Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm...

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

Network-Centric Meaning-Driven Human-Centric AI-Cyber Computing Beyond Data-Driven to Event-Driven Architectures for Quantum Uncertainty, 1995-2023:Building upon the contextual focus of current global worldwide discussions on GPT, ChatGPT, GenAI, Generative AI, Large Language Model - LLMs, we...

Goal: ISO 31000 Risk Management (RM) recently re-defined risk as the effect of uncertainty on an organization's ability to meet the objectives. Earlier, it defined risk as a combination of the probability and scope of the (predicted) consequences. The revised ISO Risk advances beyond a static...

Robust optimization (RO) is a young and active research field that has been mainly developed in the last 15 years. RO techniques are very useful for practice and not difficult to understand for practitioners. It is therefore remarkable that real-life applications of RO are still lagging behind;...

Spanish Abstract: El proceso de toma de decisiones que implique optimizar recorridos, ya sea de reparto de mercancías, servicios de mensajerías, circuitos turísticos, entre otros, es por lo general una tarea difícil. Aparece aquí uno de los problemas que ha sido y continúa siendo, un reto...

This article presents a novel combination of robust optimization developed in mathematical programming, and robust parameter design developed in statistical quality control. Robust parameter design uses metamodels estimated from experiments with both controllable and environmental inputs...

Adjustable robust optimization (ARO) is a technique to solve dynamic (multistage) optimization problems. In ARO, the decision in each stage is a function of the information accumulated from the previous periods on the values of the uncertain parameters. This information, however, is often...

The aim of this paper is to develop a hedging methodology for making a portfolio of options delta, vega and gamma neutral by taking positions in other available options, and simultaneously minimizing the net premium to be paid for the hedging. A quadratic programming solution for the problem is...

Abstract This article presents a novel combination of robust optimization developed in mathematical programming, and robust parameter design developed in statistical quality control. Robust parameter design uses metamodels estimated from experiments with both controllable and environmental...