Casey A. Cole

Aaron Hein, Casey Cole, Homayoun Valafar

Machine learning and the use of neural networks has increased precipitously over the past few years primarily due to the ever-increasing accessibility to data and the growth of computation power. It has become increasingly easy to harness the power of machine learning for predictive tasks. Protein structure prediction is one area where neural networks are becoming increasingly popular and successful. Although very powerful, the use of ANN require selection of most appropriate input/output encoding, architecture, and class to produce the optimal results. In this investigation we have explored and evaluated the effect of several conventional and newly proposed input encodings and selected an optimal architecture. We considered 11 variations of input encoding, 11 alternative window sizes, and 7 different architectures. In total, we evaluated 2,541 permutations in application to the training and testing of more than 10,000 protein structures over the course of 3 months. Our investigations concluded that one-hot encoding, the use of LSTMs, and window sizes of 9, 11, and 15 produce the optimal outcome. Through this optimization, we were able to improve the quality of protein structure prediction by predicting the φ dihedrals to within 14° - 16° and ψ dihedrals to within 23°- 25°. This is a notable improvement compared to previously similar investigations.

Casey A. Cole, Bethany Janos, Dien Anshari et al.

Diseases resulting from prolonged smoking are the most common preventable causes of death in the world today. In this report we investigate the success of utilizing accelerometer sensors in smart watches to identify smoking gestures. Early identification of smoking gestures can help to initiate the appropriate intervention method and prevent relapses in smoking. Our experiments indicate 85%-95% success rates in identification of smoking gesture among other similar gestures using Artificial Neural Networks (ANNs). Our investigations concluded that information obtained from the x-dimension of accelerometers is the best means of identifying the smoking gesture, while y and z dimensions are helpful in eliminating other gestures such as: eating, drinking, and scratch of nose. We utilized sensor data from the Apple Watch during the training of the ANN. Using sensor data from another participant collected on Pebble Steel, we obtained a smoking identification accuracy of greater than 90% when using an ANN trained on data previously collected from the Apple Watch. Finally, we have demonstrated the possibility of using smart watches to perform continuous monitoring of daily activities.