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Name: Dario Della Mura
Type: User
Bio: Master's Degree in Data Science. Bachelor's Degree in Economics. Interests and Knowledge: Computer Vision, Natural Language Generation, Machine Learning.
Name: Dario Della Mura
Type: User
Bio: Master's Degree in Data Science. Bachelor's Degree in Economics. Interests and Knowledge: Computer Vision, Natural Language Generation, Machine Learning.
Object Classification is one of the most significant tasks whose development is constantly growing in the field of deep learning research. The objective of this study is the development of neural architectures for the classification of images (of fruits and vegetables) contained within the Fruits-360 dataset. The methodological approach adopted to face the task in question has been the development of three models of convolutional neural networks obtained by applying three different techniques typical of deep learning: development of a convolutional neural network ex novo, Use of a pre-computed convolutional neural network using the transfer learning technique and use of a pre-defined convolutional neural network as a feature extraction tool.
During the project for the DIGITAL SIGNAL IMAGE MANAGEMENT course I learned how to manage and process audio and image files. The aim of the project was the classification, through machine learning and deep learning models, of musical genres by extracting specific audio features from the "gtzan dataset" dataset files with which to train the models (SVM, Linear Regression, Decision tree , Random Forest, Neural Network). Mel spectograms were also extracted to train convolutional neural network models. In addition, the extracted audio features have been used to develop a model of music retrieval which given an audio track in input produces as output 5 audio tracks recommended meiante the use of cousine similarity.
Covid Analysis in Italy: During the Data mangagement and visualization project, I learned to: collect data autonomously from multiple heterogeneous sources and store them (often in streaming) on appropriate databases (such as MongoDB and SQL); scrape online iterative graphs; perform statistical and graphic analysis of the collected data Link table with visualizations: https://public.tableau.com/views/ProgettoDMV_CovidHistory/Storia1?:language=it-IT&publish=yes&:display_count=n&:origin=viz_share_link
Thanks to this university project I acquired the skills in python and SQL, specifically a deep knowledge: in the use of numpy libraries, pandas for the correct management, cleaning and analysis of datasets consisting of some interconnected tables, such as those usually available on Kaggle.com (CSV, TSV, JSON formats); in the design of a relational database according to the principles of standard normalization and deduction of the implicit structure of a database from its tables.
revision in progress
The work presented was developed during the internship, as researchers in the field of Natural Language Generation, at the Insid&s Lab laboratory in Milan-Bicocca. The work carried out deals with the creation of a framework for the correct assessment of the impact of the quality of the input datasets on the quality of the text generated by the NLG models, specifically: Creation of the "Concept-Based" and "Entity-Based" versions of the WebNLG dataset; Evaluation of the quality of the datasets created; Training of LSTM and Transformer models using the OpenNMT tool; Natural language text generation by LSTM and Transformer models; Evaluation of the quality of the text generated by the NLG models; Final analysis.
Predicting the likely future healthcare costs of individuals across different social groups is an important and stimulating computing challenge. To address the problem of controlling healthcare costs, we retrieved data from the MEPS dataset, which contains general and medical information about 2000 patients collected during 2003 in the United States. The proposed work was carried out by using the ML Knime tool.
For the text Mining course I carried out a project related to the analysis and classification of the reviews of the "UCI ML Drug Review" dataset (link: https://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+%28Drugs.com%29). I learned to apply techniques such as bag of words, TF-IDF and build sentiment analysis models through the Bert and Vader model.
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