Machine Learning Applications (Phase 1)

This project focuses on applying Machine Learning algorithms and strategies to automate complex tasks, addressing significant social and technological challenges. The goal is to improve the quality of life for visually impaired individuals by tackling the lack of accessible technology for social activities like card games. In mental health, Machine Learning implementation is proposed to automate diagnostics, overcoming difficulties related to symptom ambiguity and scarce appropriate datasets, necessitating the development of information acquisition tools. Furthermore, the challenge of ensuring high service availability in Cloud Computing environments is addressed, utilizing Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic to solve complex problems like fault tolerance. The adopted methodology is holistic, covering both Machine Learning development and applied computing aspects, following a systematic KDD-inspired process that includes feature selection, algorithm selection, result validation, and interpretation of outcomes into applicable knowledge.<br/><br/><b>Goal</b>: <br/>Develop Machine Learning strategies for task automation across various domains, including assistance for visually impaired individuals, mental health diagnostics, and ensuring high availability management in Cloud Computing environments.<br/><br/><b>Research lines</b>: <br/>Data science and simulation