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b-ghimire's Projects

fcn.tensorflow icon fcn.tensorflow

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

i.jmdist icon i.jmdist

GRASS GIS script to calculate Jeffries-Matusita distance

location-finder icon location-finder

Python flask web application for selecting different locations (i.e., counties) in United States based on multiple factors related to population, earnings, age, housing, education, rent, employment, health and physical activity. The different factors are standardized using linear transformation and then aggregated into a suitability index by weighting each standardized factor with user-specified importance weights. The suitability indices for each county are then ranked from most suitable to least suitable and displayed on a map. Web Application Framework: Python Flask <br> Backend: Python (NumPy, Pandas), SQLite <br> Frontend: HTML, CSS, JavaScript, jQuery, Ajax, Bootstrap, D3

machine-learning-r icon machine-learning-r

A collection of R code for running machine learning algorithms including random forest, bagging, boosting and classification trees. The R scripts were originally developed for land-cover classification but can easily be extended and applied to other supervised classification problems.

python-pelican-blog icon python-pelican-blog

Bash shell scripts to automatically set-up, create, add content and host a Python Pelican blog.

tensorflow icon tensorflow

Computation using data flow graphs for scalable machine learning

twitter-sentiment-analysis icon twitter-sentiment-analysis

R shiny web application to scrape tweets based on user-defined search keyword and perform sentiment analysis of the tweets. Sentiment analysis of tweets consists of classifying tweets into emotion classes (i.e., anger, disgust, fear, joy, sadness and surprise) and also polarity classes (i.e., negative, neutral and positive) using naïve Bayes classifier. The tweets are scraped, classified into sentiment classes and visualized in R using twitteR, sentiment and ggplot2 packages, respectively.

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