Machine Learning Applications (Phase 3)

This multidisciplinary project focuses on applying Artificial Intelligence (AI) to address challenges spanning from computer vision to cybersecurity and healthcare. Specific problems tackled include robust automatic vehicle license plate recognition under variable lighting and angle conditions, and automatic vehicle counting using the Dew Computing paradigm, which combines cloud computing and end devices for operation independent of continuous internet connection. In the medical and social domains, the project proposes ethnic identification via artificial vision for genetic studies in Ecuador, and the development of a digital interpreter for Ecuadorian Sign Language (LSEC) to support the hearing-impaired population. Furthermore, research is conducted on network intrusion detection systems (NIDS) using Machine Learning, predictive modeling for the quality of thermosensitive oncological drugs during cold chain transport, and cognitive cybersecurity focused on enhancing human skills against cyberattacks. Lastly, the use of IoT for environmental assisted living solutions for patients with chronic diseases like Alzheimer's is explored. The methodology employed across all developments follows the scientific method, utilizing quantitative experimental evaluations.<br/><br/><b>Goal</b>: <br/>The main project objective is to enrich the state-of-the-art in various areas of interest through the application of advanced Artificial Intelligence techniques for solving complex, multidisciplinary problems.<br/><br/><b>Research lines</b>: <br/>Artificial intelligence and data mining