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Yawn Detection Using Support Vector Machine Classification
A model to detect yawning in a video sequence from a regular camera is proposed. Landmark detectors are able to quickly and accurately estimate the pose of a face with various amounts of light and facial expressions. We ...
A Novel Continuum Manipulator
(2015-09-21)
We address the problem of controlling continuum manipulators and evaluate Reinforcement Learning to produce a control policy for a robotic platform. Our approach discretizes the state and action spaces to reduce the training ...
Influence of Vehicle Make on Accuracy of Real-time Road Anomaly Identification
As road infrastructure in the United States is aging, road anomalies such as cracks, potholes, and other abnormalities are becoming much more prevalent. Currently there is no real-time understanding of the conditions of ...
Real-Time Classification of Road Conditions
(2015-09-03)
Common navigation algorithms like A* or D* Lite rely on costs to determine an optimal path. Costs may incorporate distance, time, or energy consumption; however, they can include anything that affects travel along a path. ...
Building a Better Machine Learning Hardware Accelerator with HARP
As machine learning is applied to ever more ambitious tasks, higher performance is required to be able to train and evaluate neural nets in reasonable amounts of time. To this end, many hardware accelerators for machine ...
Rescuing Progression in Antibiotic Discovery by Increasing Machine Learning Compatibility of High-Throughput Screening
Antibiotic discovery has stagnated. To avoid catastrophe, it must speed up. One of the most heavily used methods of drug discovery is high-throughput screening, yet in the 40 years of use of high-throughput screening, zero ...
Analyzing TnSeq Data to Predict Insertion Counts in M. tuberculosis
TnSeq is a genetic method used to evaluate the essentiality of genes in bacteria, such as Mycobacterium tuberculosis. It uses random insertions by the Himar1 transposon and high throughput sequencing to determine the most ...
Unsupervised Clustering: A Mixture of Experts Framework to Represent Flamelet Tables
A novel unsupervised learning-based clustering approach to represent the flamelet tables is developed. The typical tabulation method for flamelet-based modeling generally requires a large amount of storage. A well-developed ...
Detecting COVID-19 Outbreak with Anomalous Term Frequency
Previously many studies have aimed at predicting the trend of a disease through time series forecasting using machine learning methods. However, data extracted from the real world is often noisy, which can pose numerous ...
Machine Learning Time-to-Event Mortality Prediction in MIMIC-IV Critical Care Database
The rise in publicly available healthcare databases, such as MIMIC and the eICU, now make it possible to revolutionize medical care when paired with modern machine learning techniques. The MIMIC-IV critical care database ...