The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.

A primary need of Forensic science is to individualize missing persons that cannot be identified after death. With the use of advanced technology, Radio Frequency Identification (RFID) implant chips can drastically improve digital tracking and enable robust biological and legal identification. In this paper, I will discuss applications between different microchip technologies and indicate reasons why the RFID chip is more useful for forensic science. My results state that an RFID chip is significantly more capable of integrating a mass volume of background information, and can utilize implanted individuals’ DNA profiles to decrease the missing persons database backlogs. Since today’s society uses a lot of digital devices that can ultimately identify people by simple posts or geolocation, Forensic Science can harness that data as an advantage to help serve justice for the public in giving loved ones closure.