All City of Asheville managed data assets

The Generic DataBase Modeling Tool (GenDBM) uses the MDA approach to provide a database modeling environment for both relational and geographical models. It also uses the Generic Database Metamodel (GEDBM) which gathers concepts of the most used modeling languages and notations (ER, Crow's Foot, Min-Max, UML, OMT-G, etc.).

Markov chaIn Monte Carlo Multipole Decomposition Analysis -- a system for giant resonance MDA using the emcee package and an .so file written in C

Debian packaging information for fdm (https://github.com/nicm/fdm)

Managed data assets for the City of Asheville

fork of minuteProject (mavenization) : From Database generator to Web starter projects

Docker Dovecot IMAP server based on Alpine w/LDAP Support

Validates JSON instances using Object Constraint Language

Machine learning project conducted together with Volvo Cars

The Easiest Free Model Driven Software Factory engine for Visual Studio

Schematron rule generator for MagicDraw MDA

Metodologías de Desarrollo Ágil - UGR 2019/20

Pico TTL (MDA/CGA/EGA) to VGA converter

Scripts & queries to prepare data for integration with reporting repository

Tagged Message Delivery Agent

Face detection (class and pose) using various Classifiers - Own Implementation

Performing dimensionality reduction with various ML algorithms

Label classification for three datasets: Face, Pose and Illumination. Bayes Classifier, KNN Classier, Kerner SVM and Boosted SVM algorithms are written from scratch in Python. The results were evaluated and compared to understand the effectr of dimentionality reduction techniques including PCA, LDA and MDA validation using K-fold cross validation.

This repository contains the code to classify handwritten digits in the MNIST dataset using logistic regression.

MDA巨量資料分析