Reza Arabpour's repositories
Graph_Algorithms
C++ implementation of some of the most well-known graph algorithms in the simplest way. Mostly in non-OOP style because the algorithms itself and its performance were the main points.
RNN_GameTheoretic_DecisionMaking
In this project, we first review the paper ”A Recurrent Neural Network for Game Theoretic Decision Making” by Sudeep Bhatia and Russell Golman, 2014, introducing a computational model to solve the outcome of strategic games. Then we go through the codes which have been implemented in Matlab.
arabporr
Config files for my GitHub profile.
arabporr.github.io
Here I will create a website which is will be another way for us know each other better :)))
B-Tree_Database_Project
Used pure C++, OOP, and no pre-defined data structures, created a database capable of storing bank account data and handling different types of queries at O(logN) and creating log files online.
Clustering_Algorithms
Used clustering algorithms such as K-Means, Fuzzy C-Means, and Density-Based Algorithms like DBScan to cluster three datasets and reported result of the best algorithm after 200 random starting points.[part of my data mining course]
Data-Structures
C++ implementation of some of the most well-known data structures in the simplest way. *** Note : This repo will update as the course goes on ! [I am currently TA for Data Structures course] ***
Dog_Breed_Classification
Used different pre-trained models -such as ResNet, VGG, Alexnet- to check if an image is a picture of a dog and then recognize its breed. [Note: This project is part of Udacity's AI programming nano degree.]
Grapevine_Leaves_Classification_CNN
Used different pre-trained models like ResNet, VGG, Alexnet, and own designed architecture, to recognize grape breeds based on their leaves. Ended up in about 95% on validation data and 90% on out-of-sample test data. Besides, we tried to improve the accuracy by using image denoising and dimension reduction autoencoder networks.
SR-MCL_Graph_Clustering
Used Python to implement the Soft Regularized Markov Clustering (SR-MCL) algorithm and tested it on an extensive real-world dataset, weighted yeast proteins interactome, for time complexity and performance check beside a small random graph for showing the nodes' predicted clusters.
Signature_To_Graph
Getting an image of a signature and converting it into the underlying graph structure the signature which can be used for late classification tasks.