2

android

1

biopython

3

bootstrap

1

c-plus-plus

2

c-sharp

6

css

2

cv2

2

firebase

1

flex-lexical-analysis

6

fontawesome

2

game

2

google-maps

3

hobby

6

html

1

jasypt

8

java

1

java-spark

6

javascript

3

jquery

1

jstl

1

laravel

3

matplotlib

1

mockaroo

2

multithreading

3

mysql

1

neat-algorithm

3

netlify

1

networkx

1

neural-networks

6

numpy

2

pandas

1

php

2

pillow

2

postgresql

9

python

3

react

1

reinforcement-learning

1

scikit-image

2

scikit-learn

1

scipy

2

servlets

7

sql

2

sqlite

3

tailwindcss

2

tomcat

19

university

3

visual-studio

6

web-app

2

website

## Image Processing

Involved in this project: Panos Ioannidis, Dionisis Nikas

A Python project that was developed as a university assignment for the subject of Image Processing. The program takes an input image and a reference dataset of photos. The goal is to colorize the greyscale image using a trained support vector machine. To achieve that, we have implemented a variety of image processing techniques. First, we change color spaces from RGB to LAB. Then, we apply the SLIC algorithm to find the group of superpixels for each image. These segments along with SURF and GABOR features are given as input for the SVM. Using scikit-learn, we use machine learning techniques to predict the color of a superpixel using the dataset superpixels as reference. The output of the program returns the colorized version of the input image.

## Pattern Recognition

Involved in this project: Panos Ioannidis

A Python project that was developed as a university assignment for the subject of Pattern Recognition. The program uses the "MovieLens 100K" dataset that includes movie ratings of random users. The data included is processed using the Pandas library. We implement three algorithms: Basic Sequential Algorithmic Scheme (BSAS), K-means and hierarchical clustering. Finally, we use a neural network with multilayer perceptron and least squares linear regression to make predictions on movie ratings.

## Thesis

Involved in this project: Dionysios Sotiropoulos

On this project, we study applications of the NEAT algorithm in deterministic and non-deterministic game environments. First, we look at an overview of the NEAT algorithm, how it works, design principles and the challenges that come with implementation. Next, we introduce a custom two-dimensional game in Python for two players: blue and red. We lay down the basic rules and structure, in order to create an environment suitable for neuroevolution. Finally, we study five training cases, where the blue and red player are given several tasks that must be achieved through the evolution of neural networks.