I have developed a predictive model to estimate the success of TV shows based on data extracted from Twitter using Machine Learning techniques and Sentiment Analysis. I used Twitter API to obtain data from Twitter about the different shows.
The purpose is to analyze adjacent bands correlation of a multispectral image and the same bands' correlation of a multispectral image over time. We amended the SLSQ algorithm in order to introduce temporal redundancy using Matlab.
Auction Mechanism implements an auction based on a P2P network. It's possible to create new auctions, verify an auction's state, and offer money for an auction. It is developed using Apache Maven as a software project management and TomP2P.
MeteoWhere is a hybrid application developed using Ionic 3. It allows users to easily find places to do various activities based on the forecast. The app placed second at the App Challenge 2018, a competition held in Salerno Municipality.
Brand Storming is a website developed with vanilla JavaScript and Node.js. The back end retrieves data using scraping techniques from various sources. It provides a global view of a fashion brand: videos of advertising campaigns, tweets, events and stores.
I graduated in Networks and Software Technologies at University of Salerno.
Degree Mark: 110/110 with honours
I graduated in Computer Science at University of Salerno.
I graduated at F. Severi High School.
Mark: 100/100