5 citations · 6 across the 4 of their papers we have counts for
4 papers
Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index
Yiming Ren, Teo Susnjak
In this work, a machine learning approach is developed for predicting the outcomes of football matches. The novelty of this research lies in the utilisation of the Kelly Index to f…
Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning
Teo Susnjak, Paula Maddigan
Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly est…
Forecasting Patient Demand at Urgent Care Clinics using Machine Learning
Paula Maddigan, Teo Susnjak
Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These del…
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters
Andre Barczak, Napoleon Reyes, Teo Susnjak
In this paper we use machine learning to study the application of Local Tchebichef Moments (LTM) to the problem of texture classification. The original LTM method was proposed by M…