Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach

With recent advancements in the development of artificial intelligence applications using theories and algorithms in machine learning, many accurate models can be created to train and predict on given datasets. With the realization of the importance of imaging interpretation in cancer diagnosis, this article aims to investigate the theory behind Deep Learning and Bayesian Network prediction models. Based on the advantages and drawbacks of each model, different approaches will be used to construct a Bayesian Deep Learning Model, combining the strengths while minimizing the weaknesses. Finally, the applications and accuracy of the resulting Bayesian Deep Learning approach in the health industry in classifying images will be analyzed

Assessing the Option Value of Retrofitting a 200MW Power Plant to Oxyfuel CO2 Capture

AbstractAn advantage of oxyfuel capture technology is the flexibility of capable of retrofitting existing conventional coal-fired power plants. This analysis investigates the option value of retrofitting a 200MW coal-fired power plant to Oxyfuel CO2 capture power plant. The initial retrofit option value is the theoretical financial value for pre- investment (Oxyfuel CO2 Capture Ready) to keep the oxyfuel CO2 capture retrofit option open. The study assumes carbon price (either carbon tax or carbon allowance market) is the only driver for oxyfuel CO2 capture retrofit decision and there are no other operational or investment options in the decision making process