Ylenia Galluzzo's repositories
ProgettoMPA2021
Progetto MPA 2020/2021
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
ProjectUni-CNN
Replication of research article results: Rizzo, Riccardo, et al. "A deep learning approach to DNA sequence classification." International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer, Cham, 2015.". ML models were used: Naive Bayes, SVM, Random Forest; The CNN neural network was also implemented, respecting the modifications made to the LeNet-5 network in the research article.
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
awesome-graph
A curated list of resources for graph databases and graph computing tools
awesome-italian-public-datasets
A selection of interesting Open dataset from the Italian Public Administration and Civic Data use cases
awesome-public-datasets
A topic-centric list of HQ open datasets.
cheshire-cat
Open source and customizable AI architecture
Github-readme.md-Syntax-highlighting-syntax-code-block-
Github readme.md Syntax highlighting syntax code block
grafana-mongodb-docker
Docker container for Grafana with MongoDB datasource plugin
MEDIALpy
A small python package that allows the user to look up common medical abbreviations.
medical_abbreviations
List of medical abbreviations and their meanings
project-aquarium
project for devOps 2020
Project_RadiomicClassification
Study on the classification of radiomic data radiomic data on tumour status in patients in patients with prostate carcinoma using variational autoencoder (VAE). In this project, the VAE model is used as an alternative to PCA for the selection of dataset features. Tumour classification results obtained using PCA in combination with ML models such as: KNN, SVM, Random Forest, Logit. Tumour status classification results using VAE+MPL are also processed. In addition, oversampling of the samples was carried out using the SMOTE technique (for creating new synthetic data based on the actuals), using a non-trivial target reconstruction strategy.