Oladapo Kayode Abiodun
Kayode Abiodun Oladapo is a seasoned data scientist and a computer science lecturer with over a decade of experience in teaching and research. He currently lectures at McPherson University, where he specialises in information systems and data science. He is presently on a research fellowship with the Connect Institute in the United Kingdom and supports staff with Achieve Together.
He does research in information systems, data science, machine learning, learning analytics, and education management. He has several publications and a computer textbook, "Insight into Computer Studies," for JSS one to three. A member of NCS, CPN, and ACM and an associate member of the Society for Forensic Accounting and Fraud Prevention. He has served as the Acting Director, ICT-RMU and the acting Head of Department, Computer Science, College of Computing, McPherson University.
He is actively involved in mentoring students in Python for data science and community services. He is the campus adopter for Data Science Nigeria, McPherson University, as well as the Campus Guidance for PyClub McPherson University, and a member of the EuroPython Society. He oversees the affairs of Python Starter Hub for beginners in Python programming.
As an advocate for innovative learning and emerging technologies, Oladapo has led various national and international workshops on data science and artificial intelligence. He has mentored dozens of undergraduate and postgraduate students and has published research papers in peer-reviewed journals.
Visit here for more details: https://sites.google.com/view/kayodeabiodunoladapo.
Session
IIoT Networks' performance is significantly impacted by packet losses in the network, which is the failure of data packets in reaching their intended destination within the network. Most of the transmission control protocol versions reduce the rate of transmission during the detection of packet losses, assuming network congestion and interference, thus resulting in operational disruption, reduced efficiency, data integrity failure and economic impact.
However, not all packet losses are due to congestions and interference, some happen based on link issues from wireless which are seen as non-congestive packet losses as most transmission control protocol (TCP) modifications reduce the rate of transmission when these losses are detected while assuming network congestion, so TCP could not at present distinguish among these types of packet losses and reduces the rate of transmission irrespective of the types thus resulting in lower throughput for IIoT networks clients.
In addressing this issue, a heuristic-rule-based machine learning model was used for packet loss identification, classification, and prediction to differentiate between the types of packet losses at the IIoT network hosts’ end. The result shows that Random Forest performs better based on the rule, giving a hopeful resolution to an enhanced IIoT network's performance.