Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically...

Missing instances and mixed data types, including discrete and ordered (e.g., continuous and ordinal) variables, are widespread in many datasets in the finance sector. In this domain, estimating missing instances is crucial because many data analysis pipelines require complete data, which is...

-- Analysis of Factors Affecting Goodwill Impairment and Research on Models Assisted by Computer Vision -- The Impact of Financial Technology Innovation on New Quality Productive Forces -- Empirical Study on Digital Finance Empowering the High Quality Development of Urban Ecological Economy in...

Traditional electrical grid as the largest and most complex industry in the world is currently under fundamental development. According to the IEEE Std1547.4, large distribution systems can be clustered into a number of micro-grids (MGs) to facilitate the control and operation infrastructure in...

Traditional electrical grid as the largest and most complex industry in the world is currently under fundamental development. According to the IEEE Std1547.4, large distribution systems can be clustered into a number of micro-grids (MGs) to facilitate the control and operation infrastructure in...

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings...