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Lior Pizman's Projects

anomalydetection icon anomalydetection

A system which is able to create machine learning models for anomaly detection on UAVs. The system allows creation and loading of machine learning models by using dynamic inputs. Moreover, the system displays different output plots and evaluation metrics which compare between different models and the diagnosis of anomalies which were found.

berlinsites icon berlinsites

Berlin's Trip Advisor - using NodeJS for server-side and AngularJS for client-side

cybersquatting icon cybersquatting

The goal of the project is to take over a network of infected computers (botnet) to destroy victims through DDoS (distributed denial of service) attack.

demo_app icon demo_app

Ruby on Rails Tutorial Demo Application

drawboard-react icon drawboard-react

Online Draw Board - using ASP.NET Core and C# for server-side and React for client-side

first_app icon first_app

The first app for the Ruby on Rails Tutorial

flower-classification icon flower-classification

Flower classification system based on deep neural network model to recognize and distinguish between 5 types of flowers (Daisy, Dandelion, Rose, Sunflower and Tulip). The system contains a model that we trained on over more than 4,00 images. (BGU project)

hackbgu19 icon hackbgu19

Platform to exchange notebook between students

maze icon maze

Solving the maze by different algorithms

moxios icon moxios

Mock axios requests for testing

pacman-js icon pacman-js

Project that simulates the famous Pacman game

search-engine icon search-engine

Document information retrieval. The primary goal is to retrieve documents that are relevant to a user query. The main corpus including about 1815 different documents.

social-network icon social-network

Social network application for developers (MongoDB, React, Redux and Node.js)

templates icon templates

Document templates for open-source projects (README, CONTRIBUTING, GitHub templates)

twitter-sentiment-classification icon twitter-sentiment-classification

Train a machine learning model to predict the sentiment of the tweet and use Twitter streaming API to collect 15,000 tweets from Twitter.

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