Rose–Hulman Institute of Technology
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Board 341: Obstacles in Context: A Multi-Perspective Examination of Obstacles of Revolutionizing Engineering Education in the NSF RED Program
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A Machine Learning Based Approach for the Identification of Fake Bills
Get PDFFake 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
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Counting Hamming-Graceful Labelings of Paths
Get PDFLet Γ be a graph of m edges and n vertices. A Hamming-graceful labeling of Γ labels vertices with binary strings of length m and the edge labels are induced by the Hamming distance between vertex labels. It is known that all paths have Hamming-graceful labelings, thus the question arises, how many possible labelings exist for a path of a given size. We develop an algebraic way to generate labelings, conjecture a method for counting, prove this for small examples, and verify larger examples using a Python program
