Learning about risk: Machine learning for risk assessment

Comparing Machine Learning Approaches for Fall Risk Assessment

Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods

The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer's Disease...

Risk assessment of atmospheric emissions using machine learning

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud chara...

Risk analysis of urban flood in Bandar Abbas using Machine Learning model and Analytic Hierarchy Process

Extended abstract 1- Introduction Floods are one of the natural events that cause human casualties and damage to buildings, facilities, gardens, fields, and natural resources every year. Urbanization disturbs the balance of slopes through indirect intrusion within watersheds, kills vegetation, soil compaction, and changes in the profile of waterways, increases the severity of floods, and incr...

Credit Risk Assessment using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications

Risk assessment of financial intermediaries is an area of renewed interest due to the financial crises of the 1980’s and 90’s. An accurate estimation of risk, and its use in corporate or global financial risk models, could be translated into a more efficient use of resources. One important ingredient to accomplish this goal is to find accurate predictors of individual risk in the credit portfol...