AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research.

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework.

Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System.

There are many methods of collecting traffic flow data, especially using smart phone apps. However, few current solutions balance the need for collecting full route data whilst respecting privacy and remaining low-cost. This project looks into the creation of a wireless sensor network (WSN) that can balance these requirements in an attempt to negate some of the concerns that come with this type of technology.

Digital patient: Personalized and translational data management through the MyHealthAvatar EU project.

The advancements in healthcare practice have brought to the fore the need for flexible access to health-related information and created an ever-growing demand for the design and the development of data management infrastructures for translational and personalized medicine. In this paper, we present the data management solution implemented for the MyHealthAvatar EU research project, a project that attempts to create a digital representation of a patient's health status. The platform is capable of aggregating several knowledge sources relevant for the provision of individualized personal services.