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Full-Text Articles in Digital Humanities
Predicting Attitudes Toward The Environment Artificial Intelligence For The Humanities, Emily Rachfal
Predicting Attitudes Toward The Environment Artificial Intelligence For The Humanities, Emily Rachfal
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
No abstract provided.
Picmoji - English Translator, Alexander Beatty
Picmoji - English Translator, Alexander Beatty
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
This project lays the groundwork for studying how sentiment changes as information is selectively converted from (a) text to imagery and then from (b) image/text to interpretation. The first process is guided by Artificial Intelligence embedded within Natural Language Processing while the second processes a product of human intelligence and interpretation.
Computational Approaches To Predicting Cryptocurrency Prices, Chris Pelletier
Computational Approaches To Predicting Cryptocurrency Prices, Chris Pelletier
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
No abstract provided.
Rnn Monophonic Sheet Music Generation With Lilypond, Seth Colbert-Pollack
Rnn Monophonic Sheet Music Generation With Lilypond, Seth Colbert-Pollack
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
Computers are well suited to tasks such as data categorization and labeling. More challenging is data “generation”, a problem in which recurrent neural networks (RNNs) and more specifically long short-term memory networks (LSTMs) have made significant progress in the past few years. In this project, I train an RNN on a database of classical sheet music, and use it to generate new sheet music.
Natural Language Processing (Nlp) Of Liberal Arts College Newspapers In Ohio Over 30 Years, Shane Canfield
Natural Language Processing (Nlp) Of Liberal Arts College Newspapers In Ohio Over 30 Years, Shane Canfield
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
Computers have been extremely useful in humanity’s quest for knowledge, performing calculations and other strenuous tasks in seconds. For a computer to perform the tasks, it requires a specific set of instructions, or code, to tell it what to do. These series of commands and instructions are strict, in that any syntactic error results in faulty, or zero functionality. Human language is very much unlike that of a computer, in that it can be grammatically incorrect, irregular, or even incomplete, yet another human may still get the point and understand the information being exchanged. A significant part about what makes …
Transitional Justice Terminology Analysis In United Nations General Assembly Speeches (1971-2015), Michael Lahanas
Transitional Justice Terminology Analysis In United Nations General Assembly Speeches (1971-2015), Michael Lahanas
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
No abstract provided.
Leed Certification Prediction With K-Means Clustering Algorithm, Jack Chase
Leed Certification Prediction With K-Means Clustering Algorithm, Jack Chase
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
This project uses a K-Means Clustering algorithm. K-Means Clustering is a method of vector quantization, originally from signal processing that is popular for cluster analysis in data mining. K-Means Clustering aims to partition n observations into kclusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The example above has three clusters, and my project used four clusters, one for each LEED Certification. I
Deep Reinforcement Learning In Trading Algorithms, Tucker Bennett, Delaney Ambrosen, Joe Woody, Simon Fruth
Deep Reinforcement Learning In Trading Algorithms, Tucker Bennett, Delaney Ambrosen, Joe Woody, Simon Fruth
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
An algorithm that can learn an optimal policy to execute trade profitable is any market participant’s dream. In the project, we propose an algorithm that does just that: a Deep Reinforcement Learning trading algorithm. We design our algorithm by tuning the reward function to our specified constraints, taking into account unrealized Profits and Losses (PnL), Sharpe ratio, profits, and transaction costs. Additionally, we use a short 5-month moving average replay memory in order to ensure our algorithm is basing its decision on the most pertinent information. We combine the aforementioned concepts to make a theoretical Deep Reinforcement Learning trading algorithm.
Cold War Conflicts: Analyzing The Role Of U.S. Arms Exports, Kara Morrison
Cold War Conflicts: Analyzing The Role Of U.S. Arms Exports, Kara Morrison
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
No abstract provided.
Artistic Style Transfer: How Convolutional Breaks From Convention, Miles Shebar
Artistic Style Transfer: How Convolutional Breaks From Convention, Miles Shebar
IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
No abstract provided.