marcjerome Goto Github PK

baptist-notes

Website for compilation of baptist sermons and others

bookreviewsite

A book review website made with flask and flask-sqlalchemy

cpmsci117-web-development

django

The Web framework for perfectionists with deadlines.

django-basic-announcement

A reusable django app to have an announcement feature in a django web app.

employeetrainingmanagementsystem

Final Project in CMPSC 116

flask-socketio-chatrooms

Flask SocketIO Chatrooms is a simple chat app that allows users to make chatrooms for them to join and send messages

getmal

Returns the data of a particular anime list and status in JSON

google-i18n-address

Google's i18n address data packaged for Python.

javawordanalyzer

marcjerome

multimodal-flood-tweet-classification

While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not based on its text remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. In this study, we designed a multilingual multimodal neural network that can effectively use both textual and hydrological information. The classification data was obtained from the Twitter-streaming API using flood-related keywords in English, French, Spanish and Indonesian. Subsequently, hydrological information was extracted from a global precipitation dataset based on the tweet’s timestamp and locations mentioned in its text. We performed three experiments analyzing precision, recall and F1-scores while comparing a network that uses hydrological information against a network that does not. Results showed that F1-scores improved significantly across all experiments. Most notably, when optimizing for precision the network with hydrological information could achieve a precision of 0.91 while the network without hydrological information failed to effectively optimize. Moreover, this study shows that including hydrological information can assist in the translation of the classification algorithm to unseen languages. Tweets filtered using this network can be used to more effectively organize disaster response, validate and calibrate flood risk models, and task satellites among other applications.

one-click-hugo-cms

polkadot-dev-bootcamp-reference

prereq-wba-solana

prereq submission to wba solana - @Japarjam

sbc-workspace

school-project-recruitment-website

A django website that allows university students to properly recruit their members in a school project through online proposal

scikit-learn

scikit-learn: machine learning in Python

solana-key-decoder-cli

solana-key-decoder-cli is a simple command-line tool for decoding Base58 encoded keys and saving them as JSON files. It takes a Base58 encoded key as input, decodes it, and saves the decoded key as a JSON array in a file named key.json.

solana_sbc02_theblokc_proj

sqlalchemy-demo

Demo of SQLAlchemy through flask. A school requirement report.

testdrivenlist

A simple list project that was coded through Test-driven development.

weucf