Rose–Hulman Institute of Technology
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A Machine Learning Based Approach for the Identification of Fake Bills
Full text linkFake or counterfeiting currency, which has been around as long as money has existed, is a major economic problem. Since the US dollar is the most popular form of currency globally, it is the most popular currency to counterfeit. The United States Department of Treasury estimates that between 200 million in fake bills are in circulation. The Federal Reserve Bank uses special banknote processing systems to count each bill deposited by the bank and examine them for the possibility of counterfeits. These machines have sensors designed to detect general quality of the bills, including paper type, quality of ink, and color-shifting ink. In this paper, several machine learning algorithms were used to develop an automated identification system for the detection of fake bills. A fake bills dataset, which contains 1500 bill measurements, was used to train several machine learning models. The dataset is split into training and testing sets. The machine learning models are trained with the training set and the accuracy of the models was evaluated with the test set using a 5-fold cross-validation to provide a more reliable measure of the model’s effectiveness. Our initial results are very promising with an accuracy rate of 99% for the best machine learning model. Furthermore, the machine learning model also identifies which bill measurements are critical for the identification of the bill authenticity. These results can provide useful information to the consumers as well as experts to spot fake bills based on bills measurement
Concept Maps Afford Connections from Mathematics and Physics to Electrical Engineering Courses
No full textBuilding Empathy through Classroom and Community Integration in a Multidisciplinary Engineering Design Program
No full textBuilding Empathy Through Classroom and Community Integration in a Multidisciplinary Engineering Design Program
No full textBoard 127: Work in Progress: Strategizing the Integration of VR and AR in STEM Education: Aligning Educational, Organizational, and Technological Strategies
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Optimizing Buying Strategies in Dominion
Full text linkDominion is a deck-building card game that simulates competing lords growing their kingdoms. Here we wish to optimize a strategy called Big Money by modeling the game as a Markov chain and utilizing the associated transition matrices to simulate the game. We provide additional analysis of a variation on this strategy known as Big Money Terminal Draw. Our results show that player\u27s should prioritize buying provinces over improving their deck. Furthermore, we derive heuristics to guide a player\u27s decision making for a Big Money Terminal Draw Deck. In particular, we show that buying a second Smithy is always more optimal than a Silver after turn six, and a gold after turn eight
