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"Information Theoretic Deductions Using Machine Learning with an Applic" by Arunselvan Ramaswamy, Yunpeng Ma et al.

Conditional entropy is an important concept that naturally arises in fields such as finance, sociology, and intelligent decision making when solving problems involving statistical inferences. Formally speaking, given two random variables X and Y, one is interested in the amount and direction of information flow between X and Y . It helps to draw conclusions about Y while only observing X. Conditional entropy H(Y |X) quantifies the amount of information flow from X to Y . In practice, calculating H(Y |X) exactly is infeasible. Current estimation methods are complex and suffer from estimation bias issues. In this paper, we present a simple Machine Learning based estimation method. Our method can be used to estimate H(Y |X) for discrete X and bi-valued Y. Given X and Y observations, we first construct a natural binary classification training dataset. We then train a supervised learning algorithm on this dataset, and use its prediction accuracy to estimate H(Y |X). We also present a simple ....

Karlstad University , Machine Learning , Automated Data Mining , Binary Classification , Onditional Entropy , Information Theory , Achine Learning In Sociology , Upervised Machine Learning ,

AI-Powered Alloy Design Revolutionizes the Fight Against Corrosion

In a world where annual corrosion economic losses exceed 2.5 trillion US dollars, the search for corrosion-resistant alloys and protective coatings continues to persist. Artificial intelligence (AI) is becoming increasingly important in the design of new alloys. ....

Michael Rohwerder , Kasturi Narasimha Sasidhar , Megan Craig , Study Co , Powered Alloy Design Revolutionizes , Fight Against Corrosion , Narasimha Sasidhar , Study Lead Author , Former Postdoctoral Researcher , Study Co Author , Materials Industry Focus , Automated Data Mining ,