This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning...

Yearly weather variability has an impact on agricultural supply and farmer welfare. More accurate weather predictions may increase the net returns for farmers but the indirect environmental impact of forecasts has not been examined. A stochastic bioeconomic model, which includes weather...

This work builds upon the findings of the joint research undertaken by the Eurasian Development Bank (EDB) and the Eurasian Economic Commission (EEC) to create a system capable of generating economic forecasts for EAEU member states, subject to any applicable country-specific social components....

This paper proposes a predictive maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for...

In this paper we address the issue of assessing and communicating the joint probabilities implied by density forecasts from multivariate time series models. We focus our attention in three areas. First, we investigate a new method of producing fan charts that better communicates the uncertainty...

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment....

This research examines the structural properties of the macroscopic model introduced in [AlShelahi and Saigal, 2018]. We present a theoretical analysis of the behavior of the macroscopic variables. In particular, we show that the model exhibits shock-like solutions, providing a new narrative for...

There are many methods for explaining why a machine learning model produces a given output in response to a given input. The relative merits of these methods are often debated using theoretical arguments and illustrative examples. This paper provides a large-scale empirical test of four widely...

Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of...