I became a computer engineer and a data scientist in order
to follow my passion, learning during my
academic and working years that it has to be supported by my own effort and my
own dedication. Through the analysis of a problem, mixing the logic and a bit
of creativity, it's possible to achieve any goal. The more interesting and
intriguing is the challenge, the greater the reward in knowledge and
satisfaction
will be. The help of other people in the team is crucial, and
creating good
relationships with them allows to achieve better results.
I am keen on sports, regularly playing football and snowboarding, and
of video
games, when I have enough free time.
I also like to participate in events where
my professionality can be improved and tested, such as hackathons and code
challenges.
Research work for the automatic identification of problematic plants in orchards, through a predictive model capable of analysing images taken by drones. The analysed models are different neural network architectures: AlexNet, GoogLeNet and VGG-11.
Research project for the definition of an artificial intelligence algorithm, based on neural networks, able to understand the emotions expressed in audio files, using their spectrogram. Different datasets are used for training and testing parts. The analysed architectures are: GoogLeNet, VGG and ResNet.
Analysis of dataset Ionosphere for identifying an artificial intelligence algorithm capable of executing a classification task. The algorithms used are: logistic regression, decision tree, random forest and SVM with different kernels. The software Orange is used for the analysis.