Application of systems science in business has lately emerged as an exciting area of applied research. It aims to study the behavior of complex business systems. The present study attempts to converge the two powerful approaches of system approximation in form of artificial neural network and...

Application of systems science in business has lately emerged as an exciting area of applied research. It aims to study the behavior of complex business systems. The present study attempts to converge the two powerful approaches of system approximation in form of artificial neural network and...

In this work, we apply cutting edge machine learning algorithms to one of the oldest challenges in finance: Predicting returns. For the sake of simplicity, we focus on predicting the direction (e.g. either up or down) of several liquid ETFs and do not attempt to predict the magnitude of price...

Machine Learning algorithms are becoming widely deployed in real world decision-making. Ensuring fairness in algorithmic decision-making is a crucial policy issue. Current legislation ensures fairness by barring algorithm designers from using demographic information in their decision-making. As...

Computerized algorithms for predicting the outcomes of legal problems can extract and present information from particular databases of cases to guide the legal analysis of new problems. They can have practical value despite the limitations that make reliance on predictions risky for other...

Neural networks design, control and robotics application -- Hybrid evolutionary systems for modelling, control and robotics applications -- Fuzzy logic and bayesian systems -- Machine learning, evolutionary optimisation and information retrieval -- About the authors -- Index