Mr. Adedeji FABIYI
Brunel University London – United Kingdom
I’m a Cisco Certified Network Associate with skills and experience in telecommunication infrastructure and support. A highly self motivated researcher with a PhD in Computer Science in view. My interests and expertise include Data Science, Big Data Analytics and Business Intelligence. Also experienced in Web design, Software design and Implementation and programming in Java, Java script, HTML5 and Python.
My research interests include: Agent-Based Simulation, Infection Model, Science Gateways, and Distributed Computing Infrastructures. Other interests Include teaching as a Graduate Assistant in various modules at the undergraduate level.
Integration of a Repast-based Infection Model in a Science Gateway
The Repast-based Infection Model is an example of an Agent-Based Simulation Infection Model implemented in the well-known Repast Simphony agent-based simulation toolkit. There are three (3) types of agent to represent, namely: the infected, susceptible and the recovered population. All agents are randomly located in a Grid environment. Susceptible agents try to avoid contact with infected agents. When an infected agent approaches a cell with susceptible agent, it infects one, randomly, selected agent. This susceptible agent then becomes infected and can infect other susceptible agents in turn. The infected, susceptible and recovered agents are the input parameters that the user can modify in order to experiment with different initial conditions. The user can also specify the time (in years) that the simulation will run.
For storing documents, datasets and other products relative to the Repast-based Infection Model, the Sci-GaIA Open Access Repository (OAR) has been adopted. DataCite-issued Digital Object Identifiers (DOIs) are automatically assigned by the OAR to each record to improve its findability, discoverability and reusability. See for example this record.