ph.D. candidate Alexandra Constantin.

Predicting Credit Risk : Created a machine-learning model that attempts to predict where a loan becomes high risk or not. Both a logistic-regression model & random forest classifier were compiled and evaluated.

Raw data taken from published papers on primate traits present in the Atlantic Forest of the state of São Paulo, Brazil. The data compiled here were used to calculate functional diversity indices.

R project analyzing weight lifting exercise data using Random Forest to classify lifting technique. Includes data cleaning, model training with cross-validation, validation accuracy, and final predictions. Contains R Markdown source and compiled HTML for reproducibility.